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 photograph and photographs 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 components of photo processing know-how, photograph research und knowing, machine imaginative and prescient and development attractiveness, sizeable facts mining, special effects and VR, photo know-how application.
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Additional resources for Advances in Image and Graphics Technologies: 10th Chinese Conference, IGTA 2015, Beijing, China, June 19-20, 2015, Proceedings
IEEE Trans Pattern Analysis and Machine Intelligence 18(12), 2037–2041 (2006) 36 Z. Xie 6. : Multi-resolution, Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns. IEEE Trans on Pattern Analysis and Machine Intelligence 24(7), 971–987 (2002) 7. : Histogram of Gabor Phase Patterns (HGPP): A Novel Object Representation Approach for Face Recognition. IEEE Trans on Image Processing 16(1), 57–68 (2007) 8. : Face Recognition with salient local gradient orientation binary pattern.
However, the hue and saturation of background pixels are almost changeless. Thus, we can compute color-difference in Lab color space and set an appropriate threshold, and then the probability of judging background pixels to be foreground points can be decreased. Vibe Motion Target Detection Algorithm Based on Lab Color Space 49 Many motion detection algorithms are sensitive to object shadow, and Vibe algorithm is no exception. Brightness of moving object shadow is darker than that of actual background located at the shadow, but the hue and saturation are almost changeless.
Filtering the original image with different filters corresponding to five scales and eight orientations to get Gabor representation images. 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.