Keywords m

Machined Surfaces [Provide a short definition]
Section: 2-D Feature Analysis, Extraction and Representations, Shape, Skeletons, Texture (H)
* Statistical-Methods to Compare the Texture Features of Machined Surfaces

Magnetic Resonance [Provide a short definition]
Section: Functional Magnetic Resonance, fMRI (H3)
Section: Magnetic Resonance Imaging Systems, Hardware Implementations (H2)
Section: Magnetic Resonance Imaging, Registration, Alignment (H2)
Section: Medical Applications -- Magnetic Resonance Imaging, MRI (H1)
Section: Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models (H)
Section: MRI, Enhancement, Noise and Artifact Reduction (H2)
Section: MRI, Surveys, Overviews, Evaluations (H2)
Section: Segmentation, Features, Models from Magnetic Resonance Data, MRI (H2)
8 for Magnetic Resonance

Magnetic [Provide a short definition]
Section: Cartography, Aerial Images, Remote Sensing, Buildings, Roads, Terrain, ATR (H)
Section: Magnetic, Electromagnetic Detection for Buried Objects, UXO, Landmines (H4)

Magnification [Provide a short definition]
Section: Image Manipulation -- Expansion, Zoom, Magnification (H3)
Section: Motion Analysis --Low-Level, Image Level Analysis, Mosaic Generation, Super Resolution, Shape from Motion (H)

Mahalanobis Distance [Provide a short definition]
Section: Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants (H)
Section: Registration, Matching and Recognition Using Points, Lines, Regions, Areas, Surfaces (H)
* On the Generalized Distance in Statistics
* Practical Reliable Bayesian Recognition of 2D and 3D Objects Using Implicit Polynomials and Algebraic Invariants

Mammograms [Provide a short definition]
Section: Breast Cancer, Mammograms, Analysis, Mammography (H2)
Section: Mammograms, Image Enhancement, Noise Suppression (H3)
Section: Mammograms, Three Dimensional Analysis, Registration (H3)
Section: Mammograms, Ultrasound (H3)
Section: Mammography, Texture Based Techniques, Wavelets (H3)
Section: Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models (H)

Manipulator Path Planning [Provide a short definition]
Section: Active Vision, Camera Calibration, Mobile Robots, Navigation, Road Following (H)
Section: Planning Robot (Manipulator) Positions (H

Map Recognition [Provide a short definition]
Section: Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants (H)
* Fuzzy Pyramid Scheme for Distorted Object Recognition

Maps [Provide a short definition]
Section: Analysis of Maps (H3)
Section: OCR, Document Analysis and Character Recognition Systems (H)

MAPS/SPAM [Provide a short definition]
Section: Cartography, Aerial Images, Remote Sensing, Buildings, Roads, Terrain, ATR (H)
Section: CMU MAPS Image Database System (H1)

Markov Chain [Provide a short definition]
Section: Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants (H)
* Markov Chain Monte Carlo in Practice

Markov Model [Provide a short definition]
Section: 2-D Feature Analysis, Extraction and Representations, Shape, Skeletons, Texture (H)
* Rotation and gray scale transform invariant texture identification using wavelet decomposition and hidden Markov model

Markov Random Field [Provide a short definition]
Section: 2-D Feature Analysis, Extraction and Representations, Shape, Skeletons, Texture (H)
Section: 2-D Region Segmentation Techniques, Snakes, Active Contours (H)
Section: Markov Random Field Models (H2)
Section: Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models (H)
Section: MRF Models for Segmentation (H2)
* Markov Random-Field Models for Unsupervised Segmentation of Textured Color Images
* Prostate Cancer Segmentation With Simultaneous Estimation of Markov Random Field Parameters and Class
7 for Markov Random Field

Marr Prize [Provide a short definition]
Section: 3-D Object Description and Computation Techniques, Surfaces, Deformable, View Generation, Video Conferencing (H)
* Discriminative Models for Multi-Class Object Layout
* Population Shape Regression from Random Design Data

Marr Prize, HM Section: 3-D Shape from X -- Shading, Textures, Lasers, Structured Light, Focus, Line Drawings (H)
Section: Active Vision, Camera Calibration, Mobile Robots, Navigation, Road Following (H)
Section: Computational Vision, Regularization, Connectionist, Morphology, Scale-Space, Perceptual Grouping, Wavelets, Color, Sensors, Optical, Laser, Radar (H)
Section: Registration, Matching and Recognition Using Points, Lines, Regions, Areas, Surfaces (H)
* BRDF Acquisition with Basis Illumination
* Deformable Template As Active Basis
* Globally Optimal Affine and Metric Upgrades in Stratified Autocalibration
* Looking Around the Corner Using Transient Imaging
8 for Marr Prize, HM

Massive Parallel [Provide a short definition]
Section: Array Processors, Massive Parallel Systems, Pyramids (H2)
Section: Implementations and Applications, Databases, QBIC, Video Analysis, Hardware and Software, Inspection (H)

MAT Definition:* Medial Axis Transform.
Section: 2-D Feature Analysis, Extraction and Representations, Shape, Skeletons, Texture (H)
Section: Medial Axis Transform, MAT, Skeletons in Three Dimensions (H2)
Section: Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms (H)
Section: Processing of Skeletons for Descriptions (H2)
Section: Skeletons and Axial Descriptions - Medial Axis Transform (MAT) etc. (H
* Shock Grammar for Recognition, A
* TID: A Translation Invariant Data Structure for Storing Images
8 for MAT

MAT, Three Dimensional * 3D Medial Surfaces and 3D Skeletons
Section: 3-D Object Description and Computation Techniques, Surfaces, Deformable, View Generation, Video Conferencing (H)
Section: Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models (H)
* Central Axis Algorithm for 3D Bronchial Tree Structures, A

Match Measure [Provide a short definition]
Section: 2-D Feature Analysis, Extraction and Representations, Shape, Skeletons, Texture (H)
Section: General Similarity Measures for Database Indexing (H3)
Section: Image Registration -- The Match Technique, Match Measures (H3)
Section: Implementations and Applications, Databases, QBIC, Video Analysis, Hardware and Software, Inspection (H)
Section: Registration, Matching and Recognition Using Points, Lines, Regions, Areas, Surfaces (H)
Section: Similarity Measure, Distance Transforms and Functions for Objects and Shapes (H3)

Matched Filter [Provide a short definition]
Section: Registration, Matching and Recognition Using Points, Lines, Regions, Areas, Surfaces (H)
* Translation-Invariant Optical-Pattern Recognition without Correlation

Matching Pursuits [Provide a short definition]
Section: Image Processing, Restoration, Enhancement, Filters, Image and Video Coding (H)
Section: Matching Pursuits, Video Coding (H2)

Matching, 3-D General Section: General References for Matching (H1)
Section: Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants (H)

Matching, 3-D Section: 3-D Object Recognition from Pose Estimation or Alignment (H2)
Section: Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants (H)
Section: Matching, Volumes, 3-D (H1)
* Example of 3-D Interpretation of Images Using Bayesian Networks, An

Matching, Accumulation Section: 2-D/2-D Lines Accumuation Techniques (H3)
Section: 2-D/3-D Lines Accumuation Techniques (H3)
Section: 3-D/3-D Matching Accumulation Techniques (H3)
Section: Clustering and Accumulation Array Techniques (H3)
Section: Grimson Object Recognition Papers (H3)
Section: Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants (H)
Section: Matching Using Accumulation and Alignment Schemes (H2)
Section: Pose Estimation, 3D Models (H3)
Section: Range Data Matching -- Accumulation Methods (H3)
Section: Region/Contour Matching, Accumulation Based (H3)
Section: Registration, Matching and Recognition Using Points, Lines, Regions, Areas, Surfaces (H)
11 for Matching, Accumulation

Matching, Alignment Section: Face Recognition, Detection, Tracking, Gesture Recognition, Fingerprints, Biometrics (H)
Section: Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants (H)
* Alignment of Objects with Smooth Surfaces, The
* Online Fingerprint Verification

Matching, Areas Section: Optical Flow -- Matching Using Areas (H2)
Section: Optical Flow Field Computations and Use (H)

Matching, Aspect Graphs Section: 3-D Object Description and Computation Techniques, Surfaces, Deformable, View Generation, Video Conferencing (H)
Section: Aspect Graph Matching -- Bowyer (H3)
Section: Aspect Graph Matching -- Ikeuchi (H3)
Section: Aspect Graph Matching, Characteristic Views (H2)
Section: Aspect Graphs, Matching Systems, Object Recognition (H3)
Section: Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants (H)
* Qualitative 3-D Shape Reconstruction Using Distributed Aspect Graph Matching
7 for Matching, Aspect Graphs

Matching, Boltzmann Section: Boltzmann Machine, Simulated Annealing, and Related Topics (H3)
Section: Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants (H)

Matching, Boundaries Section: Stereo Analysis - Boundaries of Curved Surfaces (H2)
Section: Stereo: Three Dimensional Descriptions from Two or More Views, Binocular, Trinocular (H)

Matching, Chamfer Section: Cartography, Aerial Images, Remote Sensing, Buildings, Roads, Terrain, ATR (H)
Section: Registration, Matching and Recognition Using Points, Lines, Regions, Areas, Surfaces (H)
* Chamfering: A fast method for obtaining approximations of the Euclidean distance in N dimensions
* Parametric Correspondence and Chamfer Matching: Two New Techniques for Image Matching
* Research in Interactive Scene Analysis
* Scene-Analysis Approach to Remote Sensing, A

