Optimization of Visual Information Presentation for Visual Prosthesis

Guo, Fei; Yang, Yuan; Gao, Yong

doi:10.1155/2018/3198342

Cited by 24 publications

(23 citation statements)

References 27 publications

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“…Wang et al [22] proposed two image representation strategies using background subtraction to segment moving elements for object recognition. Similarly, Guo et al [23] and Li et al [24] proposed two image processing strategies based on a saliency segmentation technique. For scene recognition, McCarthy et al [83] presented a visual representation based on intensity augments in order to emphasise regions of structural change.…”

Section: Actual/predictedmentioning

confidence: 99%

“…We evaluate and compare the proposed semantic and structural image segmentation with baseline methods through a Simulated Prosthetic Vision (SPV) experiment, which is a standard procedure for non-invasive evaluation using normal vision subjects [19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36]. The experiments included two tasks: object recognition and room identification.…”

Section: Introductionmentioning

confidence: 99%

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Semantic and structural image segmentation for prosthetic vision

2020

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Prosthetic vision is being applied to partially recover the retinal stimulation of visually impaired people. However, the phosphenic images produced by the implants have very limited information bandwidth due to the poor resolution and lack of color or contrast. The ability of object recognition and scene understanding in real environments is severely restricted for prosthetic users. Computer vision can play a key role to overcome the limitations and to optimize the visual information in the prosthetic vision, improving the amount of information that is presented. We present a new approach to build a schematic representation of indoor environments for simulated phosphene images. The proposed method combines a variety of convolutional neural networks for extracting and conveying relevant information about the scene such as structural informative edges of the environment and silhouettes of segmented objects. Experiments were conducted with normal sighted subjects with a Simulated Prosthetic Vision system. The results show good accuracy for object recognition and room identification tasks for indoor scenes using the proposed approach, compared to other image processing methods.

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Section: Actual/predictedmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Semantic and structural image segmentation for prosthetic vision

2020

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“…Image processing methods were evaluated by indexes such as precision, recall, and F-measure. 40,41 In addition, some special measures, such as JC score, 44 were used to evaluate the image segmentation effect. 34,36,39 To assess the effect of the algorithms intuitively, researchers designed simulated experiments related to functional vision in which a group of people with normal vision were selected as subjects.…”

Section: Application Of Object-based Detectionmentioning

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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“…Their results demonstrated that the effectiveness of adopting the novel saliency detection algorithm to improve the processing efficiency of strategy and the perception of objects in a scene. Guo et al [22] proposed visual information optimization strategies, which focus on the recognition of the salient object detection in static life scenes. The optimization strategies are based on a two-stage salient object detection model and exploit the gray transform and zooming techniques to optimize the salient object presentation.…”

Section: Introductionmentioning

confidence: 99%

Recognition of Moving Object in High Dynamic Scene for Visual Prosthesis

Guo

Yang

Xiao

et al. 2019

IEICE Trans. Inf. & Syst.

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Currently, visual perceptions generated by visual prosthesis are low resolution with unruly color and restricted grayscale. This severely restricts the ability of prosthetic implant to complete visual tasks in daily scenes. Some studies explore existing image processing techniques to improve the percepts of objects in prosthetic vision. However, most of them extract the moving objects and optimize the visual percepts in general dynamic scenes. The application of visual prosthesis in daily life scenes with high dynamic is greatly limited. Hence, in this study, a novel unsupervised moving object segmentation model is proposed to automatically extract the moving objects in high dynamic scene. In this model, foreground cues with spatiotemporal edge features and background cues with boundary-prior are exploited, the moving object proximity map are generated in dynamic scene according to the manifold ranking function. Moreover, the foreground and background cues are ranked simultaneously, and the moving objects are extracted by the two ranking maps integration. The evaluation experiment indicates that the proposed method can uniformly highlight the moving object and keep good boundaries in high dynamic scene with other methods. Based on this model, two optimization strategies are proposed to improve the perception of moving objects under simulated prosthetic vision. Experimental results demonstrate that the introduction of optimization strategies based on the moving object segmentation model can efficiently segment and enhance moving objects in high dynamic scene, and significantly improve the recognition performance of moving objects for the blind.

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Optimization of Visual Information Presentation for Visual Prosthesis

Cited by 24 publications

References 27 publications

Semantic and structural image segmentation for prosthetic vision

Semantic and structural image segmentation for prosthetic vision

The application of computer vision to visual prosthesis

Recognition of Moving Object in High Dynamic Scene for Visual Prosthesis

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