V. Vonikakis, I. Andreadis, and A. Gasteratos (Greece)
Human Visual System, shape description, kernels.
This paper presents a new method suitable for shape classification, inspired by the early processing levels of the human visual system. It extracts a description for any simple 2-dimensional shape having a closed contour, regardless of its size, rotation and position, in affordable computational cost. The paper introduces a new approach to the modeling of the hypercolumns of the primary visual cortex, which requires significantly less computational burden and that is highly parallel. A new shape descriptor based on the relative angles of an object is also proposed. It produces close results for different shapes of the same object, it is proportion-flexible and it can identify distorted shapes correctly. Experimental results prove that the method is adequate for industrial production applications based on shape classification, as well as for shape-based image retrieval.
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