Matching, Clustering Section: Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms (H)
Section: Pattern Recognition, General Issues (H2)

Matching, Combinations Section: Combined Feature Matching (H1)
Section: Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants (H)

Matching, Constraints Section: Constraint Based Matching (H2)
Section: Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants (H)

Matching, Context Section: Context from the environment (H3)
Section: Context in Computer Vision (H2)
Section: Context Supplied by Text or Language (H3)
Section: Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants (H)

Matching, Contours Section: 2-D Contour Matching, Indexing or Hashing Techniques (H3)
Section: 2-D Region or Contour Matching (H2)
Section: Computational Vision, Regularization, Connectionist, Morphology, Scale-Space, Perceptual Grouping, Wavelets, Color, Sensors, Optical, Laser, Radar (H)
Section: Contours Through a Sequence (H3)
Section: Hierarchical/Scale-Space Contour Matching and Descriptions (H3)
Section: Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants (H)
Section: Matching Fourier Descriptors, Fourier Shape Descriptors (H3)
Section: Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models (H)
Section: Partial Contour Matching, Piecewise Segments (H3)
Section: Piecewise Segment Matching of Contours (H2)
Section: Registration, Matching and Recognition Using Points, Lines, Regions, Areas, Surfaces (H)
* Model Based Vision System to Identify and Locate Partially Visible Industrial Parts, A
* On Recognizing and Positioning Curved 3-D Objects from Image Contours
* Shape Matching Using Relaxation Techniques
* Three Dimensional Movement Analysis of Dynamic Line Images
* Wavelet-Based Affine Invariant Representation: A Tool for Recognizing Planar Objects in 3D Space
16 for Matching, Contours

Matching, Correlation Section: Correlation Based and Signal Matching Techniques (H2)
Section: Registration, Matching and Recognition Using Points, Lines, Regions, Areas, Surfaces (H)

Matching, Edges Section: 2-D Points with 2-D Structures, Point Matching, Features (H2)
Section: Edge Based Stereo Analysis: Scan Line Oriented (H2)
Section: Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants (H)
Section: Registration, Matching and Recognition Using Points, Lines, Regions, Areas, Surfaces (H)
Section: Stereo: Three Dimensional Descriptions from Two or More Views, Binocular, Trinocular (H)
* Depth from Edge and Intensity Based Stereo
* Fast Stereovision Matcher Based on Prediction and Recursive Verification of Hypothesis, A
* Segment-Based Stereo Matching
* Stereo by Two-Level Dynamic Programming
* Two-View Matching
10 for Matching, Edges

Matching, Eigen Values Section: Invariants -- Eigen Representations, General Appearance Based Methods (H2)
Section: Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants (H)
* Eigen Decomposition Approach to Weighted Graph Matching Problems, An

Matching, Evaluation Section: Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants (H)
Section: Registration, Matching and Recognition Using Points, Lines, Regions, Areas, Surfaces (H)
Section: Stereo: Three Dimensional Descriptions from Two or More Views, Binocular, Trinocular (H)
* Benchmark Evaluation of a Model-Based Object Recognition System
* Cost of Choosing the Wrong Model in Object Recognition by Constrained Search, The
* Performance Comparison of Scene Matching Techniques
* Performance Evaluation of Shape Matching via Chord Length Distribution
* Recovering 3D Information from Complex Aerial Imagery
8 for Matching, Evaluation

Matching, Features Section: Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants (H)
Section: Other Feature Matching Techniques (H1)

Matching, Fourier Section: Matching Fourier Descriptors, Fourier Shape Descriptors (H3)
Section: Registration, Matching and Recognition Using Points, Lines, Regions, Areas, Surfaces (H)

Matching, Function Section: Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants (H)
Section: Recognition by Function (H2)

Matching, Graphs Section: Basic Comparison of Relational Network Descriptions (H2)
Section: Continuous Relaxation Theory, Constraint Satisfaction (H3)
Section: General Structure and Graph Representation and Matching (H2)
Section: Graph Matching and Relaxation (H1)
Section: Graph Matching Theoretical Issues (H2)
Section: Graph Matching, Continuous Relaxation, Constraint Satisfaction (H2)
Section: Graph Matching, Neural Networks, Hopfield Networks (H2)
Section: Hummel and Zucker Relaxation Papers (H3)
Section: Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants (H)
Section: Matching Graphs and 3-D Network Descriptions (H2)
Section: Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities (H)
Section: Shmuel Peleg Theoretical Relaxation Papers (H3)
* Framework for Estimation of Motion Parameters from Range Images, A
* Some Techniques for Recognizing Structures in Pictures
14 for Matching, Graphs

Matching, Hashing Section: 2-D Contour Matching, Indexing or Hashing Techniques (H3)
Section: Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants (H)
Section: Registration, Matching and Recognition Using Points, Lines, Regions, Areas, Surfaces (H)
Section: Three-Dimensional Matching Using Hashing/Indexing (H2)
* Geometric Hashing: A General and Efficient Model-Based Recognition Scheme
* On the Sensitivity of Geometric Hashing

Matching, Hierarchical Section: Hierarchical/Scale-Space Contour Matching and Descriptions (H3)
Section: Registration, Matching and Recognition Using Points, Lines, Regions, Areas, Surfaces (H)

Matching, Hopfield Networks Section: Graph Matching, Neural Networks, Hopfield Networks (H2)
Section: Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants (H)

Matching, Hough Section: Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants (H)
Section: Matching Using Accumulation and Alignment Schemes (H2)

Matching, Images Section: Image Registration Techniques (H2)
Section: Registration, Matching and Recognition Using Points, Lines, Regions, Areas, Surfaces (H)

Matching, Indexing * 2-D Images of 3-D Oriented Points
Section: Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants (H)
Section: Registration, Matching and Recognition Using Points, Lines, Regions, Areas, Surfaces (H)
Section: Three-Dimensional Matching Using Hashing/Indexing (H2)
* Efficient Model Library Access by Projectively Invariant Indexing Functions
* Generalized Shape Autocorrelation
* Graycode Representation and Indexing: Efficient Two Dimensional Object Recognition
* Model-Group Indexing for Recognition
8 for Matching, Indexing

Matching, Invariants Section: 3-D Object Recognition Using Invariants (H2)
Section: Invariance Papers -- Mundy (H3)
Section: Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants (H)
Section: Principal Component Decompositions, Point features (H2)
Section: Region or Contour Invariants, Signatures, Metrics for Matching (H3)
Section: Registration, Matching and Recognition Using Points, Lines, Regions, Areas, Surfaces (H)
Section: Three-Dimensional Matching Using Hashing/Indexing (H2)
* Recognizing 3D Objects Using Photometric Invariant
* Shape Recognition under Affine Distortions
9 for Matching, Invariants

Matching, Lines Section: 2-D Lines with 2-D Structure (H2)
Section: 2-D Lines with 3-D Structure (H2)
Section: 2-D/2-D Lines Accumuation Techniques (H3)
Section: 2-D/3-D Lines Accumuation Techniques (H3)
Section: 3-D Lines with 3-D Structure (H2)
Section: 3-D/3-D Matching Accumulation Techniques (H3)
Section: Invariants, Lines, Curves (H3)
Section: Line Based Matching for Pose Estimation (H2)
Section: Line Segment Based Stereo Analysis (H2)
Section: Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants (H)
Section: Matching Linear Features (H1)
Section: Partial Contour Matching, Piecewise Segments (H3)
Section: Piecewise Segment Matching of Contours (H2)
Section: Registration, Matching and Recognition Using Points, Lines, Regions, Areas, Surfaces (H)
Section: Stereo: Three Dimensional Descriptions from Two or More Views, Binocular, Trinocular (H)
* Locating Structures in Aerial Images
* Matching Linear Features of Images and Maps
* Matching of Natural Terrain Scenes
18 for Matching, Lines

Matching, Models Section: 2-D Lines with 3-D Structure (H2)
Section: 3-D Lines with 3-D Structure (H2)
Section: 3-D Object Recognition from Pose Estimation or Alignment (H2)
Section: ACRONYM and SUCCESSOR Papers - Stanford University and Others (H2)
Section: Constraint Based Matching (H2)
Section: General Issues -- Knowledge-Based Vision (H2)
Section: Grimson Object Recognition Papers (H3)
Section: Knowledge-Based Vision (H1)
Section: Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants (H)
Section: Matching Graphs and 3-D Network Descriptions (H2)
Section: Model Based Recognition Systems (H2)
Section: Recognition by Function (H2)
Section: Registration, Matching and Recognition Using Points, Lines, Regions, Areas, Surfaces (H)
Section: University of Massachusetts VISIONS System (H2)
* Model-Based Recognition in Robot Vision
15 for Matching, Models

Matching, Moments Section: Matching, Descriptions Using Moments (H3)
Section: Registration, Matching and Recognition Using Points, Lines, Regions, Areas, Surfaces (H)
* Aircraft Identification by Moment Invariants

Matching, Networks Section: Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants (H)
Section: Matching Graphs and 3-D Network Descriptions (H2)

Matching, Optical Flow Section: Optical Flow Field Computation and Analysis (H1)
Section: Optical Flow Field Computations and Use (H)

