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This paper presents a novel framework for Content Based Image Retrieval(CBIR), which combines color, texture and spatial structure of image. The proposed method uses color, texture and spatial structure descriptors to form a feature vector. Images are segmented into regions to extract local color, texture and CENTRIST(CENsus Transform hISTogram) features respectively. Multiple-instance learning (MIL)...
This paper presents a new face recognition method based on the analysis of local features. Firstly, we can get the images of magnitude by means of analyzing face images with the Gabor wavelets. Secondly, the magnitude images are divided into blocks, then principle components analysis (PCA) could be directly used to all the blocks to construct the feature space. Finally, all the blocks of images are...
A crucial preprocessing stage in applications such as OCR is text extraction from mixed-type documents. The present work, in contrast to most until now, successfully faces the problem of varying text orientation and size. The technique first identifies marks using a contour following technique, followed by a PCA (principal component analyzer) which determines the direction of the main axis of each...
This paper describes new methods for handwritten Arabic character recognition. We propose a novel algorithm for smoothing image and segmentation of the Arabic character using width writing estimated from skeleton character. The moments and Fourier descriptor of profile projection and centroid distance are used as features of each character these feature are invariant in translation , rotation and...
Gait recognition has recently gained significant attention from researchers, especially computer vision researchers. Compared with other biometrics, gait has its unique advantages. Other biometrics technologies, such as face recognition, hand recognition, fingerprint recognition, can't work effectively when the person is far away. A simple and efficient gait recognition approach based on multi-resolution...
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