Trace Inference, Curvature Consistency, and Curve Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Advances in neural information processing systems 2
Inferring global perceptual contours from local features
International Journal of Computer Vision - Special issue on computer vision research at the University of Southern California
Local parallel computation of stochastic completion fields
Neural Computation
Algorithms for straight line fitting using k-means
Pattern Recognition Letters
Quantitative measures of change based on feature organization: eigenvalues and eigenvectors
Computer Vision and Image Understanding
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
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 casts the problem of perceptual grouping into an evidence combining setting using the apparatus of the EM algorithm. We are concerned with recovering a perceptual arrangement graph for line-segments using evidence provided by a raw perceptual grouping field. The perceptual grouping process is posed as one of pairwise relational clustering. The task is to assign line-segments (or other 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 pairwise affinities or link-weights for the nodes of a perceptual relation graph. Commencing from a simple probability distribution for these parameters, we show how they may be estimated using the apparatus of the EM algorithm. The new method is demonstrated on line-segment grouping problems where it is shown to outperform a non-iterative eigenclustering method.