Matching, Points Section: 2-D Points with 2-D Structures, Point Matching, Features (H2)
Section: 2-D Points with 3-D Structures (H2)
Section: 3-D Points with 3-D Structures (H2)
Section: Clustering and Accumulation Array Techniques (H3)
Section: Invariants, Points (H3)
Section: Long Sequences, Motion Matching (H3)
Section: Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants (H)
Section: Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities (H)
Section: Optical Flow Field Computations and Use (H)
Section: Point Based Pose Estimation and Recognition (H3)
Section: Point Matching for Optical Flow Computation (H2)
Section: Point Matching (H1)
Section: Point Pattern Invariants (H3)
Section: Registration, Matching and Recognition Using Points, Lines, Regions, Areas, Surfaces (H)
Section: Relaxation Based Techniques (H3)
Section: Stereo Analysis: Point Matching, Low Level Feature Matching (H2)
Section: Stereo: Three Dimensional Descriptions from Two or More Views, Binocular, Trinocular (H)
* Automatic Feature Point Extraction and Tracking in Image Sequences for Unknown Camera Motion
* Disparity Analysis of Images
19 for Matching, Points

Matching, Pose Section: 3-D Object Recognition from Pose Estimation or Alignment (H2)
Section: Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants (H)
Section: Pose Estimation, 3D Models (H3)

Matching, Range Data Section: Pose Estimation -- Range Data (H3)
Section: Range Data Matching -- Accumulation Methods (H3)
Section: Registration, Matching and Recognition Using Points, Lines, Regions, Areas, Surfaces (H)
Section: Surfaces and Range Data Matching (H2)

Matching, Recognition Section: Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants (H)
Section: Object Recognition, General Techniques (H1)

Matching, Regions Section: 2-D Feature Analysis, Extraction and Representations, Shape, Skeletons, Texture (H)
Section: 2-D Region or Contour Matching (H2)
Section: Affine Invariants (H3)
Section: Invariants -- Eigen Representations, General Appearance Based Methods (H2)
Section: Invariants, Areas (H3)
Section: Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants (H)
Section: Matching, Affine Transformations (H3)
Section: Matching, Areas, Regions, Surfaces (H1)
Section: Region or Contour Invariants, Signatures, Metrics for Matching (H3)
Section: Region/Contour Matching, Accumulation Based (H3)
Section: Registration, Matching and Recognition Using Points, Lines, Regions, Areas, Surfaces (H)
* Fuzzy Relaxation Approach for Inexact Scene Matching
* Matching of Featured Objects Using Relational Tables from Stereo Images
* Robust matching of 3D contours using iterative closest point algorithm improved by M-estimation
* Semantic Description of Aerial Images Using Stochastic Labeling
* Symbolic Image Registration and Change Detection
* Two-View Matching
17 for Matching, Regions

Matching, Relaxation Section: Continuous Relaxation Theory, Constraint Satisfaction (H3)
Section: Discrete Relaxation Methods (H2)
Section: Discrete Relaxation Theoretical Issues (H3)
Section: Faugeras and Berthod Gradient Optimization Methods (H3)
Section: Graph Matching, Continuous Relaxation, Constraint Satisfaction (H2)
Section: Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants (H)
Section: Registration, Matching and Recognition Using Points, Lines, Regions, Areas, Surfaces (H)
Section: Relaxation Based Techniques (H3)
8 for Matching, Relaxation

Matching, Scale-Space Section: Hierarchical/Scale-Space Contour Matching and Descriptions (H3)
Section: Registration, Matching and Recognition Using Points, Lines, Regions, Areas, Surfaces (H)

Matching, Sequence Section: Contours Through a Sequence (H3)
Section: Long Sequence Matching and Motion (H2)
Section: Long Sequences, Motion Matching (H3)
Section: Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities (H)
Section: Registration, Matching and Recognition Using Points, Lines, Regions, Areas, Surfaces (H)
* Using Motion from Orthographic Projections to Prune 3-D Point Matches

Matching, Signature Section: Region or Contour Invariants, Signatures, Metrics for Matching (H3)
Section: Registration, Matching and Recognition Using Points, Lines, Regions, Areas, Surfaces (H)

Matching, Stereo Section: Stereo Analysis: Point Matching, Low Level Feature Matching (H2)
Section: Stereo: Three Dimensional Descriptions from Two or More Views, Binocular, Trinocular (H)

Matching, Structures Section: Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants (H)
Section: Structural Matching for Computer Vision (H2)

Matching, Surfaces Section: 3-D Object Description and Computation Techniques, Surfaces, Deformable, View Generation, Video Conferencing (H)
Section: 3-D Surface Registration, Range Data Registration for Mosaics and Models (H4)
Section: Cartography, Aerial Images, Remote Sensing, Buildings, Roads, Terrain, ATR (H)
Section: Image to 3-D Surface Matching, 2-D to 3-D Matching (H4)
Section: Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models (H)
Section: Register 3-D LIDAR Data, Profiles (H4)
Section: Register 3-D Surfaces, Mesh Models (H4)
Section: Registration, Matching and Recognition Using Points, Lines, Regions, Areas, Surfaces (H)
Section: Surface Matching, Deformable Surface Matching (H2)
Section: Surface Registration, Matching, Patch Based, Planar Patches, Planes (H4)
Section: Surface Registration, Sonar, Ultrasound, Acoustic (H4)
Section: Surfaces and Range Data Matching (H2)
* Automatic 3D to 2D Registration for the Photorealistic Rendering of Urban Scenes
* Constraint-Based Sensor Planning for Scene Modeling
* Navigation Using Image Sequence Analysis and 3-D Terrain Matching
* Simultaneous Nonrigid Registration of Multiple Point Sets and Atlas Construction
16 for Matching, Surfaces

Matching, Survey Section: Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants (H)
* Relational Matching
* Some Computational Strategies for Object Recognition

Matching, Templates Section: Deformable Template Matching (H3)
Section: Registration, Matching and Recognition Using Points, Lines, Regions, Areas, Surfaces (H)
Section: Template Matching Techniques (H2)

Matching, Textures Section: 2-D Feature Analysis, Extraction and Representations, Shape, Skeletons, Texture (H)
* Object Detection Based on Gray Level Cooccurrence

Matching, Theory Section: Evidence Theory, Combination Techniques, Optimization Techniques (H3)
Section: Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants (H)

Matching, Tree Search * 3D-POLY: A Robot Vision System for Recognizing Objects in Occluded Environments
Section: Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants (H)
Section: Matching Using Tree Searching Techniques (H2)
Section: Registration, Matching and Recognition Using Points, Lines, Regions, Areas, Surfaces (H)
* Relational Descriptions in Picture Processing

Matching, Video Section: Registration, Matching and Recognition Using Points, Lines, Regions, Areas, Surfaces (H)
Section: Video Copy, Video Duplicate Detection (H4)
Section: Video Registration Techniques, Synchronizing, Synchronization (H3)

Matching, Volumes Section: 3-D Object Recognition Using Invariants (H2)
Section: Aspect Graph Matching -- Bowyer (H3)
Section: Aspect Graph Matching -- Ikeuchi (H3)
Section: Aspect Graph Matching, Characteristic Views (H2)
Section: Aspect Graphs, Matching Systems, Object Recognition (H3)
Section: Grimson Object Recognition Papers (H3)
Section: Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants (H)
Section: Matching Graphs and 3-D Network Descriptions (H2)
Section: Matching Using Accumulation and Alignment Schemes (H2)
Section: Matching, Volumes, 3-D (H1)
Section: Pose Estimation, 3D Models (H3)
Section: Three-Dimensional Matching Using Hashing/Indexing (H2)
12 for Matching, Volumes

Mathematical Morphology [Provide a short definition]
Section: Computational Vision, Regularization, Connectionist, Morphology, Scale-Space, Perceptual Grouping, Wavelets, Color, Sensors, Optical, Laser, Radar (H)
* Periodic Lines: Definition, Cascades, and Application to Granulometries

Matrix Factorization [Provide a short definition]
Section: Matrix Factorization, General Issues (H3)
Section: Motion Analysis --Low-Level, Image Level Analysis, Mosaic Generation, Super Resolution, Shape from Motion (H)

Matting [Provide a short definition]
Section: 3-D Object Description and Computation Techniques, Surfaces, Deformable, View Generation, Video Conferencing (H)
Section: Image Matting, Video Matting (H3)

Maximum Likelihood [Provide a short definition]
Section: Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants (H)
Section: Maximum Likelihood Estimation, Classification (H3)

MCMC [Provide a short definition]
Section: Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants (H)
* Markov Chain Monte Carlo in Practice

MDL Definition:* Minimum Description Length.
Section: 2-D Feature Analysis, Extraction and Representations, Shape, Skeletons, Texture (H)
Section: 2-D Region Segmentation Techniques, Snakes, Active Contours (H)
Section: MDL, Minimum Description Length for Shape Measure (H4)
* Segmenting Images Corrupted By Correlated Noise

Mean-Shift Tracking [Provide a short definition]
Section: Mean-Shift Tracking Techniques (H3)
Section: Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities (H)

Mean Shift Definition:* A nonparametric density estimator for detecting the modes of a distribution on a Euclidean space.

