Logarithm table with mean difference pdf

In this paper, a novel illumination compensation method for face recognition under illumination variations is proposed. To estimate the illumination o

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In this paper, a novel illumination compensation method for face recognition under illumination variations is proposed. To estimate the illumination of every point more precisely, logarithm table with mean difference pdf mean operator is applied to refine the estimation. Experimental results on the CMU PIE database, the Yale Face database B and the Extended Yale Face database B demonstrate that the method is superior in comparison with other existing methods.

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Furthermore, a simplified version of the method is also proposed. Both theoretical analysis and experimental results demonstrate the validity and high computational efficiency of the simplified version. Performances of the proposed methods under different values of parameters are also discussed in the paper. No modeling step and prior information are required. Only one image per person is required for training.

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Coarse illumination compensation using DCT coefficients in local areas. Finer illumination estimation with a mean operator. Check if you have access through your login credentials or your institution. This common mistake confuses sufficient versus necessary conditions for the applicability of modern portfolio theory. Critically evaluates attacks on assumptions of modern portfolio theory. Argues attacks confuse sufficient versus necessary conditions for applying theory.

This paper is based on Chapter 2 of a book which the author is writing under the sponsorship of 1st Global of Dallas, TX. Batman, its President David Knoch, and Kenneth Blay, my principal day-to-day contact at 1st Global. This is a featured article. Click here for more information.