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Characterization of high-level images features for surface gloss perception

机译:表面光泽感知的高级图像特征的特征

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摘要

Effective cues for surface gloss perception reside in low-level image statistics, including skewness of luminance or subband histogram (Motoyoshi, Nishida, Sharan & Adelson, 2007). It is also suggested that gloss perception is modulated by high-level spatial information such as consistency between patterns of specular highlight and diffuse shading (Anderson & Kim, 2009). To gain further insight into the nature and the processing mechanism of high-level gloss features, we examined the effects of highlight consistency, peripheral viewing and texture synthesis on gloss perception. The stimulus we used was a surface modulated in depth by a low-pass-filtered random field and rendered by the Ward illumination model. We made a highlight-consistent image by combining specular and diffuse patterns made from the same depth profile, and a highlight inconsistent image by combining specular and diffuse patterns made from different uncorrelated depth profiles. Using the algorithm by Portilla & Simoncelli (2000), we also synthesized two types of texture image each sharing low-level image statistical measurements with the highlight-consistent and highlight-inconsistent surface images, respectively. We used a magnitude rating, and a pairwise magnitude comparison, to evaluate gloss perception for these images. The results indicate that highlight-consistent surfaces looked much more glossy than highlight-inconsistent surfaces when viewed in the fovea, but this difference was reduced as the retinal eccentricity was increased. Synthesized textures did not look glossy at any eccentricity, and it was hard to discriminate highlight-consistent textures from inconsistent ones. Our findings suggest that critical image features that produce the apparent gloss difference between highlight-consistent and inconsistent images are available to human observers mainly through foveal vision, and that these features cannot be captured by the image statistics preserved by the Portilla & Simoncelli algorithm.
机译:表面光泽感知的有效提示位于低级图像统计数据,包括亮度或子带直方图的偏差(Motoyoshi,Nishida,Sharan&Adelson,2007)。还建议通过高级空间信息调制光泽感知,例如镜面突出显示和漫射着散示模式之间的一致性(Anderson&Kim,2009)。为了进一步了解高级光泽特征的性质和加工机制,我们研究了突出一致性,外围观察和纹理合成对光泽感知的影响。我们使用的刺激是通过低通滤波的随机场进行深度调制的表面,并通过病房照明模型呈现。我们通过组合由相同深度分布组合的镜面和漫射模式来制造突出显示的图像,并且通过组合由不同不相关的深度轮廓制成的镜面和漫射模式来突出显示不一致图像。使用Portilla和Simoncelli(2000)的算法,我们还通过突出显示和突出显示不一致的表面图像来合成两种类型的纹理图像,每个纹理图像分别共享低级图像统计测量。我们使用了一个幅度额定值和成对幅度比较,以评估这些图像的光泽感知。结果表明,在FOVEA观看时,突出的一致表面看起来比突出 - 不一致的表面更有光泽,但随着视网膜偏心率的增加,这种差异减少了。合成的纹理在任何偏心度下看起来都没有光泽,并且很难区分突出的一致纹理来自不一致的纹理。我们的研究结果表明,在人体观察者中,可以主要通过心脏视觉来提供突出显示一致和不一致图像之间表观光泽差异的关键图像特征,并且通过Portilla和Simoncelli算法保留的图像统计,不能捕获这些功能。

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