Contrast Compensation for Back-lit and Front-lit Color Face Image via Fuzzy Logic Classification and Image Illumination Analysis

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题名:	Contrast Compensation for Back-lit and Front-lit Color Face Image via Fuzzy Logic Classification and Image Illumination Analysis
作者:	Tsai, C. M.;蔡俊明;Yeh, Z. M.;Wang, Y. F.
贡献者:	臺北市立教育大學資訊科學系
关键词:	Contrast compensation;Fuzzy logic classification;Image illumination analysis;Parameter-free;Color face images
日期:	2008
上传时间:	2017-07-25 09:57:37 (UTC+8)
摘要:	Conventional contrast enhancement methods have two shortcomings. First, most of them do not produce satisfactory enhancement results for face images with back-lit or front-lit. Second, most of them need transformation functions and parameters which are specified manually. Thus, this paper proposes an automatic and parameter-free contrast compensation algorithm for color face images. This method includes: RGB color space is transformed to YIQ color space. Fuzzy logic is used to classify the color images into back-lit, normal-lit, and front-lit categories. Image illumination analysis is used to analyze the image distribution. The input image is compensated by piecewise linear based compensation method. Finally, the compensation image is transformed back to RGB color space. This novel compensation method is automatic and parameter-free. Our experiments included back-lit and front-lit images. Experiment results show that the performance of the proposed method is better than other available methods in visual perception measurements.
显示于类别:	[資訊科學系] 會議論文

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