Contrast-dependent surround suppression models for contour detection

Tang, Qiling; Sang, Nong; Liu, Haihua

doi:10.1016/j.patcog.2016.05.009

Cited by 17 publications

(4 citation statements)

References 59 publications

(110 reference statements)

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“…The notion of texton is an effective tool for describing human textural perception, which is successfully applied to different visual processing tasks, including contour detection [25], texture classification [26], and object recognition [27]. It also finds widespread applications in the medical domain, such as breast cancer risk estimation [28], breast density characterization [29], and retinal vessel segmentation [30].…”

Section: Texton Assignment By Locality-constrained Linear Codingmentioning

confidence: 99%

Medical Image Retrieval Using Multi-Texton Assignment

2017

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In this paper, we present a multi-texton representation method for medical image retrieval, which utilizes the locality constraint to encode each filter bank response within its local-coordinate system consisting of the k nearest neighbors in texton dictionary and subsequently employs spatial pyramid matching technique to implement feature vector representation. Comparison with the traditional nearest neighbor assignment followed by texton histogram statistics method, our strategies reduce the quantization errors in mapping process and add information about the spatial layout of texton distributions and, thus, increase the descriptive power of the image representation. We investigate the effects of different parameters on system performance in order to choose the appropriate ones for our datasets and carry out experiments on the IRMA-2009 medical collection and the mammographic patch dataset. The extensive experimental results demonstrate that the proposed method has superior performance.

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Section: Texton Assignment By Locality-constrained Linear Codingmentioning

confidence: 99%

Medical Image Retrieval Using Multi-Texton Assignment

2017

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“…Numerous studies ( Hubel and Wiesel 1962 ; Hess et al, 2003 ; Loffler 2008 ) have shown that the visual cortex plays a crucial role in edge detection and processing. Many studies have proposed bio-inspired edge detection algorithms ( Li 1998 ; Yen and Finkel 1998 ; Grigorescu et al, 2003 ; La Cara and Ursino 2008 ; Yang et al, 2014 ; Tang et al, 2016 ) by simulating the neural cell response pattern in the visual cortex. The dual-pathway structure of the biological visual system promotes information processing and information exchange in the visual cortex.…”

Section: Introductionmentioning

confidence: 99%

DPED: Bio-inspired dual-pathway network for edge detection

Chen

Lin

Qiao

2022

Front. Bioeng. Biotechnol.

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Edge detection is significant as the basis of high-level visual tasks. Most encoder-decoder edge detection methods used convolutional neural networks, such as VGG16 or Resnet, as the encoding network. Studies on designing decoding networks have achieved good results. Swin Transformer (Swin) has recently attracted much attention in various visual tasks as a possible alternative to convolutional neural networks. Physiological studies have shown that there are two visual pathways that converge in the visual cortex in the biological vision system, and that complex information transmission and communication is widespread. Inspired by the research on Swin and the biological vision pathway, we have designed a two-pathway encoding network. The first pathway network is the fine-tuned Swin; the second pathway network mainly comprises deep separable convolution. To simulate attention transmission and feature fusion between the first and second pathway networks, we have designed a second-pathway attention module and a pathways fusion module. Our proposed method outperforms the CNN-based SOTA method BDCN on BSDS500 datasets. Moreover, our proposed method and the Transformer-based SOTA method EDTER have their own performance advantages. In terms of FLOPs and FPS, our method has more benefits than EDTER.

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“…Yang et al [39] used cues of the visual system to modulate the final peripheral inhibition of neurons, which enhanced the inhibition performance for different visual features. Tang et al [40] proposed a peripheral inhibition model that modulates the inhibition term depending on the contrast and finally achieved a good effect. However, these algorithms use a preset method to complete the division of the suppression region of the nCRF, and use a plus or minus 45° line differentiate for the inhibitory end region and inhibitory side region, limiting the dynamic weighting of the inhibition kernel.…”

Section: Introductionmentioning

confidence: 99%

Bio‐inspired contour detection model based on multi‐bandwidth fusion and logarithmic texture inhibition

Lin

Cao

et al. 2019

IET image process

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Relevant physiological studies have revealed that the response of the classical receptive field (CRF) to visual stimuli could be suppressed by non‐CRF (nCRF) inhibition of the kernel in the primary visual cortex (V1). Based on this mechanism, many bio‐inspired contour detection models have been proposed, which are mainly achieved through CRF responses and nCRF surround inhibition calculation. In fact, the dynamic characteristics of neurons play an important role in contour detection in biological vision. Inspired by these visual mechanisms, the authors propose a contour detection model that emulates these dynamic characteristics. By introducing a multi‐bandwidth Gabor filter, according to the target image, they can effectively adjust the weight ratios of the filter to protect the contours and filter the background textures in the calculation of CRF responses. Additionally, they logarithmically modulate the nCRF inhibition kernel to make texture suppression more flexible and effective, thus improving the accuracy of detection algorithm as a whole. Compared with existing bio‐inspired contour detection models, the proposed model is more effective at contour detection, which will aid engineering applications that utilise pattern recognition in machine vision.

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Contrast-dependent surround suppression models for contour detection

Cited by 17 publications

References 59 publications

Medical Image Retrieval Using Multi-Texton Assignment

Medical Image Retrieval Using Multi-Texton Assignment

DPED: Bio-inspired dual-pathway network for edge detection

Bio‐inspired contour detection model based on multi‐bandwidth fusion and logarithmic texture inhibition

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