Dejun Wang

TL;DR: In this paper, a facial image is separated into sub-regions, and each sub-region makes individual contribution for performing facial recognition, where each subregion is subjected to a subregion based adaptive gamma (SadaGamma) correction or sub region based histogram equalization (SHE).

TL;DR: In this article, the authors present a global localization method that selects one from a plurality of samples and shifts the selected sample according to a movement of a robot, generating a new sample within a predetermined range of the shifted sample, determining either the shifted samples or the new samples as a next sample at a next time step, repeating for all the samples, and estimating a next position of the robot according to positions of the next samples when the number of next samples is equal to or larger than the maximum number of samples.

TL;DR: A face detection method and apparatus using a level-set method was proposed in this article, where a first initial shape model over a face image, evolving shape curves and a modified shape model rendered using the detected first through M-th neighboring boundary points over the face image were used.

TL;DR: An apparatus for and method of performing a most informative feature extraction (MIFE) method in which a facial image is separated into sub-regions, and each sub-region makes individual contribution for performing facial recognition is presented in this article.

TL;DR: In this article, the authors give the proof that in a neighborhood of the initial zero level set, there exists a unique solution, which must be the signed distance junction with respect to the evolving surface.