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.

• Type: Conference
• Year: 2001
• Where: ICIAP
• Authors: Manuele Bicego, Vittorio Murino
• Keywords: ICIAP 2001, Image Processing, Literature Concerning Shape, Object Contours, Two-dimensional Shape Classifcation