Image processing strategies based on saliency segmentation for object recognition under simulated prosthetic vision |
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| Affiliation: | 1. School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China;2. College of Information Technology, Shanghai Ocean University, Shanghai 201306, China;1. School of biotechnology, East China University of Science and Technology, Shanghai, China;2. Shanghai Center for Bioinformation Technology, Shanghai, China;3. Key Lab of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China;4. Shanghai Industrial Technology Institute, Shanghai, China;5. Shanghai Engineering Research Center of Pharmaceutical Translation, Shanghai, China;1. School of Informatics, Xiamen University, Xiamen, China;2. King’s College London, London, UK;3. University of Chinese Academy of Sciences, Beijing, China;4. National Centre for Computer Animation, Bournemouth University, Bournemouth, UK;1. Systems Engineering Institute, Xi’an Jiaotong University, 28 Xianning West Road, Xi’an, Shaanxi 710049, China;2. Department of Biostatistics, University of Michigan, 1415 Washington Heights, Ann Arbor, MI 48109, USA;1. Department of Computing and Information Systems, The University of Melbourne, Australia;2. New York University, USA;3. Ophthalmology, NYU School of Medicine, USA;1. School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China;2. Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China;3. Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences, Suzhou, China;4. Innovation Center for Textile Science and Technology, Donghua University, Shanghai, China |
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| Abstract: | Background and objectiveCurrent retinal prostheses can only generate low-resolution visual percepts constituted of limited phosphenes which are elicited by an electrode array and with uncontrollable color and restricted grayscale. Under this visual perception, prosthetic recipients can just complete some simple visual tasks, but more complex tasks like face identification/object recognition are extremely difficult. Therefore, it is necessary to investigate and apply image processing strategies for optimizing the visual perception of the recipients. This study focuses on recognition of the object of interest employing simulated prosthetic vision.MethodWe used a saliency segmentation method based on a biologically plausible graph-based visual saliency model and a grabCut-based self-adaptive-iterative optimization framework to automatically extract foreground objects. Based on this, two image processing strategies, Addition of Separate Pixelization and Background Pixel Shrink, were further utilized to enhance the extracted foreground objects.Resultsi) The results showed by verification of psychophysical experiments that under simulated prosthetic vision, both strategies had marked advantages over Direct Pixelization in terms of recognition accuracy and efficiency. ii) We also found that recognition performance under two strategies was tied to the segmentation results and was affected positively by the paired-interrelated objects in the scene.ConclusionThe use of the saliency segmentation method and image processing strategies can automatically extract and enhance foreground objects, and significantly improve object recognition performance towards recipients implanted a high-density implant. |
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| Keywords: | Visual prosthesis Simulated prosthetic vision Saliency segmentation Image processing strategy Objects recognition |
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