Measurement [Provide a short definition]
Section: Automated Measurement Systems (H2)
Section: Books, Collections, Overviews, General, and Surveys (H)
Section: Implementations and Applications, Databases, QBIC, Video Analysis, Hardware and Software, Inspection (H)
* Reliability Analysis of Parameter Estimation in Linear Models with Applications to Mensuration Problems in Computer Vision

Measurment [Provide a short definition]
Section: Optical Flow Field Computations and Use (H)
Section: Visual Odometry, Distance Measurments from Vision, Motion (H3)

Medial Axis Transform [Provide a short definition]
Section: 2-D Feature Analysis, Extraction and Representations, Shape, Skeletons, Texture (H)
Section: 2-D Region Segmentation Techniques, Snakes, Active Contours (H)
Section: Distance Transforms, Functions and Skeletons (H2)
Section: Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models (H)
Section: Registration, Matching and Recognition Using Points, Lines, Regions, Areas, Surfaces (H)
Section: Skeletons and Axial Descriptions - Medial Axis Transform (MAT) etc. (H
* Medial Axis Transform-Based Features and a Neural-Network for Human-Chromosome Classification
* Multiscale Medial Axis and Its Applications in Image Registration, The
* Transform for Multiscale Image Segmentation by Integrated Edge and Region Detection, A
* Transformation for Extracting New Descriptions of Shape, A
10 for Medial Axis Transform

Medial Axis [Provide a short definition]
Section: 3-D Object Description and Computation Techniques, Surfaces, Deformable, View Generation, Video Conferencing (H)
Section: Generalized Cylinders, Medial Axis Descriptions (H1)

Median Computation [Provide a short definition]
Section: 2-D Feature Analysis, Extraction and Representations, Shape, Skeletons, Texture (H)
Section: Median Filtering (H2)

Median Filters Definition:* Replace the center image element in the window with the median of the image values in the window.
Section: 2-D Feature Analysis, Extraction and Representations, Shape, Skeletons, Texture (H)
Section: Median Filtering (H2)

Medical [Provide a short definition]
Section: Fusion of Medical Data (H3)
Section: Registration, Matching and Recognition Using Points, Lines, Regions, Areas, Surfaces (H)

Medical, Applications Section: Breast Cancer, Mammograms, Analysis, Mammography (H2)
Section: General Medical Applications (H1)
Section: Lungs, and Lung Cancer Image Analysis (H2)
Section: Mammograms, Image Enhancement, Noise Suppression (H3)
Section: Mammograms, Three Dimensional Analysis, Registration (H3)
Section: Mammograms, Ultrasound (H3)
Section: Mammography, Texture Based Techniques, Wavelets (H3)
Section: Medical Applications -- Cancer Diagnosis and Analysis (H1)
Section: Medical Applications -- Endoscopy, Colonscopy (H1)
Section: Medical Applications -- General Systems (H1)
Section: Medical Applications -- Surgery (H1)
Section: Medical Applications -- Surveys (H1)
Section: Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models (H)
Section: Motion Analysis --Low-Level, Image Level Analysis, Mosaic Generation, Super Resolution, Shape from Motion (H)
Section: Pulmonary Nodules, Lung Nodules (H3)
Section: Registration, Matching and Recognition Using Points, Lines, Regions, Areas, Surfaces (H)
Section: Retinal Images, Analysis of Eye, etc. (H1)
Section: Retinal Images, Angiography, Blood Vessels in the Eye (H2)
Section: Ribs, Chest X-Rays (H3)
* Automatic MR-PET Registration Algorithm
* Robust Hierarchical Algorithm for Constructing a Mosaic from Images of the Curved Human Retina
21 for Medical, Applications

Mellin Transform [Provide a short definition]
Section: Image Processing, Restoration, Enhancement, Filters, Image and Video Coding (H)
Section: Transforms, Radon, Haar, Hadamard, etc. (H2)

Mensuration [Provide a short definition]
Section: Automated Measurement Systems (H2)
Section: Cartography, Aerial Images, Remote Sensing, Buildings, Roads, Terrain, ATR (H)
Section: Implementations and Applications, Databases, QBIC, Video Analysis, Hardware and Software, Inspection (H)
* Close Range Photogrammetry: Principles, Techniques and Applications

Mesh Coding [Provide a short definition]
Section: 3-D Object Description and Computation Techniques, Surfaces, Deformable, View Generation, Video Conferencing (H)
Section: Mesh Compression Techniques (H4)

Mesh Models [Provide a short definition]
Section: 3-D Object Description and Computation Techniques, Surfaces, Deformable, View Generation, Video Conferencing (H)
Section: 3-D Shape from X -- Shading, Textures, Lasers, Structured Light, Focus, Line Drawings (H)
Section: Feature Extraction and Processing on Mesh Representation (H4)
Section: Hierarchical Mesh Representations, Multi-Resolution Mesh Algorithms (H4)
Section: Mesh Compression Techniques (H4)
Section: Mesh Representations, Remeshing Algorithms (H4)
Section: Triangulated Surface Models, Mesh Models, Mesh Descriptions, 3-D Meshes (H3)
* Implicit Meshes for Effective Silhouette Handling
8 for Mesh Models

Mesh, 2-D Section: 2-D Feature Analysis, Extraction and Representations, Shape, Skeletons, Texture (H)
Section: Voronoi Diagrams, Delaunay Triangulation, 2-D Meshes (H2)

Metrology [Provide a short definition]
Section: Automated Measurement Systems (H2)
Section: Implementations and Applications, Databases, QBIC, Video Analysis, Hardware and Software, Inspection (H)

Microarray Data [Provide a short definition]
Section: Cell, DNA, Genome, Chromosome Analysis and Extraction, Microarray (H1)
Section: Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models (H)

Microcalcification [Provide a short definition]
Section: Breast Cancer, Mammograms, Analysis, Mammography (H2)
Section: Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models (H)

Microscope [Provide a short definition]
Section: Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models (H)
Section: Medical Applications, Microscope Image Analysis, Electron Microscope (H2)

Microwave [Provide a short definition]
Section: Computational Vision, Regularization, Connectionist, Morphology, Scale-Space, Perceptual Grouping, Wavelets, Color, Sensors, Optical, Laser, Radar (H)
Section: Microwave Sensors and Analysis (H2)

Minimal Spanning Tree [Provide a short definition]
Section: 2-D Region Segmentation Techniques, Snakes, Active Contours (H)
Section: Decision Trees (H2)
Section: Edge Detection and Analysis, Lines, Segments, Curves, Corners, Hough Transform (H)
Section: Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms (H)
* Curvilinear Feature Extraction Using Minimum Spanning Trees
* Data-driven Procedure for Density-Estimation with Some Applications, A
* Minimal Spanning Tree-Based Clustering Technique: Relationship with Bayes Classifier
* Parallel Algorithm For Constructing Minimum Spanning Trees, A
8 for Minimal Spanning Tree

Minimum Description Length [Provide a short definition]
Section: 2-D Feature Analysis, Extraction and Representations, Shape, Skeletons, Texture (H)
Section: 2-D Region Segmentation Techniques, Snakes, Active Contours (H)
Section: MDL, Minimum Description Length for Shape Measure (H4)
* Region Competition and its Analysis: A Unified Theory for Image Segmentation

Minutiae [Provide a short definition]
Section: Face Recognition, Detection, Tracking, Gesture Recognition, Fingerprints, Biometrics (H)
Section: Fingerprint Features, Minutiae, Ridges (H2)
Section: Fingerprint Features, Ridges, Flow, Orientation Based (H3)

Mixed Pixels [Provide a short definition]
Section: Mixed Pixels, Subpixel Classification (H3)
Section: Mixed Pixels, Unmixing (H3)
Section: Mixture Models, Mixed Pixels (H2)
Section: Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms (H)

Mixture Models [Provide a short definition]
Section: Mixture Models, Mixed Pixels (H2)
Section: Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms (H)

MMSE Definition:* Minimum Mean-Squared Error.

Mobile Robots [Provide a short definition]
Section: Active Vision, Camera Calibration, Mobile Robots, Navigation, Road Following (H)
Section: Autonomous Vehicles (H1)
* Vision for Mobile Robot Navigation: A Survey

Mode Selection [Provide a short definition]
Section: AVC/H.264 Mode Selection, Mode Decision (H4)
Section: Image Processing, Restoration, Enhancement, Filters, Image and Video Coding (H)

Model-Based Descriptions [Provide a short definition]
Section: Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants (H)
Section: Parameterized Models, Fit Model to Objects (H3)

Model-Based Vision [Provide a short definition]
Section: Face Recognition and Using Models (H3)
Section: Face Recognition Using Three-Dimensional Models, 3-D Models (H3)
Section: Face Recognition, Detection, Tracking, Gesture Recognition, Fingerprints, Biometrics (H)
Section: Three-Dimensional Model Generation for Recognition, 3-D Models (H4)

Model Acquisition [Provide a short definition]
Section: Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants (H)
* Learning Object Models from Appearance

Model Based Recognition [Provide a short definition]
Section: 2-D Region Segmentation Techniques, Snakes, Active Contours (H)
Section: 3-D Object Description and Computation Techniques, Surfaces, Deformable, View Generation, Video Conferencing (H)
Section: 3-D Object Recognition Using Invariants (H2)
Section: ACRONYM and SUCCESSOR Papers - Stanford University and Others (H2)
Section: ACRONYM and SUCCESSOR Papers - Stanford University and Others (H2)
Section: Aspect Graph Matching, Characteristic Views (H2)
Section: ATR -- Model, Object Based Radar and SAR Recognition (H3)
Section: Cartography, Aerial Images, Remote Sensing, Buildings, Roads, Terrain, ATR (H)
Section: Constraint Based Matching (H2)
Section: Context in Computer Vision (H2)
Section: Knowledge-Based Vision (H1)
Section: Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants (H)
Section: Model Based Recognition Systems (H2)
Section: Recognition by Function (H2)
Section: Three-Dimensional Matching Using Hashing/Indexing (H2)
Section: University of Massachusetts VISIONS System (H2)
Section: University of Massachusetts VISIONS System (H2)
* Constructing Constraint Tables for Model-Based Recognition and Localization
* Region-Oriented Image-Analysis System by Computer, A
19 for Model Based Recognition

