Detection of general edges and keypoints

Rosenthaler, L.; Heitger, F.; Kübler, Olaf; Heydt, Rüdiger von der

doi:10.1007/3-540-55426-2_10

Cited by 43 publications

(25 citation statements)

References 16 publications

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“…One approach inspired by neuro-biological mechanisms was proposed in [87,198]. They apply Gabor like filters to compute local energy of the signal.…”

Section: Feature Detection As Part Of the Pre-attentive Stagementioning

confidence: 99%

Local Invariant Feature Detectors: A Survey

Tuytelaars

Mikolajczyk

2007

FNT in Computer Graphics and Vision

1,126

573

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In this survey, we give an overview of invariant interest point detectors, how they evolved over time, how they work, and what their respective strengths and weaknesses are. We begin with defining the properties of the ideal local feature detector. This is followed by an overview of the literature over the past four decades organized in different categories of feature extraction methods. We then provide a more detailed analysis of a selection of methods which had a particularly significant impact on the research field. We conclude with a summary and promising future research directions.

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“…One approach inspired by neuro-biological mechanisms was proposed in [87,198]. They apply Gabor like filters to compute local energy of the signal.…”

Section: Feature Detection As Part Of the Pre-attentive Stagementioning

confidence: 99%

Local Invariant Feature Detectors: A Survey

Tuytelaars

Mikolajczyk

2007

FNT in Computer Graphics and Vision

1,126

573

View full text Add to dashboard Cite

show abstract

“…The local energy model [14] is employed to detect the boundary position and measure the boundary strength (local energy). The local energy of an image is formed as a combination of the oriented energy [18] over all orientations and the oriented energy represents the energy at a given orientation calculated via orientation selective filters. Given image I, the local energy at pixel p is defined as [14,17],…”

Section: Proposed Trilateral Filter Weight Functionmentioning

confidence: 99%

A Novel Trilateral Filter based Adaptive Support Weight Method for Stereo Matching

Chen¹,

Ardabilian²,

Chen³

2013

Procedings of the British Machine Vision Conference 2013

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Adaptive support weight (ASW) approach represents the state-of-the-art local stereo matching method. Recent extensive evaluation studies on ASW approaches show that the bilateral filter weight function enables outstanding performance on a large data set in comparison with various weight functions. However, it does not resolve the ambiguity induced by nearby pixels at different disparities but with similar colors. In this paper, we propose a novel trilateral filter based ASW method which remedies such ambiguities by considering disparity discontinuities through color discontinuity boundaries, i.e., the strength of the boundary between two pixels. The experimental evaluation on the Middlebury benchmark shows that the proposed algorithm ranks 15 th out of 150 submissions and is the current most accurate local stereo matching algorithm.

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“…The biological role of orientation-selective cells is believed to be the extraction of local contour information, which is a fundamental step for further, more complex visual tasks, such as object recognition (Morrone and Owens 1987;Morrone and Burr 1988;Rosenthaler et al 1992;Mehrotra et al 1992;Heitger 1995;Kovesi 1999). The performance of various computational models of a simple cell in contour detection tasks has, however, not been quantified and they have not been compared in that respect.…”

Section: Introductionmentioning

confidence: 99%

A CORF computational model of a simple cell that relies on LGN input outperforms the Gabor function model

Azzopardi

Petkov

2012

Biol Cybern

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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

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Detection of general edges and keypoints

Cited by 43 publications

References 16 publications

Local Invariant Feature Detectors: A Survey

Local Invariant Feature Detectors: A Survey

A Novel Trilateral Filter based Adaptive Support Weight Method for Stereo Matching

A CORF computational model of a simple cell that relies on LGN input outperforms the Gabor function model

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