Color reproduction method by support vector regression for color computer vision

Please use this identifier to cite or link to this item: https://ir.lib.ncu.edu.tw/handle/987654321/103165

Title:	Color reproduction method by support vector regression for color computer vision
Authors:	楊宗勳;Yang, Bo;Chou, Hung-Yu;Yang, Tsung-Hsun
Contributors:	理學院光電科學與工程學系
Keywords:	Color reproduction;Least mean squared validating errors;Successive 3σ filter;Support vector regression
Date:	2013-11-01
Issue Date:	2026-04-23 11:24:39 (UTC+8)
Publisher:	Urban und Fischer Verlag Jena;Elsevier GmbH
Abstract:	摘要： In the color computer vision system, the nonlinearity of the camera and computer screen may result in different colors between the screen and the actual color of objects, which requires for color calibration. In this paper, support vector regression (SVR) method was introduced to reproduce the colors of the nonlinear imaging system. Firstly, successive 3σ method was used to eliminate the large errors found in the color measurement. Then, based on the training set measured in advance, SVR model of RBF kernel was applied to map the nonlinear imaging system. In this step, two important parameters (C, γ) were optimized by the Least Mean Squared Validating Errors algorithm to get the best SVR model. Finally, this optimized model could predict the real values displayed on the screen. Compared with quadratic polynomial regression, BP neural network and relevance vector machine, the optimized SVR model has better ability in color reproduction performance and generalization. 出版者： Elsevier GmbH 出版日期： 2013-11 出處： Optik (Stuttgart), 2013-11, Vol.124 (22), p.5649-5656 資源來源： Elsevier ScienceDirect Journals 版權： 2013 Elsevier GmbH 識別號： ISSN: 0030-4026 識別號： EISSN: 1618-1336 識別號： DOI: 10.1016/j.ijleo.2013.04.036
Appears in Collections:	[Department of Optics and Photonics] journal & Dissertation

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