Investigating Hidden Markov Models' Capabilities in 2D Shape Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern recognition using neural-fuzzy networks based on improved particle swam optimization
Expert Systems with Applications: An International Journal
Similarity-based classification of sequences using hidden Markov models
Pattern Recognition
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Abstract: In Computer Vision, two-dimensional shape classification is a complex and well studied topic, often basic for three-dimensional object recognition. Object contours are a widely chosen feature for representing objects, useful in many respects for classification problems. In this paper, we address the use of Hidden Markov Models (HMMs) for shape analysis, based on chain code representation of object contours. HMMs represent a widespread approach to the modeling of sequences, and are largely used for many applications, but unfortunately it is poorly considered in literature concerning shape analysis, and, in any case, without reference on noise or occlusion sensitivity. In this paper HMM approach to shape modeling is tested, probing good invariance of this method in term of noise, occlusions, and object scaling.