How many clusters are best?—an experiment
Pattern Recognition
Algorithms for clustering data
Algorithms for clustering data
On the Sensitivity of the Hough Transform for Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the Verification of Hypothesized Matches in Model-Based Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Local Grayvalue Invariants for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM Computing Surveys (CSUR)
International Journal of Computer Vision - Special issue on statistical and computational theories of vision: modeling, learning, sampling and computing, Part I
Perceptual Organization and Visual Recognition
Perceptual Organization and Visual Recognition
Computational Framework for Segmentation and Grouping
Computational Framework for Segmentation and Grouping
Edge Detection by Helmholtz Principle
Journal of Mathematical Imaging and Vision
Geometric Hashing: An Overview
IEEE Computational Science & Engineering
A Grouping Principle and Four Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Extracting Meaningful Curves from Images
Journal of Mathematical Imaging and Vision
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
An A Contrario Decision Method for Shape Element Recognition
International Journal of Computer Vision
Fast computation of a contrast-invariant image representation
IEEE Transactions on Image Processing
On Straight Line Segment Detection
Journal of Mathematical Imaging and Vision
Automatically finding clusters in normalized cuts
Pattern Recognition
A probabilistic grouping principle to go from pixels to visual structures
DGCI'11 Proceedings of the 16th IAPR international conference on Discrete geometry for computer imagery
Conjoining Gestalt rules for abstraction of architectural drawings
Proceedings of the 2011 SIGGRAPH Asia Conference
Journal of Mathematical Imaging and Vision
SIAM Journal on Imaging Sciences
Random walk distances in data clustering and applications
Advances in Data Analysis and Classification
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A unified a contrario detection method is proposed to solve three classical problems in clustering analysis. The first one is to evaluate the validity of a cluster candidate. The second problem is that meaningful clusters can contain or be contained in other meaningful clusters. A rule is needed to define locally optimal clusters by inclusion. The third problem is the definition of a correct merging rule between meaningful clusters, permitting to decide whether they should stay separate or unite. The motivation of this theory is shape recognition. Matching algorithms usually compute correspondences between more or less local features (called shape elements) between images to be compared. Each pair of matching shape elements leads to a unique transformation (similarity or affine map.) The present theory is used to group these shape elements into shapes by detecting clusters in the transformation space.