2000
DOI: 10.1007/s004220000153
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Computational model of dot-pattern selective cells

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Cited by 26 publications
(20 citation statements)
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“…The difference of Gaussians (DoG) is a set of filters that approximate the Mexican hat by subtracting a wide Gaussian from a narrow Gaussian (6). In a technical report by the same authors, a modified 2D DoG is proposed.…”
Section: Previous Methodsmentioning
confidence: 99%
See 1 more Smart Citation
“…The difference of Gaussians (DoG) is a set of filters that approximate the Mexican hat by subtracting a wide Gaussian from a narrow Gaussian (6). In a technical report by the same authors, a modified 2D DoG is proposed.…”
Section: Previous Methodsmentioning
confidence: 99%
“…A straightforward method for detecting point-like signals is the difference of Gaussians (DoG) (6), where the image is convolved with two Gaussian kernels of different sizes; the larger kernel describes the background variation while the smaller kernel describes the signals of interest. Gaussian kernels are also used in (4) where 2D signals from biological samples with varying noise levels are detected using multiscale products (MPs) of sub-band images from a wavelet transform.…”
mentioning
confidence: 99%
“…Simple and complex cells in area V1 may code noise patterns, such as stochastic textures, but their main purpose is to code lines and edges for object recognition and brightness perception (Rodrigues and du Buf, 2009). When focusing on models for disparity, one easily forgets that there also are other cells for coding specific patterns, like individual bars and periodic gratings (du Buf, 2007) and dot patterns like the ones used in Sanger's RDSs (Kruizinga and Petkov, 2000). Our insight into the complexity of the visual system, with a myriad of different cell functions and a zillion of connexions, is still rather poor.…”
Section: A Very Simple Model In a Very Complex Systemmentioning
confidence: 99%
“…Above results are valid for isolated events, i.e., if there is only one in a receptive field, and at all possible scales (the T 0 parameter). In case of dense patterns of lines and edges, the smallest scale will be the limiting factor unless specific circuits tuned to such patterns are assumed: periodic grating cells (du Buf, 2007) and dot pattern cells (Kruizinga and Petkov, 2000).…”
Section: Using the Phase Of One Viewmentioning
confidence: 99%
“…Information reflects objects importance in global background. A 2D difference of Gaussian (DoG) functions for every receptive region [11] is used to model the spatial summation properties of a center-surround cell [11]. The input image is segmented into different regions using K-means, and the image patches can be obtained as a perceptive unit.…”
Section: Static Attention Detectionmentioning
confidence: 99%