Trace Inference, Curvature Consistency, and Curve Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature grouping in a hierarchical probabilistic network
Image and Vision Computing
Advances in neural information processing systems 2
A Bayesian multiple-hypothesis approach to edge grouping and contour segmentation
International Journal of Computer Vision
Inferring global perceptual contours from local features
International Journal of Computer Vision - Special issue on computer vision research at the University of Southern California
A probabilistic approach to perceptual grouping
Computer Vision and Image Understanding
Iterative curve organisation with the EM algorithm
Pattern Recognition Letters
Quantitative measures of change based on feature organization: eigenvalues and eigenvectors
Computer Vision and Image Understanding
A probabilistic method for extracting chains of collinear segments
Computer Vision and Image Understanding - Special issue on perceptual organization in computer vision
Pairwise Data Clustering by Deterministic Annealing
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Factorization Approach to Grouping
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
Normalized Cuts and Image Segmentation
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Segmentation Using Eigenvectors: A Unifying View
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Optical flow estimation and moving object segmentation based on median radial basis function network
IEEE Transactions on Image Processing
Pairwise Clustering with Matrix Factorisation and the EM Algorithm
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
Graph-Based Methods for Vision: A Yorkist Manifesto
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
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This paper presents an iterative maximum likelihood framework for perceptual grouping. We pose the problem of perceptual grouping as one of pairwise relational clustering. The method is quite generic and can be applied to a number of problems including region segmentation and line-linking. The task is to assign image tokens to clusters in which there is strong relational affinity between token pairs. The parameters of our model are the cluster memberships and the link weights between pairs of tokens. Commencing from a simple probability distribution for these parameters, we show how they may be estimated using an EM-like algorithm. The cluster memberships are estimated using an eigendecomposition method. Once the cluster memberships are to hand, then the updated link-weights are the expected values of their pairwise products. The new method is demonstrated on region segmentation and line-segment grouping problems where it is shown to outperform a noniterative eigenclustering method.