7 2-D Feature Analysis, Extraction and Representations, Shape, Skeletons, Texture

Keyword list at end

• 7.1 Computation of General Features
  • 7.1.1 Shape Decomposition
  • 7.1.2 Differential and Derivative Filters
  • 7.1.3 Median Filtering
    • 7.1.3.1 Order Statistics, Rank Statistics, Filters
  • 7.1.4 Smoothing Techniques, Adaptive Smoothing
    • 7.1.4.1 Implementation of Convolution and Smoothing Techniques
  • 7.1.5 Fourier Descriptors, DFT, FFT Computation, Use, Frequency Analysis
    • 7.1.5.1 DCT Computation
  • 7.1.6 Gabor Filters -- General
  • 7.1.7 Feature and Object Detection Systems
    • 7.1.7.1 Detection Transformer, DETR Applications
    • 7.1.7.2 Blob Detection
    • 7.1.7.3 Spot Detection, Bright Spots
    • 7.1.7.4 Low Light Object Detection
    • 7.1.7.5 Remote Sensing Object Detection Applications
    • 7.1.7.6 Camouflaged Object Detection, Camouflage
    • 7.1.7.7 Object Localization
    • 7.1.7.8 One-Shot Object Detection, Single Shot Detector, and Segmentation
      • 7.1.7.8.1 YOLO, You Only Look Once, Family Object Detection
      • 7.1.7.8.2 SWIN Transformer
    • 7.1.7.9 Semi-Supervised Object Detection
    • 7.1.7.10 Instance of Particular Object, Specified Object
    • 7.1.7.11 Fiducial Markers Design, Detection and Analysis
    • 7.1.7.12 Generic Object Detection, Open World, Open-Set, Open-Vocabulary
    • 7.1.7.13 Dense Object Detection
  • 7.1.8 Fisheye Camera, Fisheye Image Detection, Analysis, Rectification
  • 7.1.9 Masked Image Modeling
  • 7.1.10 Interest Operators, Interest Points, Feature Points, Salient Points
    • 7.1.10.1 Local Features, Computation, Analysis
    • 7.1.10.2 Keypoint Detection
    • 7.1.10.3 HOG Analysis, Histogram of Oriented Gradient
    • 7.1.10.4 Three-Dimensional Interest Points, Depth Data Interest Points
    • 7.1.10.5 Rotation Invariant Features
    • 7.1.10.6 Scale Invariant Features, SIFT, SURF, ASIFT
    • 7.1.10.7 Local Binary Patterns, LBP, Point Features
    • 7.1.10.8 Vector of Locally Aggregated Descriptors, VLAD Features
    • 7.1.10.9 Evaluation of Salient Point, Interest Point Detection, Object Detection
  • 7.1.11 Contourlet Representations and Processing
  • 7.1.12 Region of Interest Detection, ROI
    • 7.1.12.1 Salient Regions, Saliencey for Regions
      • 7.1.12.1.1 Weakly Supervised, Unsupervised Salient Regions
      • 7.1.12.1.2 Color, Multispectral, RGB, for Salient Regions
      • 7.1.12.1.3 Salient Regions, Convolutional Neural Networks, Deep Nets
    • 7.1.12.2 Object Proposals, Initial Points, Proto-Objects, Candidates
    • 7.1.12.3 Small Objects, Detect Small Objects
  • 7.1.13 Maximally Stable Extremal Regions, MSER Descriptions
• 7.2 Computation of Shape Features of Two Dimensional Objects
  • 7.2.1 General Shape Computation and Representation
    • 7.2.1.1 Shape Computation Surveys, Comparisons
    • 7.2.1.2 Computing Area, Diameter and Perimeter
    • 7.2.1.3 Shape Measures
      • 7.2.1.3.1 MDL, Minimum Description Length for Shape Measure
  • 7.2.2 Convex Hull Algorithms and Convexity Analysis
    • 7.2.2.1 Convex Hull of Polygons
    • 7.2.2.2 Concavity Detection
    • 7.2.2.3 Inside/Outside Tests
  • 7.2.3 Digital Geometry
    • 7.2.3.1 Digital Geometry -- Lines, Curves and Contours
    • 7.2.3.2 Triangular, Hexagonal Grids, Geometry, Computations
  • 7.2.4 Digital Topology
  • 7.2.5 Waveform and Contour Analysis
• 7.3 Skeletons and Axial Descriptions - Medial Axis Transform (MAT) etc.
  • 7.3.1 Distance Transforms, Functions and Skeletons
  • 7.3.2 Use of Skeletons for Recognition and Representation
  • 7.3.3 Medial Axis Transform, MAT, Skeletons in Three Dimensions
  • 7.3.4 Processing of Skeletons for Descriptions
  • 7.3.5 Axial Descriptions
  • 7.3.6 Thinning Algorithms
    • 7.3.6.1 Thinning Algorithms in Three Dimensions
  • 7.3.7 Distance Transforms, Distance Functions, Distance Measures
    • 7.3.7.1 Three Dimensional Distance Transforms and Distance Functions
    • 7.3.7.2 Similarity Measure, Distance Transforms and Functions for Objects and Shapes
      • 7.3.7.2.1 Just Noticiable Difference, JND
    • 7.3.7.3 Closest Point Algorithms, ICP, Iterative Closest Point
  • 7.3.8 Fast, Parallel, Multiresolution Techniques for the Computation of Skeletons
• 7.4 Symmetries in Two Dimensions
  • 7.4.1 Ribbon Descriptions
• 7.5 Processing and Analysis of Binary (Two Level) Images
• 7.6 Extraction and Analysis of Connected Components and Boundaries
• 7.7 Two Dimensional Data Representations, Image Order
  • 7.7.1 Run-Length Coding Representations and Operations
  • 7.7.2 Voronoi Diagrams, Delaunay Triangulation, 2-D Meshes
  • 7.7.3 Contour Coding, Boundary Coding
• 7.8 Quadtree, Hierarchical and Pyramid Representations
  • 7.8.1 Quadtree Representations
  • 7.8.2 Quadtree Generation and Computations
    • 7.8.2.1 Quadtree Boundary Operations
  • 7.8.3 Pyramid Representations
• 7.9 Review, Comparison, Evaluation of Texture Analysis Techniques
  • 7.9.1 Texture Models, Analysis Techniques
• 7.10 Statistical Methods for Texture Description and Analysis
  • 7.10.1 Texture Discrimination and Classification
  • 7.10.2 Color Textures and Texture with Color
  • 7.10.3 Markov Random Field Models
  • 7.10.4 Hierarchical, Multi-Scale Texture Representations and Analysis
  • 7.10.5 Texture Synthesis
    • 7.10.5.1 Models for Texture Synthesis
  • 7.10.6 Oriented Texture, Directional Texture Patterns
  • 7.10.7 Filter Approaches to Texture
    • 7.10.7.1 Local Binary Patterns, LPB for Texture
    • 7.10.7.2 Wavelets, Gabor Filters for Texture
  • 7.10.8 Fractals for Texture Analysis and Description, Fractal Dimension
  • 7.10.9 Autoregressive Texture Models, AR Models
  • 7.10.10 Autocorrelation Texture Models
  • 7.10.11 Statistical Image Models
  • 7.10.12 Dynamic Textures
  • 7.10.13 Texture for Defect Detection
• 7.11 Transforms for Texture -- Fourier
• 7.12 Co-occurrence Matrix Description Methods
• 7.13 Structural Methods for Texture Description
  • 7.13.1 Texture Periods Description