Evidence Accumulation Clustering Based on the K-Means Algorithm
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Investigating Hidden Markov Models' Capabilities in 2D Shape Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Hidden Markov Model approach for appearance-based 3D object recognition
Pattern Recognition Letters
Object Contour Extraction Using Adaptive B-Snake Model
Journal of Mathematical Imaging and Vision
Dynamic B-snake model for complex objects segmentation
Image and Vision Computing
A new adaptive B-spline VFC snake for object contour extraction
ICCCI'10 Proceedings of the Second international conference on Computational collective intelligence: technologies and applications - Volume Part III
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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 performance. While the syntactic paradigm is flexible, the structure of the grammars being automatically inferred from the data, the HMMs are more robust in terms of training data sets requirements.