Distance transformations in digital images
Computer Vision, Graphics, and Image Processing
Multiple binary decision tree classifiers
Pattern Recognition
Joint Induction of Shape Features and Tree Classifiers
IEEE Transactions on Pattern Analysis and Machine Intelligence
The art of computer programming, volume 3: (2nd ed.) sorting and searching
The art of computer programming, volume 3: (2nd ed.) sorting and searching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Comparing Images Using the Hausdorff Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Similarity Measures, Properties and Constructions
VISUAL '00 Proceedings of the 4th International Conference on Advances in Visual Information Systems
A Probabilistic Formulation for Hausdorff Matching
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Locating objects using the Hausdorff distance
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
An Approach to Word Image Matching Based on Weighted Hausforff Distance
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
Shock Graphs and Shape Matching
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Analysis of Planar Shapes Using Geodesic Paths on Shape Spaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Coarse-to-Fine Strategy for Multiclass Shape Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical Shape Analysis: Clustering, Learning, and Testing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pedestrian Detection in Crowded Scenes
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Image Analysis and Mathematical Morphology
Image Analysis and Mathematical Morphology
Learning Shape-Classes Using a Mixture of Tree-Unions
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
Pedestrian Detection via Classification on Riemannian Manifolds
IEEE Transactions on Pattern Analysis and Machine Intelligence
Monocular Pedestrian Detection: Survey and Experiments
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape-Based Human Detection and Segmentation via Hierarchical Part-Template Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Online shape learning using binary search trees
Image and Vision Computing
Object detection combining recognition and segmentation
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part I
Automatic target recognition by matching oriented edge pixels
IEEE Transactions on Image Processing
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In this paper, a novel algorithm for shape matching based on the Hausdorff distance and a binary search tree data structure is proposed. The shapes are stored in a binary search tree that can be traversed according to a Hausdorff-like similarity measure that allows us to make routing decisions at any given internal node. Each node functions as a classifier that can be trained using supervised learning. These node classifiers are very similar to perceptrons, and can be trained by formulating a probabilistic criterion for the expected performance of the classifier, then maximizing that criterion. Methods for node insertion and deletion are also available, so that a tree can be dynamically updated. While offline training is time consuming, all online training and both online and offline testing operations can be performed in O(logn) time. Experimental results on pedestrian detection indicate the efficiency of the proposed method in shape matching.