NDLI: Image processing using a saliency map for a 49-channel retinal prosthesis

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Image processing using a saliency map for a 49-channel retinal prosthesis

Content Provider	Semantic Scholar
Author	Kanda, Hiroyuki Kanda, Takaomi Nagai, Yukie Fujikado, Takashi
Copyright Year	2015
Abstract	Purpose: Our second-generation device as a suprachoroidaltransretinal stimulatoin (STS) comprises a 49-channel active electrode array. To achieve effective prosthetic vision using a limited number of electrodes, it is necessary to develop an optimum image processing strategy. In recent years, a computational model “saliency map” (Itti et al. 1998) that can detect an outstanding location using primitive features has been proposed. The purpose of this study is to evaluate the feasibility of a saliency map for image processing in a 49-pixel retinal prosthesis. Methods: We constructed a retinal prosthesis simulator system and compared the eyehand coordination in two different algorithms: a saliency map and a conventional model (brightness map). The simulator system comprised a head-mounted camera, a laptop computer, and a head-mounted display (HMD). Images were captured using the head-mounted camera and converted into 7 × 7-pixel images for projection onto HMD. We recruited five healthy volunteers aged 21–38 years. All subjects wore the simulator and took the localization test. For the localization test, the subjects were instructed to point at the center of the visual target on a touch panel display, placed 40 cm from the subject. The distances from the target center to the position where the subject pointed were recorded as error distances. The target and the background color combinations were randomly changed (black/white, white/black, green/red, and red/green). The localization test trials were repeated 20 times for each condition.
Starting Page	4783
Ending Page	4783
Page Count	1
File Format	PDF HTM / HTML
Volume Number	56
Alternate Webpage(s)	http://www.er.ams.eng.osaka-u.ac.jp/Paper/2015/Nagai15n.pdf
Language	English
Access Restriction	Open
Content Type	Text
Resource Type	Article

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