2D shape recognition by hidden Markov models
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. We address the use of hidden Markov models (HMM) for shape analysis, based on chain code representation of object contours. HMM represent a widespread approach to the modeling of sequences, and are largely used for many applications, but unfortunately are poorly considered in the literature concerning shape analysis, and in any case, without reference to noise or occlusion sensitivity. The HMM approach to shape modeling is tested, probing good invariance of this method in terms of noise, occlusions, and object scaling.