Ghosh, Anarta

English

In this thesis the author deals with two important aspects of a computer-based pattern analysis system, viz., representation and classification. The representation involves extraction of characteristic features of the patterns and the classification deals with learning and discriminant analysis. The author investigates the performance of: (a) representation schemes of contour based shape patterns of objects present in digital images and (b) Learning Vector Quantization (LVQ) classifier in a proto-typical scenario. In the context of (a) more specifically the robustness of contour based shape descriptors to incomplete representations of objects is investigated. Using concepts of statistical physics and theory of on-line learning, the dynamics and the generalization ability of LVQ algorithms in a given data model are studied in the context of (b)

ARTS repository - University of Groningen

Robustness of shape descriptors and dynamics of learning vector quantization

https://pure.rug.nl/ws/files/2824712/02_c2.pdf

Robustness of shape descriptors and dynamics of learning vector quantization

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ARTS repository - University of Groningen