Yao-Hsiang Yang

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Natural images are known to carry several distinct properties which are not shared with randomly generated images. In this article we utilize the scale invariant property of natural images to construct a filter which extracts features invariant to illumination conditions. In contrast to most of the existing methods which assume that such features lie in(More)
We propose a Bayesian framework of Gaussian process in order to extend Fisher’s discriminant to classify functional data such as spectra and images. The probability structure for our extended Fisher’s discriminant is explicitly formulated, and we utilize the smoothness assumptions of functional data as prior probabilities. Existing methods which directly(More)
This paper introduces a general framework for image contrast enhancement based on histogram equalization (HE) and specification (HS). Traditional HE and HS are simple and effective, but they often amplify the noise level of the image while enhancing it. Furthermore, they may not utilize the entire dynamic range due to the discrete nature of the image. In(More)
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