Hierarchical Chamfer Matching: A Parametric Edge Matching Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Method for Registration of 3-D Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
Boundary Finding with Parametrically Deformable Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Smoothing and matching of 3-D space curves
International Journal of Computer Vision
Rigid, affine and locally affine registration of free-form surfaces
International Journal of Computer Vision
A Framework for Automatic Landmark Identification Using a New Method of Nonrigid Correspondence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer and Robot Vision
Comparing Images Using the Hausdorff Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Modal Matching for Correspondence and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Softassign Procrustes Matching Algorithm
IPMI '97 Proceedings of the 15th International Conference on Information Processing in Medical Imaging
IEEE Transactions on Image Processing
Handwritten Chinese Radical Recognition Using Nonlinear Active Shape Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition Letters - In memory of Professor E.S. Gelsema
Learning-Based Detection, Segmentation, and Matching of Objects
ICAPR '01 Proceedings of the Second International Conference on Advances in Pattern Recognition
Non-rigid registration using distance functions
Computer Vision and Image Understanding - Special issue on nonrigid image registration
Strings: Variational Deformable Models of Multivariate Continuous Boundary Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Learning of an Atlas from Unlabeled Point-Sets
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical Shape Analysis: Clustering, Learning, and Testing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dental Biometrics: Alignment and Matching of Dental Radiographs
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Bayesian, Exemplar-Based Approach to Hierarchical Shape Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Age Estimation Based on Facial Aging Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Active-GNG: model acquisition and tracking in cluttered backgrounds
VNBA '08 Proceedings of the 1st ACM workshop on Vision networks for behavior analysis
Shape and texture clustering: Best estimate for the clusters number
Image and Vision Computing
Contour Regularity Extraction Based on String Edit Distance
IbPRIA '09 Proceedings of the 4th Iberian Conference on Pattern Recognition and Image Analysis
Group-Wise Point-Set Registration Using a Novel CDF-Based Havrda-Charvát Divergence
International Journal of Computer Vision
A new editing scheme based on a fast two-string median computation applied to OCR
SSPR&SPR'10 Proceedings of the 2010 joint IAPR international conference on Structural, syntactic, and statistical pattern recognition
Elastic-string models for representation and analysis of planar shapes
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Robust curve clustering based on a multivariate t-distribution model
IEEE Transactions on Neural Networks
Characterization of contour regularities based on the Levenshtein edit distance
Pattern Recognition Letters
Data clustering: a user’s dilemma
PReMI'05 Proceedings of the First international conference on Pattern Recognition and Machine Intelligence
An approach to vision-based person detection in robotic applications
IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part I
An improved fast edit approach for two-string approximated mean computation applied to OCR
Pattern Recognition Letters
EM-GPA: Generalized Procrustes analysis with hidden variables for 3D shape modeling
Computer Vision and Image Understanding
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A procedure for automated $2D$ shape model design is presented. The modeling system is given a set of training example shapes defined by the coordinates of their contour points. The shapes are automatically aligned using Procrustes analysis and clustered to obtain cluster prototypes (typical objects) and statistical information about intracluster shape variation. One difference from previously reported methods is that the training set is first automatically clustered and those shapes considered to be outliers are discarded. In this way, the cluster prototypes are not distorted by outlier shapes. A second difference is in the manner in which registered sets of points are extracted from each shape contour. We propose a flexible point matching technique that takes into account both pose/scale differences as well as nonlinear shape differences between a pair of objects. The matching method is independent of the initial relative position/scale of the two objects and does not require any manually tuned parameters. Our shape model design method was used to learn 11 different shapes from contours that were manually traced in MR brain images. The resulting model was then employed to segment several MR brain images that were not included in the shape-training set. A quantitative analysis of our shape registration approach, within the main cluster of each structure, demonstrated results that compare very well to those achieved by manual registration; achieving an average registration error of about 1 pixel. Our approach can serve as a fully automated substitute to the tedious and time-consuming manual $2D$ shape registration and analysis.