Model Based Segmentation [Provide a short definition]
Section: 2-D Region Segmentation Techniques, Snakes, Active Contours (H)
Section: Fua and Leclerc Guided Segmentation Papers (H2)
Section: Techniques for Model Guided Segmentation, Context in Segmentation (H1)

Model Based Vision [Provide a short definition]
Section: Learning Model Descriptions (H3)
Section: Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants (H)
Section: Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms (H)
* Model-Based Recognition in Robot Vision
* Survey of Model-Based Image Analysis Systems

Model Based [Provide a short definition]
Section: Books, Collections, Overviews, General, and Surveys (H)
Section: Expert Vision Systems (H3)
Section: Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities (H)
* Model-Based Image Analysis of Human Motion Using Constraint Propagation

Moire [Provide a short definition]
Section: Face Recognition, Detection, Tracking, Gesture Recognition, Fingerprints, Biometrics (H)
* Real-time Optically Processed Face Recognition System Based on Arbitrary Moire Contours

Moment Invariants [Provide a short definition]
Section: Matching, Descriptions Using Moments (H3)
Section: Registration, Matching and Recognition Using Points, Lines, Regions, Areas, Surfaces (H)

Moment Matching [Provide a short definition]
Section: Matching, Descriptions Using Moments (H3)
Section: Registration, Matching and Recognition Using Points, Lines, Regions, Areas, Surfaces (H)

Moments [Provide a short definition]
Section: 2-D Feature Analysis, Extraction and Representations, Shape, Skeletons, Texture (H)
Section: Face Recognition, Detection, Tracking, Gesture Recognition, Fingerprints, Biometrics (H)
Section: Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants (H)
Section: Matching, Descriptions Using Moments (H3)
Section: Moment Computation, Computation of Moments (H4)
Section: OCR, Document Analysis and Character Recognition Systems (H)
Section: Registration, Matching and Recognition Using Points, Lines, Regions, Areas, Surfaces (H)
* Extending The Feature Vector For Automatic Face Recognition
* Method of Normalization to Determine Invariants, The
* Object Recognition by Three Dimensional Moment Invariants
* Optical Character Recognition by the Method of Moments
* Orientation of 3-D Structures in Medical Images
* Orthogonal Moment Features for Use with Parametric and Nonparametric Classifiers
* Review of Algorithms for Shape Analysis, A
* Shape Analysis of Three Dimensional Objects Using Range Information
15 for Moments

Moments, Computation Section: Moment Computation, Computation of Moments (H4)
Section: Registration, Matching and Recognition Using Points, Lines, Regions, Areas, Surfaces (H)

Moments, Three-Dimensional * 3-D Moment Forms: Their Construction and Application to Object Identification and Positioning
Section: 3-D Object Description and Computation Techniques, Surfaces, Deformable, View Generation, Video Conferencing (H)

Montage [Provide a short definition]
Section: Image Montage, Mosaic Generation, Super Resolution and Stabilization (H1)
Section: Mosaic Generation for Video (H3)
Section: Mosaic Generation, Image Stitching, Photomosaic (H2)
Section: Motion Analysis --Low-Level, Image Level Analysis, Mosaic Generation, Super Resolution, Shape from Motion (H)

Morphing [Provide a short definition]
Section: 3-D Object Description and Computation Techniques, Surfaces, Deformable, View Generation, Video Conferencing (H)
Section: Virtual View Generation, View Synthesis, Image Based Rendering, IBR, Morphing (H2)

Morphology [Provide a short definition]
Section: 2-D Feature Analysis, Extraction and Representations, Shape, Skeletons, Texture (H)
Section: 2-D Region Segmentation Techniques, Snakes, Active Contours (H)
Section: Computational Vision, Regularization, Connectionist, Morphology, Scale-Space, Perceptual Grouping, Wavelets, Color, Sensors, Optical, Laser, Radar (H)
Section: Implementations and Applications, Databases, QBIC, Video Analysis, Hardware and Software, Inspection (H)
Section: Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models (H)
Section: Morphology - General, Surveys (H2)
Section: Morphology - Techniques and Applications (H2)
Section: Morphology - Theory (H2)
Section: Morphology (H1)
* Automatic generation of image-segmentation processes
* Automatic Screening of Cytological Specimens
* Geodesic Saliency of Watershed Contours and Hierarchical Segmentation
* Morpholog: A Software Package for the Quantitative Image Analysis
* Spectral and Rank Order Approaches to Texture Analysis
14 for Morphology

Morphology, Implementations Section: Computational Vision, Regularization, Connectionist, Morphology, Scale-Space, Perceptual Grouping, Wavelets, Color, Sensors, Optical, Laser, Radar (H)
Section: Morphological Operator Decomposition (H3)

MOSAIC System [Provide a short definition]
Section: 3-D Shape from X -- Shading, Textures, Lasers, Structured Light, Focus, Line Drawings (H)
Section: MOSAIC System -- Herman (H2)

Mosaic [Provide a short definition]
Section: 3-D Object Description and Computation Techniques, Surfaces, Deformable, View Generation, Video Conferencing (H)
Section: Aerial Image Mosaic Generation, UAV Mosaics (H3)
Section: Document Mosaic Generation (H3)
Section: Image Montage, Mosaic Generation, Super Resolution and Stabilization (H1)
Section: Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models (H)
Section: Mosaic Generation for Video (H3)
Section: Mosaic Generation, Image Stitching, Photomosaic (H2)
Section: Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities (H)
Section: Motion Analysis --Low-Level, Image Level Analysis, Mosaic Generation, Super Resolution, Shape from Motion (H)
Section: Panoramic Image, Panorama Creation (H3)
Section: Registration, Matching and Recognition Using Points, Lines, Regions, Areas, Surfaces (H)
Section: Retinal Mosaic Generation (H3)
* Building mosaics from video using MPEG motion vectors
* Detecting and Tracking Moving Objects in Video from an Airborne Observer
* Feature-Based, Robust, Hierarchical Algorithm for Registering Pairs of Images of the Curved Human Retina, A
* Independent Motion: The Importance of History
* Iso-Shaping Rigid Bodies for Estimating Their Motion From Image Sequences
* Robust Parameter Estimation in Computer Vision
18 for Mosaic

Mosaicking [Provide a short definition]
Section: Motion Analysis --Low-Level, Image Level Analysis, Mosaic Generation, Super Resolution, Shape from Motion (H)
* Electronic Digital Stabilization: Design and Evaluation, with Applications

Motion Analysis [Provide a short definition]
Section: Implementations and Applications, Databases, QBIC, Video Analysis, Hardware and Software, Inspection (H)
Section: Video Database Indexing, Motion Analysis (H4)

Motion and Depth [Provide a short definition]
Section: Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities (H)
Section: Motion Analysis --Low-Level, Image Level Analysis, Mosaic Generation, Super Resolution, Shape from Motion (H)
Section: Motion Using Depth Information (H2)
Section: Motion Using Stereo Pairs or Depth, Multiple Cameras -- Features (H1)
Section: Motion with Optical Flow and Depth (H2)

Motion and Stereo [Provide a short definition]
Section: Active Vision, Camera Calibration, Mobile Robots, Navigation, Road Following (H)
Section: Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities (H)
Section: Motion Analysis --Low-Level, Image Level Analysis, Mosaic Generation, Super Resolution, Shape from Motion (H)
Section: Motion Using Stereo Pairs or Depth, Multiple Cameras -- Features (H1)
Section: Motion Using Stereo Pairs or Depth (H1)
* Integrated Stereo-Based Approach to Automatic Vehicle Guidance, An

Motion Capture [Provide a short definition]
Section: Human Motion Capture, Dance Activities (H4)
Section: Human Motion Capture, Joint Information, Special Activities (H4)
Section: Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities (H)

Motion Compensation [Provide a short definition]
Section: Computation for General Motion Compensation, Motion Estimation (H4)
Section: Global Motion Compensation (H4)
Section: Image Processing, Restoration, Enhancement, Filters, Image and Video Coding (H)
Section: Motion Analysis --Low-Level, Image Level Analysis, Mosaic Generation, Super Resolution, Shape from Motion (H)
Section: Motion Compensation for Coding (H4)
* New Merit Version for MPEG-2 Encoded Files, A
* Two-Stage Motion Compensation Using Adaptive Global MC and Local Affine MC
7 for Motion Compensation

Motion Detection [Provide a short definition]
Section: Motion Analysis --Low-Level, Image Level Analysis, Mosaic Generation, Super Resolution, Shape from Motion (H)
Section: Motion Detection, Analysis of Motion Detectors (H2)

Motion Prediction [Provide a short definition]
Section: Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities (H)
Section: Target Tracking Techniques, Motion Model, Prediction, Control (H3)
Section: Target Tracking Techniques, Multiple Trackers, Multiple Models, Fusion (H4)

