Photometric Linearization under Near Point Light Sources

Satoshi Sato, K. Takata, K. Nobori
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引用次数: 3

Abstract

We present a method for classifying image pixels of real images into multiple photometric factors: specular reflection, diffuse reflection, attached shadows and cast shadows. Conventional photometric linearization methods cannot correctly classify pixels under near point light sources, since they assume parallel light. To satisfy this assumption, our method utilizes a photometric linearization method that divides images into small regions. It also propagates linearization coefficients from neighboring regions. Our experimental results show that the proposed method can correctly classify image pixels into photometric factors, even if images are obtained under near point light sources.
近点光源下的光度线性化
我们提出了一种将真实图像的图像像素分类为多个光度因子的方法:镜面反射、漫反射、附加阴影和投射阴影。传统的光度线性化方法在近点光源下不能正确分类像素,因为它们假设平行光。为了满足这一假设,我们的方法利用光度线性化方法将图像划分为小区域。它还从邻近区域传播线性化系数。实验结果表明,即使是在近点光源下获得的图像,该方法也能正确地将图像像素划分为光度因子。
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