Computational model of dot-pattern selective cells

Kruizinga, P.; Petkov, Nikolay

doi:10.1007/s004220000153

Cited by 26 publications

(20 citation statements)

References 23 publications

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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%

“…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.…”

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confidence: 99%

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Robust signal detection in 3D fluorescence microscopy

2009

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Robust detection and localization of biomolecules inside cells is of great importance to better understand the functions related to them. Fluorescence microscopy and specific staining methods make biomolecules appear as point-like signals on image data, often acquired in 3D. Visual detection of such point-like signals can be time consuming and problematic if the 3D images are large, containing many, sometimes overlapping, signals. This sets a demand for robust automated methods for accurate detection of signals in 3D fluorescence microscopy. We propose a new 3D point-source signal detection method that is based on Fourier series. The method consists of two parts, a detector, which is a cosine filter to enhance the point-like signals, and a verifier, which is a sine filter to validate the result from the detector. Compared to conventional methods, our method shows better robustness to noise and good ability to resolve signals that are spatially close. Tests on image data show that the method has equivalent accuracy in signal detection in comparison to visual detection by experts. The proposed method can be used as an efficient point-like signal detection tool for various types of biological 3D image data. ' 2009 International Society for Advancement of Cytometry

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Section: Previous Methodsmentioning

confidence: 99%

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confidence: 99%

Robust signal detection in 3D fluorescence microscopy

2009

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“…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%

Phase-differencing in Stereo Vision - Solving the Localisation Problem

Buf

Terzic

Rodrigues

2013

Proceedings of the International Conference on Bio-Inspired Systems and Signal Processing

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Abstract:Complex Gabor filters with phases in quadrature are often used to model even-and odd-symmetric simple cells in the primary visual cortex. In stereo vision, the phase difference between the responses of the left and right views can be used to construct a disparity or depth map. Various constraints can be applied in order to construct smooth maps, but this leads to very imprecise depth transitions. In this theoretical paper we show, by using lines and edges as image primitives, the origin of the localisation problem. We also argue that disparity should be attributed to lines and edges, rather than trying to construct a 3D surface map in cortical area V1. We derive allowable translation ranges which yield correct disparity estimates, both for left-view centered vision and for cyclopean vision.

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“…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%

Visual Attention Based Motion Object Detection and Trajectory Tracking

Guo

et al. 2010

Advances in Multimedia Information Processing - PCM 2010

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Abstract.A motion trajectory tracking method using a novel visual attention model and kernel density estimation is proposed in this paper. As a crucial step, moving objects detection is based on visual attention. The visual attention model is built by combination of the static and motion feature attention map and a Karhunen-Loeve transform (KLT) distribution map. Since the visual attention analysis is conducted on object level instead of pixel level, the proposed method can detect any kinds of motion objects provided saliency without the affection of objects appearance and surrounding circumstance. After locating the region of moving object, the kernel density is estimated for trajectory tracking. The experimental results show that the proposed method is promising for moving objects detection and trajectory tracking.

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Computational model of dot-pattern selective cells

Cited by 26 publications

References 23 publications

Robust signal detection in 3D fluorescence microscopy

Robust signal detection in 3D fluorescence microscopy

Phase-differencing in Stereo Vision - Solving the Localisation Problem

Visual Attention Based Motion Object Detection and Trajectory Tracking

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