Current Issue : July - September Volume : 2013 Issue Number : 3 Articles : 6 Articles
Generally, we are facing the problem when we want to capture the same thing what we seen. But in the real life, the range of the image capturing device is smaller than the scene which is sense by eye. Even when try made for image capturing in single exposure it may result in image in which small data is not available or it is available then also they are blurred or it is also possible that we loss some part of information. In such a problem the only situation can be that get the different spilt images of same object and then stitch it that is mosaiced it. If there is not very careful photography some part is missed in rectangle image and this mosaiced image may result with black patches, which omit aim of mosaicing to merge image seamlessly. So the possible approach is to fill this missing part with the help of image completion concept which is image inpainting. The proposed methodology gives a analytical approach is to generate the image in such a way that the person who is not familiar with that place or object will consider it as original image. The aim of proposed methodology is to remove artefacts of the image mosaicing....
A lane detection technique has become a interesting research topic in the field of vehicular electronics and in Intelligent Transportation System for driver safety. This paper presents a system for road lane detection using a test video recorded at Highway . The video is input to the system and after processing gives the lane detected video. The lanes are detected by the use of edge detection and Hough transform and fitted to the Kalman filter for tracking and smoothing. The propose algorithm can be used to detect the lane on road....
In this article, a high performance face recognition system based on local binary pattern (LBP) using the probability\r\ndistribution functions (PDFs) of pixels in different mutually independent color channels which are robust to frontal\r\nhomogenous illumination and planer rotation is proposed. The illumination of faces is enhanced by using the stateof-\r\nthe-art technique which is using discrete wavelet transform and singular value decomposition. After equalization,\r\nface images are segmented by using local successive mean quantization transform followed by skin color-based\r\nface detection system. Kullbackââ?¬â??Leibler distance between the concatenated PDFs of a given face obtained by LBP\r\nand the concatenated PDFs of each face in the database is used as a metric in the recognition process. Various\r\ndecision fusion techniques have been used in order to improve the recognition rate. The proposed system has\r\nbeen tested on the FERET, HP, and Bosphorus face databases. The proposed system is compared with conventional\r\nand the state-of-the-art techniques. The recognition rates obtained using FVF approach for FERET database is\r\n99.78% compared with 79.60 and 68.80% for conventional gray-scale LBP and principle component analysis-based\r\nface recognition techniques, respectively....
We propose a novel image segmentation algorithm using piecewise smooth (PS) approximation to image. The\r\nproposed algorithm is inspired by four well-known active contour models, i.e., Chan and Vese� piecewise constant\r\n(PC)/smooth models, the region-scalable fitting model, and the local image fitting model. The four models share\r\nthe same algorithm structure to find a PC/smooth approximation to the original image; the main difference is how\r\nto define the energy functional to be minimized and the PC/smooth function. In this article, pursuing the same\r\nidea we introduce different energy functional and PS function to search for the optimal PS approximation of the\r\noriginal image. The initial function with our model can be chosen as a constant function, which implies that the\r\nproposed algorithm is robust to initialization or even free of manual initialization. Experiments show that the\r\nproposed algorithm is very appropriate for a wider range of images, including images with intensity inhomogeneity\r\nand infrared ship images with low contrast and complex background....
Although many lung disease diagnostic procedures can benefit from computer-aided detection (CAD), current CAD\r\nsystems are mainly designed for lung nodule detection. In this article, we focus on tuberculosis (TB) cavity\r\ndetection because of its highly infectious nature. Infectious TB, such as adult-type pulmonary TB (APTB) and\r\nHIV-related TB, continues to be a public health problem of global proportion, especially in the developing countries.\r\nCavities in the upper lung zone provide a useful cue to radiologists for potential infectious TB. However, the\r\nsuperimposed anatomical structures in the lung field hinder effective identification of these cavities. In order to\r\naddress the deficiency of existing computer-aided TB cavity detection methods, we propose an efficient\r\ncoarse-to-fine dual scale technique for cavity detection in chest radiographs. Gaussian-based matching, local binary\r\npattern, and gradient orientation features are applied at the coarse scale, while circularity, gradient inverse\r\ncoefficient of variation and Kullbackââ?¬â??Leibler divergence measures are applied at the fine scale. Experimental results\r\ndemonstrate that the proposed technique outperforms other existing techniques with respect to true cavity\r\ndetection rate and segmentation accuracy....
In this article, we propose a new method for localizing optic disc in retinal images. Localizing the optic disc and its\r\ncenter is the first step of most vessel segmentation, disease diagnostic, and retinal recognition algorithms. We use\r\noptic disc of the first four retinal images in DRIVE dataset to extract the histograms of each color component. Then,\r\nwe calculate the average of histograms for each color as template for localizing the center of optic disc. The DRIVE,\r\nSTARE, and a local dataset including 273 retinal images are used to evaluate the proposed algorithm. The success\r\nrate was 100, 91.36, and 98.9%, respectively....
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