Efficient Implementation of the Fuzzy c-Means Clustering Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Algorithms for clustering data
Algorithms for clustering data
A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Factorial Hidden Markov Models
Machine Learning - Special issue on learning with probabilistic representations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning and Recognizing Human Dynamics in Video Sequences
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Steerable wedge filters for local orientation analysis
IEEE Transactions on Image Processing
Detection of machine failure: Hidden Markov Model approach
Computers and Industrial Engineering
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This paper is concerned with an application of Hidden markov Models (HMMs) to the generation of shape boundaries from image features. In the proposed model, shape classes are defined by sequences of "shape states" each of which has a probability distribution of expected image feature types (features "symbols"). The tracking procedure uses a generalization of the well-known Viterbi method by replacing its search by a type of "beam-search" so allowing the procedure, at any time, to consider less likely features (symbols) as well the search for an instantiable optimal state sequences. We have evaluated the model's performace on a variety of image shape types and have also developed a new performance measure defined by an expected Hamming distance between predicted and observed symbol sequences. Result point to the use of this type of model for the depiction of shape boundaries when it is necessary to have accurate boundary annotations as, for example, occurs in Cartogrpahy.