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Image processing strategy based on saliency detection for artificial vision(PDF)

《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

Issue:
2019年第11期
Page:
1277-1283
Research Field:
医学影像物理
Publishing date:

Info

Title:
Image processing strategy based on saliency detection for artificial vision
Author(s):
WANG Jing1 2 ZHANG Yuting3 ZHANG Yun1 2 HAN Yanling1 HONG Zhonghua1 YANG Shuhu1
1. College of Information Technology, Shanghai Ocean University, Shanghai 201306, China; 2. Key Laboratory of Fisheries Information, Ministry of Agriculture and Rural Affairs, Shanghai 201306, China; 3. China Mobile Group Shanghai Co., Ltd, Shanghai 200082, China
Keywords:
retinal prosthesis artificial vision information processing image saliency detection
PACS:
R318
DOI:
DOI:10.3969/j.issn.1005-202X.2019.11.007
Abstract:
Objective To effectively solve the problem of limited visual information obtained by prosthetic implant users by introducing the appropriate image processing strategy into the visual information processing module of the prosthesis device for optimizing the low-resolution artificial vision information. Methods Based on the saliency detection algorithm (luminance contrast) of global luminance contrast features, a strategy of artificial visual information optimization for retinal prosthesis was proposed by combining color space conversion and visual attention simulation processing model. Two benchmark databases were used to evaluate the result of image processing. Meanwhile, the simulation of images of the frequently used objects for the blind people which were determined by epidemiological investigation was carried out. Results The evaluation results verified the superiority of the proposed strategy over original luminance contrast algorithm in extracting image foreground. The simulated result verified the feasibility of the proposed strategy in artificial vision. Conclusion The proposed strategy is helpful for retinal prosthetic implant users to better accomplish the basic visual tasks such as object recognition in daily life.

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Last Update: 2019-11-28