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Image Recognition With a Limited Number of Pixels for Visual Prostheses Design
Author(s) -
Li Sheng,
Hu Jie,
Chai Xinyu,
Peng Yinghong
Publication year - 2012
Publication title -
artificial organs
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.684
H-Index - 76
eISSN - 1525-1594
pISSN - 0160-564X
DOI - 10.1111/j.1525-1594.2011.01347.x
Subject(s) - artificial intelligence , pixel , computer vision , computer science , pattern recognition (psychology) , cognitive neuroscience of visual object recognition , image processing , visual prosthesis , image resolution , image (mathematics) , object (grammar) , stimulation , medicine
With the rapid development and crossover among the information science, microelectronics, material science, and biomedical disciplines, the visual prosthesis makes visual reparation possible. Because the number of stimulation electrodes is strictly limited by various complicated factors, it is necessary to determine the minimum visual requirements to achieve useful artificial vision for image recognition. This research has studied how many pixels individual images need to have to be correctly and economically recognized by blind subjects. In order to extract the figure of the image with a limited number of pixels, we have proposed a wavelet‐based image processing methods, and six resolutions (8 × 8, 16 × 16, 24 × 24, 32 × 32, 48 × 48, and 64 × 64) are investigated. Psychophysical experiments have been designed to verify our proposed image processing method and to investigate the recognition accuracy with a limited number of pixels. The results show that the recognition accuracy increases with the number of pixels. The recognition accuracy varied with tested images, when a resolution of 24 × 24 was used: six of the eight image objects were recognized with an accuracy of >50%, and the remaining two of the eight image objects were recognized with an accuracy of <50%. Moreover, when the resolution is more than 32 × 32, the increase of the recognition accuracy is no longer obvious. We also have investigated the impact of different perspectives of the same object to the recognition accuracy. The experiment shows that providing multiview image sequences, subjects can receive more visual information to obtain higher recognition accuracy.