Embedded clutter reduction and face detection algorithms for a visual prosthesis

Rollend, Derek; Rosendall, Paul E.; Wolfe, Kevin; Kleissas, Dean; Billings, Seth; Oben, Jonathan M.; Helder, John B.; Burlina, Philippe; Roy, Arup; Greenberg, Robert M.; Katyal, Kapil D.

doi:10.1109/embc.2016.7590727

Cited by 4 publications

(2 citation statements)

References 5 publications

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“…Due to the limitations on the size of the multiple electrode array (MEA), saliency detection-based methods were adopted to zoom in on the interest zone for better object recognition. With the development of artificial intelligence, AI-based image processing methods is helpful for in-depth coding, semantic segmentation, and face recognition [93][94][95].…”

Section: Computer Vision-related Methodsmentioning

confidence: 99%

Artificial intelligence techniques for retinal prostheses: a comprehensive review and future direction

Wang

Fang

Zou³

et al. 2023

J. Neural Eng.

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Abstract. Objective. Retinal prostheses are promising devices to restore vision for patients with severe AMD or RP disease. The visual processing mechanism embodied in retinal prostheses play an important role in the restoration effect. Its performance depends on our understanding of the retina’s working mechanism and the evolvement of computer vision models. Recently, remarkable progress has been made in the field of processing algorithm for retinal prostheses where the new discovery of the retina’s working principle and state-of-the-arts computer vision models are combined together. Approach. We investigated the related research on artificial intelligence techniques for retinal prostheses. The processing algorithm in these studies could be attributed to three types: computer vision-related methods, biophysical models, and deep learning models. Main results. In this review, we first illustrate the structure and function of the normal and degenerated retina, then demonstrate the vision rehabilitation mechanism of three representative retinal prostheses. It is necessary to summarize the computational frameworks abstracted from the normal retina. In addition, the development and feature of three types of different processing algorithms are summarized. Finally, we analyze the bottleneck in existing algorithms and propose our prospect about the future directions to improve the restoration effect. Significance. This review systematically summarizes existing processing models for predicting the response of the retina to external stimuli. What’s more, the suggestions for future direction may inspire researchers in this field to design better algorithms for retinal prostheses.

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Section: Computer Vision-related Methodsmentioning

confidence: 99%

Artificial intelligence techniques for retinal prostheses: a comprehensive review and future direction

Wang

Fang

Zou³

et al. 2023

J. Neural Eng.

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“…Other works also focused on face recognition and scene recognition to study the roles and application feasibility of other machine learning models. Rollend et al 49 proposed a set of algorithms for face recognition and background clutter reduction for Argus II visual prosthesis wearers. In the following year, they further improved this achievement 50 by adding an object detection function, combining it with the previously proposed face recognition method to form a system, and integrating them into an application program, which was tested on a laptop computer equipped with a graphic processing unit (GPU).…”

Section: Machine Learning–based Methodsmentioning

confidence: 99%

The application of computer vision to visual prosthesis

et al. 2021

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A visual prosthesis is an auxiliary device for patients with blinding diseases that cannot be treated with conventional surgery or drugs. It converts captured images into corresponding electrical stimulation patterns, according to which phosphenes are generated through the action of internal electrodes on the visual pathway to form visual perception. However, due to some restrictions such as the few implantable electrodes that the biological tissue can accommodate, the induced perception is far from ideal. Therefore, an important issue in visual prosthesis research is how to detect and present useful information in low-resolution prosthetic vision to improve the visual function of the wearer. In recent years, with the development and broad application of computer vision methods, researchers have investigated the possibility of their utilization in visual prostheses by simulating prosthetic visual percepts.Through the optimization of visual perception by image processing, the efficiency of visual prosthesis devices can be further improved to better meet the needs of prosthesis wearers. In this article, recent works on prosthetic vision centering on implementing computer vision methods are reviewed. Differences, strengths, and weaknesses of the mentioned methods are discussed. The development directions of optimizing prosthetic vision and improving methods of visual perception are analyzed.

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