Hidden Markov models vs syntactic modeling in object recognition

This paper addresses the problem of object recognition based on contour descriptions. Two approaches, namely hidden Markov models (HMM) and syntactic modeling based on stochastic finite-state grammars (SFSG), are analyzed and applied to the classification of hardware tools. It is shown that both approaches are able to capture the data variability, leading to high classification performances. While the syntactic paradigm is flexible, the structure of the grammars being automatically inferred from the data, the HMMs reveal to be more robust in terms of training data sets requirements.