A CORF computational model of a simple cell that relies on LGN input outperforms the Gabor function model
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Simple cells in primary visual cortex are believed to extract local contour information from a visual scene. The 2D Gabor function (GF) model has gained particular popularity as a computational model of a simple cell. However, it short-cuts the LGN, it cannot reproduce a number of properties of real simple cells, and its effectiveness in contour detection tasks has never been compared with the effectiveness of alternative models. We propose a computational model that uses as afferent inputs the responses of model LGN cells with center–surround receptive fields (RFs) and we refer to it as a Combination of Receptive Fields (CORF) model. We use shifted gratings as test stimuli and simulated reverse correlation to explore the nature of the proposed model. We study its behavior regarding the effect of contrast on its response and orientation bandwidth as well as the effect of an orthogonal mask on the response to an optimally oriented stimulus. We also evaluate and compare the performances of the CORF and GF models regarding contour detection, using two public data sets of images of natural scenes with associated contour ground truths. The RF map of the proposed CORF model, determined with simulated reverse correlation, can be divided in elongated excitatory and inhibitory regions typical of simple cells. The modulated response to shifted gratings that this model shows is also characteristic of a simple cell. Furthermore, the CORF model exhibits cross orientation suppression, contrast invariant orientation tuning and response saturation. These properties are observed in real simple cells, but are not possessed by the GF model. The proposed CORF model outperforms the GF model in contour detection with high statistical confidence (RuG data set: p < 10−4, and Berkeley data set: p < 10−4). The proposed CORF model is more realistic than the GF model and is more effective in contour detection, which is assumed to be the primary biological role of simple cells.
KeywordsAligned receptive fields Computational model Contour detection Gabor function LGN Simple cell
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- Azzopardi G, Petkov N (2012) Trainable COSFIRE filters for keypoint detection and pattern recognition. IEEE Trans Pattern Anal Mach Intell (to appear)Google Scholar
- De Valois RL, Albrecht DG, Thorell LG (1978) Cortical cells: bar and edge detectors, or spatial frequency filters? In: Cool SJ, Smith I E L (eds) Frontiers in visual science, Springer-Verlag, Berlin, West Germany pp 544–556Google Scholar
- Gabor D (1946) Theory of communication. J Inst Electr Eng 93: 429–457Google Scholar
- Heitger F (1995) Feature detection using suppression and enhancement. Communication Technology Laboratory, Swiss Federal Institute of Technology, Lausanne, Technical Report TR-163Google Scholar
- Kovesi P (1999) Image features from phase congruency. Videre 1(3)Google Scholar
- Martin D, Fowlkes C, Tal D, Malik J (2001) A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: Proceedings of the 8th international conference computer vision, vol 2, pp 416–423Google Scholar
- Rosenthaler L, Heitger F, Kubler O, von der Heydt R (1992) Detection of general edges and keypoints. In: Sandini G (ed) Proceedings of the European Conference Computer Vision (ECCV92), pp 78–86Google Scholar
- Shin MC, Goldgof D, Bowyer KW (1998) An objective comparison methodology of edge detection algorithms using a structure from motion task. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1998 (Cat. No.98CB36231), pp 190–195Google Scholar
- Sonka M, Hlavac V, Boyle R (1999) Image processing, analysis, and machine vision. Brooks/Cole, Pacific GroveGoogle Scholar