2D Shape Recognition by Hidden Markov Models

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2D Shape Recognition by Hidden Markov Models
In Computer Vision, two-dimensional shape classifcation is a complex and well studied topic, often basic for three-dimensional object recognition. Object contours are a widely chosenfeature for representing objects, useful in many respects for classifcation 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 ispoorly considered in literature concerning shape analysis, and, in any case, without reference on noise or occlusion sensitivi9. In thispaper HMM approach to shape modeling is tested, probing good invariance of this method in term of noise, occlusions, and object scaling.
Manuele Bicego, Vittorio Murino
Added 08 Dec 2009
Updated 08 Dec 2009
Type Conference
Year 2001
Authors Manuele Bicego, Vittorio Murino
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