title: Image Processing Strategies Based on a Visual Saliency Model for Object Recognition Under Simulated Prosthetic Vision.
abstract: Retinal prostheses have the potential to restore partial vision. Object recognition in scenes of daily life is one of the essential tasks for implant wearers. Still limited by the low-resolution visual percepts provided by retinal prostheses, it is important to investigate and apply image processing methods to convey more useful visual information to the wearers. We proposed two image processing strategies based on Itti's visual saliency map, region of interest (ROI) extraction, and image segmentation. Itti's saliency model generated a saliency map from the original image, in which salient regions were grouped into ROI by the fuzzy c-means clustering. Then Grabcut generated a proto-object from the ROI labeled image which was recombined with background and enhanced in two ways--8-4 separated pixelization (8-4 SP) and background edge extraction (BEE). Results showed that both 8-4 SP and BEE had significantly higher recognition accuracy in comparison with direct pixelization (DP). Each saliency-based image processing strategy was subject to the performance of image segmentation. Under good and perfect segmentation conditions, BEE and 8-4 SP obtained noticeably higher recognition accuracy than DP, and under bad segmentation condition, only BEE boosted the performance. The application of saliency-based image processing strategies was verified to be beneficial to object recognition in daily scenes under simulated prosthetic vision. They are hoped to help the development of the image processing module for future retinal prostheses, and thus provide more benefit for the patients.
authors.affiliation: School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.|College of Information Technology, Shanghai Ocean University, Shanghai, China.
authors.forename: Jing
authors.initials: J
authors.lastname: Wang
authors.affiliation: School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
authors.forename: Heng
authors.initials: H
authors.lastname: Li
authors.affiliation: School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
authors.forename: Weizhen
authors.initials: W
authors.lastname: Fu
authors.affiliation: School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
authors.forename: Yao
authors.initials: Y
authors.lastname: Chen
authors.affiliation: School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
authors.forename: Liming
authors.initials: L
authors.lastname: Li
authors.affiliation: School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
authors.forename: Qing
authors.initials: Q
authors.lastname: Lyu
authors.affiliation: School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
authors.forename: Tingting
authors.initials: T
authors.lastname: Han
authors.affiliation: School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
authors.forename: Xinyu
authors.initials: X
authors.lastname: Chai
mesh_terms: D000328:Adult; D000465:Algorithms; D005260:Female; D017143:Fuzzy Logic; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008297:Male; D010363:Pattern Recognition, Automated / Q000379:methods*; D010775:Photic Stimulation; D011474:Prosthesis Design; D021641:Recognition, Psychology; D014796:Visual Perception; D057488:Visual Prosthesis; D019987:Persons with Visual Disabilities / Q000523:psychology / Q000534:rehabilitation*; D055815:Young Adult
keywords: Image processing; Object recognition; Retinal prosthesis; Simulated prosthetic vision; Visual saliency