Motion Segmentation [Provide a short definition]
Section: 2-D Region Segmentation Techniques, Snakes, Active Contours (H)
Section: Active Contours and Snakes, Video, Motion Segmentation Issues (H4)
Section: Background Detection, Background Model (H3)
Section: Computational Vision, Regularization, Connectionist, Morphology, Scale-Space, Perceptual Grouping, Wavelets, Color, Sensors, Optical, Laser, Radar (H)
Section: Extract Moving Objects from Sequences or Video (H2)
Section: Hough, Votion, Accumulation Methods for Moving Object Extraction (H3)
Section: Image Segmentation from Motion Information (H2)
Section: Motion Analysis --Low-Level, Image Level Analysis, Mosaic Generation, Super Resolution, Shape from Motion (H)
Section: Motion Detection, Analysis of Motion Detectors (H2)
Section: Motion Segmentation, Object Extraction in Compressed Domains (H3)
Section: Motion Segmentation, Object Extraction, Evaluation, Survey (H2)
Section: Moving Object Extraction with Moving Cameras (H3)
Section: Moving Object Extraction, Using Models or Analysis of Regions (H3)
* Attention-from-motion: A factorization approach for detecting attention objects in motion
* Expectation-Maximisation Framework for Segmentation and Grouping, An
15 for Motion Segmentation

Motion Tracking [Provide a short definition]
Section: Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities (H)
Section: Target Tracking Systems, Real-Time Issues (H3)

Motion [Provide a short definition]
Section: Arm Tracking, Arm Pose for Gestures (H4)
Section: Face Recognition, Detection, Tracking, Gesture Recognition, Fingerprints, Biometrics (H)
Section: Hand Tracking for Gestures (H3)

Motion, Coding Section: Computation for Vector Fields, Flow Fields (H4)
Section: Computational Vision, Regularization, Connectionist, Morphology, Scale-Space, Perceptual Grouping, Wavelets, Color, Sensors, Optical, Laser, Radar (H)
Section: Image Processing, Restoration, Enhancement, Filters, Image and Video Coding (H)
Section: Moving Image Coding, Compression: Using Vector Fields, Flow Fields (H4)
Section: Wavelets for Image Coding, Compression -- Quantization Issues (H4)
Section: Wavelets for Image Coding (H3)
Section: Wavelets for Motion and Video Coding (H3)
* Motion-Compensated Television Coding: Some new Results
8 for Motion, Coding

Motion, Compression Section: Image Processing, Restoration, Enhancement, Filters, Image and Video Coding (H)
Section: Moving Image Coding, Compression: Using Vector Fields, Flow Fields (H4)

Motion, Detection Section: 2-D Region Segmentation Techniques, Snakes, Active Contours (H)
Section: Background Detection, Background Model (H3)
Section: Background Models, Textured Surfaces, Regions (H4)
Section: Consecutive Image Differencing Techniques (H2)
Section: Differencing Papers -- Ramesh Jain (H3)
Section: Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities (H)
Section: Motion Analysis --Low-Level, Image Level Analysis, Mosaic Generation, Super Resolution, Shape from Motion (H)
Section: Motion Detection, Analysis of Motion Detectors (H2)
Section: Motion Sequence, Background Subtraction (H4)
Section: Moving Object Extraction, Using Models or Analysis of Regions (H3)
Section: Shadows and Motion, Detection and Extraction (H3)
Section: Surveillance Applications, Motion Detection (H1)
* Unified Approach to Moving Object Detection in 2D and 3D Scenes, A
13 for Motion, Detection

Motion, Differencing Section: Consecutive Image Differencing Techniques (H2)
Section: Differencing Papers -- Ramesh Jain (H3)
Section: Image Differencing, Motion Segmentation and Filtering Techniques (H1)
Section: Motion Analysis --Low-Level, Image Level Analysis, Mosaic Generation, Super Resolution, Shape from Motion (H)
* Formation of an Object Concept by Analysis of Systematic Time Variations in the Optically Perceptible Environment

Motion, Discontinuity Section: Image Segmentation from Motion Information (H2)
Section: Motion Analysis --Low-Level, Image Level Analysis, Mosaic Generation, Super Resolution, Shape from Motion (H)
Section: Motion Edges -- Detection and Analysis (H1)
Section: Optical Flow Field -- Boundaries (H2)
Section: Optical Flow Field -- Multiple Flows, Transparent Layers, Motion Layers (H2)
Section: Optical Flow Field Computations and Use (H)
* Simple Scheme for Motion Boundary Detection, A
7 for Motion, Discontinuity

Motion, Edges Section: Motion Analysis --Low-Level, Image Level Analysis, Mosaic Generation, Super Resolution, Shape from Motion (H)
Section: Motion Edges -- Detection and Analysis (H1)

Motion, Epipolar plane Section: Epipolar-Plane Analysis in Spatio-Temporal Analysis (H2)
Section: Motion Analysis --Low-Level, Image Level Analysis, Mosaic Generation, Super Resolution, Shape from Motion (H)

Motion, Estimation Equations Section: Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities (H)
Section: Motion Equation Estimation Methods (H1)

Motion, Estimation Evaluation Section: Error Analysis of Motion and Structure Computations (H2)
Section: Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities (H)
Section: Motion Analysis --Low-Level, Image Level Analysis, Mosaic Generation, Super Resolution, Shape from Motion (H)
* Error Analysis of Motion Parameter Estimation from Image Sequences
* Performance Bounds for Estimating Three-Dimensional Motion Parameters from a Sequence of Noisy Images
* Recovering 3-D Motion Parameters from Image Sequences with Gross Errors
* Robust Algorithms for Motion Estimation Based on Two Sequential Stereo Image Pairs
* Some Properties of the E Matrix in Two-View Motion Estimation
8 for Motion, Estimation Evaluation

Motion, Estimation * 3-D Kalman Filter for Image Motion Estimation
Section: Image Processing, Restoration, Enhancement, Filters, Image and Video Coding (H)

Motion, Factorization Section: Factorization Approach to Motion and Structure (H2)
Section: Motion Analysis --Low-Level, Image Level Analysis, Mosaic Generation, Super Resolution, Shape from Motion (H)
* Shape and Motion from Image Streams: A Factorization Method

Motion, Feature Based Section: General Feature Based Motion (H1)
Section: Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities (H)

Motion, Five Frames Section: Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities (H)
Section: Motion Estimates Using 5 or More Frames (H2)
Section: Shariat and Related Papers (H3)

Motion, FOE Section: Focus of Expansion and Other Features (H2)
Section: Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities (H)
Section: Optical Flow Field Computations and Use (H)
* Determining Motion Parameters for Scenes with Translation and Rotation
* Estimating 3-D Egomotion from Perspective Image Sequences

Motion, Four Frames Section: Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities (H)
Section: Motion Estimates Using 4 Frames (H2)

Motion, Hardware Section: 3-D Shape from X -- Shading, Textures, Lasers, Structured Light, Focus, Line Drawings (H)
* Depth And Motion Analysis Machine, The

Motion, Human Section: Crowds, Tracking Multiple People, Multiple Pedestrian Tracking (H4)
Section: Human Motion, General Analysis (H3)
Section: Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants (H)
Section: Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities (H)
Section: Motion Analysis --Low-Level, Image Level Analysis, Mosaic Generation, Super Resolution, Shape from Motion (H)
Section: Tracking Animals, Animal Gait (H4)
Section: Tracking People with 3D Models, Articulation Models (H4)
Section: Tracking People with Multiple Cameras, Stereo, or Depth (H4)
Section: Tracking People, Re-Identification Issues, Occlusions (H4)
* Advances in View-Invariant Human Motion Analysis: A Review
* Factorization-Based Approach for Articulated Nonrigid Shape, Motion and Kinematic Chain Recovery From Video, A
* Human and Object Tracking and Verification in Video
12 for Motion, Human

Motion, Kalman filter Section: Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities (H)
* Estimation of Object Motion Parameters from Noisy Images

Motion, Lines Section: Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities (H)
* Estimation of Displacements from Two 3-D Frames Obtained from Stereo

Motion, Low Level Section: Low Level Motion, General Issues (H1)
Section: Motion Analysis --Low-Level, Image Level Analysis, Mosaic Generation, Super Resolution, Shape from Motion (H)

Motion, Many Frames Section: Broida and Related Work (H3)
Section: Integration over a Sequence (H2)
Section: Long Sequence Matching and Motion (H2)
Section: Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities (H)
Section: Motion Analysis --Low-Level, Image Level Analysis, Mosaic Generation, Super Resolution, Shape from Motion (H)
Section: Motion Estimates Using 5 or More Frames (H2)
Section: Optical Flow Field Computations and Use (H)
Section: Spatio-Temporal Analysis -- Many Frames (H1)
* Robust Estimation of Multiple Motions: Affine and Piecewise-Smooth Flow-Fields, The
9 for Motion, Many Frames

Motion, Matching Section: Long Sequences, Motion Matching (H3)
Section: Registration, Matching and Recognition Using Points, Lines, Regions, Areas, Surfaces (H)

Motion, Multiple Frames Section: Motion Analysis --Low-Level, Image Level Analysis, Mosaic Generation, Super Resolution, Shape from Motion (H)
* Euclidean Reconstruction: From Paraperspective to Perspective

Motion, Multiple Section: Image Segmentation from Motion Information (H2)
Section: Motion Analysis --Low-Level, Image Level Analysis, Mosaic Generation, Super Resolution, Shape from Motion (H)
Section: Optical Flow Field -- Boundaries (H2)
Section: Optical Flow Field -- Multiple Flows, Transparent Layers, Motion Layers (H2)
Section: Optical Flow Field Computations and Use (H)

Motion, Nonrigid Section: 3-D Object Description and Computation Techniques, Surfaces, Deformable, View Generation, Video Conferencing (H)
Section: Nonrigid, Non-Rigid, Deformable Motion Analysis and Tracking (H2)

