By Tieniu Tan, Qiuqi Ruan, Shengjin Wang, Huimin Ma, Kaichang Di

This booklet constitutes the refereed lawsuits of the tenth chinese language convention on Advances in snapshot and photos applied sciences, IGTA 2015, held in Beijing, China, in June 2015. The 50 papers awarded have been rigorously reviewed and chosen from 138 submissions. they supply a discussion board for sharing new facets of the progresses within the parts of picture processing expertise, photo research und realizing, machine imaginative and prescient and development reputation, substantial information mining, special effects and VR, snapshot know-how application.

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Experiments show that the improved color-difference formula is less sensitive to illumination change and object shadow. Then, the algorithm makes use of space consistency of pixels to correct pixel classification results, which improves the anti-noise ability and makes extracted movement area more complete. Experimental results demonstrate that the algorithm proposed has better detection effects both in indoor and outdoor environments. Keywords: Motion target detection · Visual background extractor (Vibe) · Lab color space · Color-difference formula 1 Introduction In video content analyses and multimedia retrieval, extracting movement area from a video sequence completely and exactly, is a basic and critical task [1].

Single Training Sample Face Recognition Based on Gabor and 2DPCA 41 2. Down sampling each Gabor images to 16×16 resolution and normalizing it to zero mean and unit variance. 3. Splicing the forty Gabor representation images to form a big global image by scales variety with row and orientations variety with column. It denoted by: G2 D 4. Collecting each G2 D  F0,0  =  F4,7    F0,7    F4,7  (9) image of every face to form new images training set. 8. 5. Projecting the G2 D image to the subspace spanned by the eigenvectors to get the ultimate feature denoted as FG 2 DPCA .

Then the final Gabor feature of the image is defined by: (5) G = { F0 ,0 ,  , F0,7 ,  , F4,7 ,  , F4 ,7 } It can be seen that the conventional Gabor feature is a high dimension vector. , X N } , where N is the num- X i ∈ R m×n is a face image. Projecting the training sam- a will yield a row vector. The process can be denoted by: yi = X ia (6) The 2DPCA algorithm tries to find the optimal vector such that the scatter of samples in projected space reaches maximum. The aim can be denoted by: 40 J.

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