Unsupervised foveal vision neural architecture with top-down attention

Burt, Ryan; Thigpen, Nina; Keil, Andreas; Prı́ncipe, José C.

doi:10.1016/j.neunet.2021.03.003

Cited by 5 publications

(2 citation statements)

References 72 publications

(103 reference statements)

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“…This needs to be repeated with thousands of individuals with normal vision so that the data recorded are substantial and provide sufficient facts to derive convincing results [30]. There may be small variations in the output as we take the readings for different individuals.…”

Section: Figure 3: Electroretinographymentioning

confidence: 99%

Encoding the Photoreceptors of the Human Eye

Roy¹,

Nagrale²

2022

Cureus

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This review aims to assess the anatomy of the human eye with a focus on exploring opportunities to mimic certain functionalities of photoreceptors in the optical system. This can help restore vision issues in people who had normal vision earlier, but their vision was impaired due to reasons that damaged parts of the eye; however, the functionality of the optic nerve remained intact. It is a conceptual article where the methodology to simulate artificial photoreceptors is discussed.

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Section: Figure 3: Electroretinographymentioning

confidence: 99%

Encoding the Photoreceptors of the Human Eye

Roy¹,

Nagrale²

2022

Cureus

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“…Countless applications of saliency detection have led to the occurrence of numerous methods for saliency computation. The orthodox saliency detection methods can be classified into two approaches, top-down and bottom-up, based on the perspective of information processing [9,[32][33][34][35]. The top-down approach is task-driven with semantic information, prior knowledge and it focuses on supervised machine learning from a plethora of training images [8,32,36].…”

Section: Introductionmentioning

confidence: 99%

Detecting Salient Image Objects Using Color Histogram Clustering for Region Granularity

Joseph

Olugbara

2021

J. Imaging

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Salient object detection represents a novel preprocessing stage of many practical image applications in the discipline of computer vision. Saliency detection is generally a complex process to copycat the human vision system in the processing of color images. It is a convoluted process because of the existence of countless properties inherent in color images that can hamper performance. Due to diversified color image properties, a method that is appropriate for one category of images may not necessarily be suitable for others. The selection of image abstraction is a decisive preprocessing step in saliency computation and region-based image abstraction has become popular because of its computational efficiency and robustness. However, the performances of the existing region-based salient object detection methods are extremely hooked on the selection of an optimal region granularity. The incorrect selection of region granularity is potentially prone to under- or over-segmentation of color images, which can lead to a non-uniform highlighting of salient objects. In this study, the method of color histogram clustering was utilized to automatically determine suitable homogenous regions in an image. Region saliency score was computed as a function of color contrast, contrast ratio, spatial feature, and center prior. Morphological operations were ultimately performed to eliminate the undesirable artifacts that may be present at the saliency detection stage. Thus, we have introduced a novel, simple, robust, and computationally efficient color histogram clustering method that agglutinates color contrast, contrast ratio, spatial feature, and center prior for detecting salient objects in color images. Experimental validation with different categories of images selected from eight benchmarked corpora has indicated that the proposed method outperforms 30 bottom-up non-deep learning and seven top-down deep learning salient object detection methods based on the standard performance metrics.

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Hardware Acceleration of Explainable AI

Pan,

Mishra

2023

Explainable AI for Cybersecurity

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Unsupervised foveal vision neural architecture with top-down attention

Cited by 5 publications

References 72 publications

Encoding the Photoreceptors of the Human Eye

Encoding the Photoreceptors of the Human Eye

Detecting Salient Image Objects Using Color Histogram Clustering for Region Granularity

Hardware Acceleration of Explainable AI

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