Motion, Observer Section: Egomotion or Ego Motion Computation from Flow Fields (H2)
Section: More Direct Ego Motion Computation (H3)
Section: Optical Flow Field Computations and Use (H)

Motion, Optical Flow Section: Optical Flow Field Computation -- General Issues (H1)
Section: Optical Flow Field Computations and Use (H)

Motion, Parameters Section: Egomotion or Ego Motion Computation from Flow Fields (H2)
Section: More Direct Ego Motion Computation (H3)
Section: Optical Flow Field Computations and Use (H)

Motion, Planning Section: Active Vision - Path/Trajectory Planning (H2)
Section: Active Vision, Camera Calibration, Mobile Robots, Navigation, Road Following (H)
Section: Planning Robot (Manipulator) Positions (H
Section: Planning Sensor Position, View Selection, View Planning (H3)
Section: Planning Vehicle Position, Path Planning or Route Planning (H3)

Motion, Psychology Section: Motion Analysis --Low-Level, Image Level Analysis, Mosaic Generation, Super Resolution, Shape from Motion (H)
Section: Psychology and Psychophysics (H1)

Motion, Psychophysics Section: Motion Analysis --Low-Level, Image Level Analysis, Mosaic Generation, Super Resolution, Shape from Motion (H)
Section: Psychology and Psychophysics (H1)

Motion, Regions Section: Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities (H)
* Segmentation, Matching and Estimation of Structure and Motion of Textured Piecewise Planar Surfaces

Motion, Rigidity Constraint Section: Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities (H)
* Determining the Movement of Objects from a Sequence of Images
* Uniqueness and Estimation of 3-D Motion Parameters and Surface Structures of Rigid Objects

Motion, Rotation Section: Active Vision, Camera Calibration, Mobile Robots, Navigation, Road Following (H)
Section: Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities (H)
Section: Optical Flow Field Computations and Use (H)
Section: Rotation Only (H2)
Section: Special Case Motion Estimation (H1)
* Analyzing Orthographic Projection of Multiple 3-D Velocity Vector Fields in Optical Flow
* Self-Calibration from Multiple Views with a Rotating Camera
7 for Motion, Rotation

Motion, Segmentation Section: 2-D Region Segmentation Techniques, Snakes, Active Contours (H)
Section: Extract Moving Objects from Sequences or Video (H2)
Section: Image Segmentation from Motion Information (H2)
Section: Motion Analysis --Low-Level, Image Level Analysis, Mosaic Generation, Super Resolution, Shape from Motion (H)
Section: Motion Segmentation, Object Extraction in Compressed Domains (H3)
Section: Motion Segmentation, Object Extraction, Evaluation, Survey (H2)
Section: Optical Flow Field -- Boundaries (H2)
Section: Optical Flow Field -- Multiple Flows, Transparent Layers, Motion Layers (H2)
Section: Optical Flow Field Computations and Use (H)
Section: Shadows and Motion, Detection and Extraction (H3)
* Velocity Determination in Scenes Containing Several Moving Objects
11 for Motion, Segmentation

Motion, Smoothness Constraint Section: Optical Flow Field -- Smoothness (H1)
Section: Optical Flow Field Computations and Use (H)

Motion, Spatio-Temporal Section: General Spatio-Temporal Analysis (H2)
Section: Motion Analysis --Low-Level, Image Level Analysis, Mosaic Generation, Super Resolution, Shape from Motion (H)
Section: Spatio-Temporal Analysis -- Many Frames (H1)

Motion, Structure Evaluation Section: Error Analysis of Motion and Structure Computations (H2)
Section: Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities (H)
Section: Motion Analysis --Low-Level, Image Level Analysis, Mosaic Generation, Super Resolution, Shape from Motion (H)
* Accuracy of 3D Parameters in Correspondence-Based Techniques, The
* Perception of Structure from Motion: I: Optic Flow vs. Discrete Displacements; II: Lower Bound Results
* Refinement of Environmental Depth Maps over Multiple Frames

Motion, Structure Section: Illumination Variations in Structure, Depth, and Shape from Motion (H2)
Section: Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities (H)
Section: Motion Analysis --Low-Level, Image Level Analysis, Mosaic Generation, Super Resolution, Shape from Motion (H)
Section: Structure from Motion - Other (H1)
Section: Structure, Depth, and Shape from Motion (H1)
Section: Vehicle Based Structure, Depth, and Shape from Motion (H2)
* Acceleration-Based Structure-from-Motion
* Analysis of Long Image Sequence for Structure and Motion Estimation
* Empirical Study of Structure from Motion, The
* Epipolar-Plane Image Analysis: An Approach to Determining Structure from Motion
* Estimating Motion and Structure from Line Matches: Performance Obtained and Beyond
* Interpretation of Structure from Motion, The
* Kinematics of a Rigid Object from a Sequence of Noisy Images: A Batch Approach
* Maximizing Rigidity: The Incremental Recovery of 3-D Structure from Rigidity and Rubbery Motion
* Motion and Structure from Motion from Point and Line Matches
* Natural Representation of Motion in Space-Time
* Observing Jointed Objects
* On Combining Points and Lines in an Image Sequence to Recover 3D Structure and Motion
* On Kineopsis and Computation of Structure and Motion
* Polynomial Methods for Structure from Motion
* Representation and Tracking of Point Structures Using Stereovision
* Simple Procedure to Solve Motion and Structure from Three Orthographic Views, A
22 for Motion, Structure

Motion, Surface Reconstruction Section: Motion Analysis --Low-Level, Image Level Analysis, Mosaic Generation, Super Resolution, Shape from Motion (H)
Section: Optical Flow Field Computations and Use (H)
Section: Surface or Contour Motion Using Global Surfaces (H1)
Section: Surface Reconstruction from Optical Flow (H1)

Motion, Survey Section: Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities (H)
Section: Motion Analysis --Low-Level, Image Level Analysis, Mosaic Generation, Super Resolution, Shape from Motion (H)
Section: Optical Flow Field Computations and Use (H)
* Analysis of Visual Motion by Biological and Computer Systems
* Analysis Techniques for Image Sequences
* Computational Analysis of Time-Varying Images, A
* Computing Motion and Structure from Noisy, Time-Varying Image Velocity Information
* Derivation of Optical Flow Using a Spatiotemporal-Frequency Approach
* Estimation of Motion from a Pair of Range Images: A Review
* Motion and Structure from Feature Correspondences: A Review
* Motion Segmentation and Estimation by Constraint Line Filtering
* On the Computation of Motion from Sequences of Images: A Review
12 for Motion, Survey

Motion, Three Frames Section: Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities (H)
Section: Motion Analysis --Low-Level, Image Level Analysis, Mosaic Generation, Super Resolution, Shape from Motion (H)
Section: Motion Estimates Using 3 Frames (H2)
* Affine Structure from Line Correspondences with Uncalibrated Affine Cameras
* Computing Two Motions from Three Frames

Motion, Tracking Section: 2-D Region Segmentation Techniques, Snakes, Active Contours (H)
Section: Long Sequence Matching and Motion (H2)
Section: Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities (H)
Section: Motion Estimates Using 5 or More Frames (H2)
Section: Region, Object, Target Tracking (H2)
Section: Snakes, Contours, Motion Tracking (H2)
Section: Tracking of Moving Objects and Matching in Sequences (H1)
* Computer Analysis of Moving Polygonal Images
8 for Motion, Tracking

Motion, Translation Section: Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities (H)
Section: Optical Flow Field Computations and Use (H)
Section: Optical Flow for Simple Motions (H2)
Section: Special Case Motion Estimation (H1)
Section: Translation Only (H2)

Motion, Two Frames Section: Active Vision, Camera Calibration, Mobile Robots, Navigation, Road Following (H)
Section: Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities (H)
Section: Motion Estimates Using 2 Frames (H2)
Section: Univ. of Illinois Parameter Estimation Papers (H3)
* Relative Orientation

Motion, Walking Section: Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities (H)
* Motion Analysis of Human Ambulatory Patterns

Movement Ephenthesis Definition:* A movement segment that is added between the last segment of one sign and the first segment of the next sign in ASL and other languages.

Moving Light Displays [Provide a short definition]
Section: Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities (H)
* Lights: A Study in Motion
* Observing Jointed Objects
* Recognition of Moving Light Displays Using Hidden Markov-Models
* Tracking Three Dimensional Moving Light Displays

MPEG Standard [Provide a short definition]
Section: Image Processing, Restoration, Enhancement, Filters, Image and Video Coding (H)
* MPEG: A Video Compression Standard for Multimedia Applications

MPEG [Provide a short definition]
Section: Image Processing, Restoration, Enhancement, Filters, Image and Video Coding (H)
Section: MPEG 4 Issues (H3)
Section: MPEG 7 Issues (H3)
Section: MPEG and Related Standard Coding Methods (H2)
Section: MPEG Error Concealment, Artifacts Issues (H3)
Section: MPEG Hardware and Implementations (H3)
Section: MPEG Rate-Distortion Trade-Offs, Transmissions Issues (H3)
Section: MPEG Standard (H3)
8 for MPEG

MRF Optimization [Provide a short definition]
Section: Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants (H)
Section: MRF Optimization, Energy Minimization (H3)

MRF Definition:* Markov Random Field.
Section: 2-D Feature Analysis, Extraction and Representations, Shape, Skeletons, Texture (H)
Section: Markov Random Field Models (H2)

