NORDIA08
* *Workshop on Non-Rigid Shape Analysis and Deformable Image Alignment
* 3D non-rigid registration for MPU implicit surfaces
* discrete search method for multi-modal non-rigid image registration, A
* Efficient partial shape matching using Smith-Waterman algorithm
* Face Model Fitting Based on Machine Learning from Multi-Band Images of Facial Components
* Geometric modeling of rigid and non-rigid 3D shapes using the global geodesic function
* Gromov-Hausdorff distances in Euclidean spaces
* MDL patch correspondences on unlabeled images with occlusions
* new framework for behavior modeling of organs and soft tissue using the Boundary-Element Methods, A
* Non-rigid registration of 3D surfaces by deformable 2D triangular meshes
* Not only size matters: Regularized partial matching of nonrigid shapes
* Riemannian manifold optimisation for non-rigid structure from motion
* Template-based paper reconstruction from a single image is well posed when the rulings are parallel
* topological method for shape comparison, A
* Tracking articulated bodies using Generalized Expectation Maximization
* Tracking deformable surfaces with optical flow in the presence of self occlusion in monocular image sequences
* Vesicles and amoebae: Globally constrained shape evolutions
17 for NORDIA08
NORDIA09
* *Workshop on Non-Rigid Shape Analysis and Deformable Image Alignment
* Bending invariant meshes and application to groupwise correspondences
* Detailed body shapes from flash photographs
* Effective and efficient interpolation for mutual information based multimodality elastic image registration
* Efficient retrieval of deformable shape classes using local self-similarities
* Fast nonrigid mesh registration with a data-driven deformation prior
* Integrating contour and skeleton for shape classification
* Joint estimation of deformable motion and photometric parameters in single view video
* Learning shape metrics based on deformations and transport
* Learning varying dimension radial basis functions for deformable image alignment
* Markov Chain Monte Carlo shape sampling using level sets
* Non-rigid registration between color channels based on joint-histogram entropy in subspace
* On reconstruction of non-rigid shapes with intrinsic regularization
* Online Active Feature Model for lip tracking
* phase field higher-order active contour model of directed networks, A
* Probabilistic constrained adaptive local displacement experts
* Shape Google: a computer vision approach to isometry invariant shape retrieval
* Spectral Gromov-Wasserstein distances for shape matching
* Uncalibrated non-rigid factorisation with automatic shape basis selection
* Unsupervised learning of human body parts from video footage
20 for NORDIA09
NORDIA10
* *Workshop on Non-Rigid Shape Analysis and Deformable Image Alignment
* Bypass information-theoretic shape similarity from non-rigid points-based alignment
* Content-aware image resizing by quadratic programming
* Continuous procrustes analysis to learn 2D shape models from 3D objects
* Local shape estimation from a single keypoint
* Persistence-based segmentation of deformable shapes
* Shape matching based on diffusion embedding and on mutual isometric consistency
* Straight skeletons for binary shapes
8 for NORDIA10
NORDIA11
* *Workshop on Non-Rigid Shape Analysis and Deformable Image Alignment
* Consistent pose normalization of non-rigid shapes using One-Class Support Vector Machines
* Deformable image alignment as a source of stereo correspondences on portraits
* Dense shape correspondences using spectral high-order graph matching
* Efficient nonlinear DTI registration using DCT basis functions
* Resolving occlusion in multiframe reconstruction of deformable surfaces
* Separating rigid motion from linear local deformation models
* Temperature distribution descriptor for robust 3D shape retrieval
8 for NORDIA11
North-Holland
* *Graphic Languages
* Statistical Optimization for Geometric Computation: Theory and Practice
* Topological Algorithms for Digital Image Processing
North Holland
* *Computer Vision
* *From Pixels to Features, II
* *Progress in Pattern Recognition I
* *Techniques For 3-D Machine Perception
* Bayesian Inference in Model-Based Machine Vision
* Computer Vision, A Unified, Biologically-Inspired Approach
* From Pixels to Features
* Graphs and Hypergraphs
8 for North Holland