A real-time image optimization strategy based on global saliency detection for artificial retinal prostheses

Li, Heng; Han, Tingting; Wang, Jing; Lu, Zhuofan; Cao, Xiaofei; Chen, Yao; Li, Liming; Zhou, Chuanqing; Chai, Xinyu

doi:10.1016/j.ins.2017.06.014

Cited by 20 publications

(27 citation statements)

References 28 publications

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“…Most of the SPV configurations are usually based on a computer screen for the presentation of static or dynamic phosphene images [29,74,75]. This methodology allow controlled evaluation of normally sighted subject response and task performance which is fundamental to know the way humans perceive and interpret phosphenized renderings.…”

Section: Methodsmentioning

confidence: 99%

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: Methodsmentioning

confidence: 99%

Semantic and structural image segmentation for prosthetic vision

2020

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“…In addition, we will investigate the effectiveness of our method compared to other visual tasks, such as eye–hand coordination. For example, Li et al proposed a real-time image-processing method based on global saliency detection and showed significant advantages in eye–hand coordination visual tasks [ 20 ].…”

Section: Discussionmentioning

confidence: 99%

Spatiotemporal Pixelization to Increase the Recognition Score of Characters for Retinal Prostheses

Kim

Park

2017

Sensors

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Most of the retinal prostheses use a head-fixed camera and a video processing unit. Some studies proposed various image processing methods to improve visual perception for patients. However, previous studies only focused on using spatial information. The present study proposes a spatiotemporal pixelization method mimicking fixational eye movements to generate stimulation images for artificial retina arrays by combining spatial and temporal information. Input images were sampled with a resolution that was four times higher than the number of pixel arrays. We subsampled this image and generated four different phosphene images. We then evaluated the recognition scores of characters by sequentially presenting phosphene images with varying pixel array sizes (6 × 6, 8 × 8 and 10 × 10) and stimulus frame rates (10 Hz, 15 Hz, 20 Hz, 30 Hz, and 60 Hz). The proposed method showed the highest recognition score at a stimulus frame rate of approximately 20 Hz. The method also significantly improved the recognition score for complex characters. This method provides a new way to increase practical resolution over restricted spatial resolution by merging the higher resolution image into high-frame time slots.

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“…Bottom-up approaches generally aim at detecting the most important regions of an image from a visual perception viewpoint by using low-level features as in literature (Achanta, Hemami, Estrada, & Susstrunk, 2009; Cheng, Mitra, Huang, Torr, & Hu, 2015; Harel, Koch, & Perona, 2007; Itti, Koch, & Niebur, 1998). Li et al. (2017) proposed a bottom-up approach for visual saliency detection in real time aiming at detecting and enhancing visual objects in the foreground of a scene.…”

Section: Introductionmentioning

confidence: 99%

“…(2018) performed a recoloring algorithm based on Hue, Saturation and Value (HSV) space color channels. Cercenelli et al. (2017) proposed a new MATLAB toolbox for saccade analysis to increase usability of eye-tracking systems in clinical ophthalmology practice.…”

Section: Introductionmentioning

confidence: 99%

Image Content Enhancement Through Salient Regions Segmentation for People With Color Vision Deficiencies

et al. 2019

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Color vision deficiencies affect visual perception of colors and, more generally, color images. Several sciences such as genetics, biology, medicine, and computer vision are involved in studying and analyzing vision deficiencies. As we know from visual saliency findings, human visual system tends to fix some specific points and regions of the image in the first seconds of observation summing up the most important and meaningful parts of the scene. In this article, we provide some studies about human visual system behavior differences between normal and color vision-deficient visual systems. We eye-tracked the human fixations in first 3 seconds of observation of color images to build real fixation point maps. One of our contributions is to detect the main differences between the aforementioned human visual systems related to color vision deficiencies by analyzing real fixation maps among people with and without color vision deficiencies. Another contribution is to provide a method to enhance color regions of the image by using a detailed color mapping of the segmented salient regions of the given image. The segmentation is performed by using the difference between the original input image and the corresponding color blind altered image. A second eye-tracking of color blind people with the images enhanced by using recoloring of segmented salient regions reveals that the real fixation points are then more coherent (up to 10%) with the normal visual system. The eye-tracking data collected during our experiments are in a publicly available dataset called Eye-Tracking of Color Vision Deficiencies.

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A real-time image optimization strategy based on global saliency detection for artificial retinal prostheses

Cited by 20 publications

References 28 publications

Semantic and structural image segmentation for prosthetic vision

Semantic and structural image segmentation for prosthetic vision

Spatiotemporal Pixelization to Increase the Recognition Score of Characters for Retinal Prostheses

Image Content Enhancement Through Salient Regions Segmentation for People With Color Vision Deficiencies

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