Face Detection and Object Recognition for a Retinal Prosthesis

Rollend, Derek; Rosendall, Paul E.; Billings, Seth; Burlina, Philippe; Wolfe, Kevin; Katyal, Kapil D.

doi:10.1007/978-3-319-54407-6_20

Cited by 2 publications

(6 citation statements)

References 11 publications

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“…Whether the model can meet the real-time requirements of the processing part in the implant device and help the implant user better obtain the useful information of the image are of great importance for visual prostheses. Many researchers chose to input a series of static images from a public or self-built dataset to serve as learning samples, 46,50,51,54,55 whereas others chose different types of input such as video sequences. 48 In Zhao's study, 54 pairs of object images and their pixelized results were adopted as the training samples.…”

Section: Methodsmentioning

confidence: 99%

“…For the platform to implement the algorithm, some researchers integrated the algorithm and optimization strategies into a program and installed it on a desktop or laptop computer, 50 and some chose computers with a GPU to run algorithms with real-time requirements. 50 In terms of the processing on the original image, researchers cut the original image into rectangles of the same size (some cut to different sizes to investigate the impact of input resolution on the time efficiency and accuracy of system processing 24,25,26,27,36,40,41 ) and adjusted the perspective of the original image according to the vision field presented by the prosthesis. Experiments had different parameters according to application field as well.…”

Section: Methodsmentioning

confidence: 99%

“…As for the selection of subjects, most researchers appropriately selected people with normal vision and corrected vision of different ages and genders who possessed no relevant experience in this field and assigned them proportionally to experimental groups corresponding to several image optimization strategies to evaluate their performance. For the platform to implement the algorithm, some researchers integrated the algorithm and optimization strategies into a program and installed it on a desktop or laptop computer, 50 and some chose computers with a GPU to run algorithms with real-time requirements. 50 In terms of the processing on the original image, researchers cut the original image into rectangles of the same size (some cut to different sizes to investigate the impact of input resolution on the time efficiency and accuracy of system processing 24,25,26,27,36,40,41 ) and adjusted the perspective of the original image according to the vision field presented by the prosthesis.…”

Section: Comparisons Of the T Wo Methodsmentioning

confidence: 99%

“…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). Sanchez‐Garcia et al 51 carried out recognition experiments for room type with simulated prosthetic vision.…”

Section: Machine Learning–based Methodsmentioning

confidence: 99%

“…This requires image processing to be capable of multitask processing in real time with good performance. Rolland et al 50 originally integrated face detection and object recognition models and ran them successfully in real time on low power, embedded devices. To fulfill multitasking in functional vision, the models need to extract different features from images, or to be more complex for the computation or model structure.…”

Section: Future Developmentsmentioning

confidence: 99%

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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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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Comparisons Of the T Wo Methodsmentioning

confidence: 99%

Section: Machine Learning–based Methodsmentioning

confidence: 99%

Section: Future Developmentsmentioning

confidence: 99%

See 3 more Smart Citations

The application of computer vision to visual prosthesis

et al. 2021

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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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Face Detection and Object Recognition for a Retinal Prosthesis

Cited by 2 publications

References 11 publications

The application of computer vision to visual prosthesis

The application of computer vision to visual prosthesis

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

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