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A n on-linear illuminant compensation case

ACCV 2007, Tokyo, Japan. Color Constancy via Convex Kernel Optimization Xiaotong Yuan, Stan Z. Li. and Ran He Center for Biometrics and Security Research & National Laboratory of Pattern Recognition Institute of Automation, Chinese Academy of Sciences. Introduction

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A n on-linear illuminant compensation case

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  1. ACCV 2007, Tokyo, Japan Color Constancy via Convex Kernel Optimization Xiaotong Yuan, Stan Z. Li. and Ran He Center for Biometrics and Security Research & National Laboratory of Pattern RecognitionInstitute of Automation, Chinese Academy of Sciences • Introduction • We address the problem of color constancy with linear render model. To efficiently estimate the render model parameters , we define a novel convex kernel based criteria function to measure the color compensation accuracy in a new scene. A well designed adaptive multiple modes-seeking framework is introduced to optimize the objective function. Advantages: • Initialization invariant. • Linear order time efficiency • Method Description • Kernel based objective function: • Local transformation vector seeking: Half-Quadratic optimization • Global transformation vector (GTV) seeking: annealed mean-shift [Shen, 2007] • Multiple GTV seeking algorithm (Ada-GTV): a sequential and adaptive mechanism • A non-linear illuminant compensation case Compensated image Color classification • Problem Formulation • Linear Render Model [Manduchi 2006] Prior weight images • More test results Training image: color surfaces under “canonical” illuminant Color transformation, linearly parameterized by vectors Gaussian distribution • Experiments • Initialization invariant property of our algorithm Test Image: with pixels , containing the color surfaces with illumination changes References [Manduchi 2006] R. Manduchi, “Learning Outdoor Color Classification”, TPAMI, Vol. 28, No. 11, pp.1713-1723, 2006 [Shen 2007] C. Shen, M. J. Brooks and A.V. D. Hengel, “Fast Global Kernel Density Mode Seeking: Applications to Localization and Tracking”, TIP, Vol. 16 No.5, pp. 1457-1469, 2007 By EM Optimization [Manduchi 2006] Problem:How to estimate possible existing transformation vectors?

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