MRI [Provide a short definition]
Section: Brain, Cortex, MRI Analysis, Models, 3-D (H2)
Section: Brain, Cortex, MRI Segmentation (H3)
Section: Brain, Cortex, Registration, MRI, Other (H3)
Section: Heart, Cardiac, Angiography using MRI Analysis, Cardiac MRI (H2)
Section: Magnetic Resonance Imaging Systems, Hardware Implementations (H2)
Section: Magnetic Resonance Imaging, Registration, Alignment (H2)
Section: Medical Applications -- Magnetic Resonance Imaging, MRI (H1)
Section: Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models (H)
8 for MRI

Multi-Camera Tracking [Provide a short definition]
Section: Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities (H)
Section: Multi-Target Tracking with Multiple Sensors, Stereo, Depth, Range (H3)
Section: Target Tracking, Multiple Sensors, Multiple Cameras, Multi-Camera Tracking (H3)

Multi-Grid [Provide a short definition]
Section: 3-D Object Description and Computation Techniques, Surfaces, Deformable, View Generation, Video Conferencing (H)
Section: Reconstruction, Hierarchical, Multi-Grid Approaches (H2)

Multi-level Segmentation [Provide a short definition]
Section: 2-D Region Segmentation Techniques, Snakes, Active Contours (H)
Section: Multi-level, Multi-Scale Segmentation and Smoothing Methods (H2)

Multi-Object Tracking [Provide a short definition]
Section: Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities (H)
Section: Multi-Target Tracking with Multiple Sensors, Stereo, Depth, Range (H3)
Section: Target and Feature Tracking, Multi-Object, Multiple Objects, Multiple Target (H3)

Multi-Scale computations [Provide a short definition]
Section: Computational Vision, Regularization, Connectionist, Morphology, Scale-Space, Perceptual Grouping, Wavelets, Color, Sensors, Optical, Laser, Radar (H)
Section: Scale Space Computation of Features (H2)

Multi-Scale [Provide a short definition]
Section: 2-D Region Segmentation Techniques, Snakes, Active Contours (H)
Section: Computational Vision, Regularization, Connectionist, Morphology, Scale-Space, Perceptual Grouping, Wavelets, Color, Sensors, Optical, Laser, Radar (H)
Section: Multi-Scale, Pyramid Texture Segmentation Approaches (H2)
Section: Optical Flow Field Computations and Use (H)
Section: Scale Space and Multi-Scale Techniques (H1)
* Fast Scalable Algorithm for Discontinuous Optical-Flow Estimation, A

Multi-Target Tracking [Provide a short definition]
Section: Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities (H)
Section: Multi-Target Tracking with Multiple Sensors, Stereo, Depth, Range (H3)
Section: Target and Feature Tracking, Multi-Object, Multiple Objects, Multiple Target (H3)

Multibaseline Stereo [Provide a short definition]
Section: Multi-Baseline Stereo, Multibaseline Stereo (H2)
Section: Stereo: Three Dimensional Descriptions from Two or More Views, Binocular, Trinocular (H)

Multibiometrics [Provide a short definition]
Section: Biometrics, Multi-Modal Systems, Multibiometrics, Combined Face and Other Features, Fusion (H2)
Section: Face Recognition, Detection, Tracking, Gesture Recognition, Fingerprints, Biometrics (H)

Multicast [Provide a short definition]
Section: Image Processing, Restoration, Enhancement, Filters, Image and Video Coding (H)
Section: Wireless Tranmission, Multicast, Streaming (H4)

Multilevel Reconstruction [Provide a short definition]
Section: 3-D Object Description and Computation Techniques, Surfaces, Deformable, View Generation, Video Conferencing (H)
* Volumetric Segmentation Using Hierarchical Representation And Triangulated Surface

Multimedia [Provide a short definition]
Section: Image Processing, Restoration, Enhancement, Filters, Image and Video Coding (H)
Section: Implementations and Applications, Databases, QBIC, Video Analysis, Hardware and Software, Inspection (H)
Section: Multimedia Systems, Multimedia Indexing (H2)
Section: Multimedia, Audio-Visual Communications, Survey (H2)
Section: OCR, Document Analysis and Character Recognition Systems (H)
Section: Watermarks in Video and Multi-Media, Other Data (H2)
* Informationally Decentralized System Resource Management for Multiple Multimedia Tasks
7 for Multimedia

Multiple Description [Provide a short definition]
Section: Image Processing, Restoration, Enhancement, Filters, Image and Video Coding (H)
Section: Multiple Description Video Coding (H3)

Multiple Light Sources [Provide a short definition]
Section: 3-D Shape from X -- Shading, Textures, Lasers, Structured Light, Focus, Line Drawings (H)
Section: Shape from Multiple Light Sources, Photometric Stereo (H1)

Multiple Motions [Provide a short definition]
Section: Image Segmentation from Motion Information (H2)
Section: Motion Analysis --Low-Level, Image Level Analysis, Mosaic Generation, Super Resolution, Shape from Motion (H)
Section: Optical Flow Field -- Boundaries (H2)
Section: Optical Flow Field -- Multiple Flows, Transparent Layers, Motion Layers (H2)
Section: Optical Flow Field Computations and Use (H)
* Closing the Loop on Multiple Motions

Multiple Resolutions [Provide a short definition]
Section: 2-D Feature Analysis, Extraction and Representations, Shape, Skeletons, Texture (H)
Section: Computational Vision, Regularization, Connectionist, Morphology, Scale-Space, Perceptual Grouping, Wavelets, Color, Sensors, Optical, Laser, Radar (H)
Section: Edge Detection and Analysis, Lines, Segments, Curves, Corners, Hough Transform (H)
Section: Face Recognition, Detection, Tracking, Gesture Recognition, Fingerprints, Biometrics (H)
Section: Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants (H)
Section: Multiple Resolution Edge Detectors to Improve Performance, Hierarchical, Multi-Scale (H2)
Section: Optical Flow -- Hierarchical, Multi-Grid, Multi-Scale Approaches (H2)
Section: Optical Flow Field Computations and Use (H)
Section: Registration, Matching and Recognition Using Points, Lines, Regions, Areas, Surfaces (H)
Section: Stereo Systems: Multiple Resolutions, Hierarchical (H2)
Section: Stereo: Three Dimensional Descriptions from Two or More Views, Binocular, Trinocular (H)
* Edge Detection by Computer Using Planning
* Fast Algorithms for Estimating Local Image Properties
* Layered Recognition Cone Networks That Preprocess Classify and Describe
* Multiple Resolution Representation and Probabilistic Matching of 2-D Gray-Scale Shape
* Multiple Resolution Skeletons
* VISIONS: A computer System for Interpreting Scenes
* Visual Identification of People by Computer
18 for Multiple Resolutions

Multiple Sclerosis [Provide a short definition]
Section: Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models (H)
Section: Multiple Sclerosis Detection and Analysis (H2)

Multiple Sensors [Provide a short definition]
Section: Shape Computations from Multiple Sensors (H1)
Section: Stereo: Three Dimensional Descriptions from Two or More Views, Binocular, Trinocular (H)

Multiple Thresholds [Provide a short definition]
Section: 2-D Region Segmentation Techniques, Snakes, Active Contours (H)
Section: Segmentation by Thresholding, Quantization, or Relaxation (H2)

Multiple Views [Provide a short definition]
Section: 3-D Shape from X -- Shading, Textures, Lasers, Structured Light, Focus, Line Drawings (H)
Section: MOSAIC System -- Herman (H2)
Section: Three-Dimensional Reconstruction from Different Views (H1)

Multiple Views, Stereo Section: Multiple Cameras or Views (H1)
Section: Stereo: Three Dimensional Descriptions from Two or More Views, Binocular, Trinocular (H)

Multiplicative Noise [Provide a short definition]
Section: Image Processing, Restoration, Enhancement, Filters, Image and Video Coding (H)
Section: Multiplicative Noise Removal (H3)

Multiresolution [Provide a short definition]
Section: Image Processing, Restoration, Enhancement, Filters, Image and Video Coding (H)
Section: Multiresolution, Hierarchical Restoration Techniques (H3)

Multiscale Representation [Provide a short definition]
Section: Registration, Matching and Recognition Using Points, Lines, Regions, Areas, Surfaces (H)
* Towards Effective Planar Shape Representation with Multiscale Digital Curvature Analysis Based on Signal-processing Techniques

Multiscale [Provide a short definition]
Section: 2-D Feature Analysis, Extraction and Representations, Shape, Skeletons, Texture (H)
Section: Hierarchical, Multi-Scale Texture Representations and Analysis (H2)

Multispectral [Provide a short definition]
Section: Implementations and Applications, Databases, QBIC, Video Analysis, Hardware and Software, Inspection (H)
* Designing a Deer Detection System Using a Multistage Classification Approach

Multiview Geometry [Provide a short definition]
Section: Stereo: Three Dimensional Descriptions from Two or More Views, Binocular, Trinocular (H)
* Multiview Geometry: Profiles and Self-Calibration

Multiview Video Coding [Provide a short definition]
Section: Image Processing, Restoration, Enhancement, Filters, Image and Video Coding (H)
Section: Multiview Video Coding (H2)

Music [Provide a short definition]
Section: Face Recognition, Detection, Tracking, Gesture Recognition, Fingerprints, Biometrics (H)
Section: Music Related Gestures, Systems, Music Video Analysis (H3)
Section: Music (H2)
Section: New Unsorted Entries, and Other Miscellaneous Papers (H)

Musical Notation [Provide a short definition]
Section: Analysis of Music, Musical Notation, Music Scores (H3)
Section: OCR, Document Analysis and Character Recognition Systems (H)

Index for "n"