Wednesday, May 27th 2015
Combination Of Receptive Fields (CORF): A novel computational simple cell model with application to contour detection and delineation.
The pioneering work of Hubel and Wiesel that led to the discovery of simple and complex cells in the visual cortex of cats, has been an inspiration to a large body of research in computational neuroscience and computer vision. We introduced a computational model of a simple cell called Combination of Receptive Fields or CORF for brevity. A CORF model uses as afferent inputs the responses of model LGN cells whose center-surround receptive fields are aligned in a co-linear manner, and combines their output with a weighted geometric mean. It achieves properties that are typical of real simple cells, namely push-pull inhibition, contrast invariant orientation tuning, cross orientation suppression, among others, which are not exhibited by the Gabor model. We demonstrated the effectiveness of the proposed CORF model in a contour detection task, which is believed to be the primary biological role of simple cells. We used two benchmark data sets (RuG and Berkeley) of images with natural scenes and showed that it outperforms the Gabor function model with (and without) surround suppression and Canny contour detectors. Moreover, the CORF model is also very effective for the delineation of vessels in retinal fundus images.
Colloquium coordinators are Prof.dr. M. Aiello (e-mail :
Prof.dr. M. Biehl (e-mail:
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