By Sudipta Roy, Nidul Sinha, Asoke Kr. Sen (auth.), Dhinaharan Nagamalai, Eric Renault, Murugan Dhanuskodi (eds.)

This booklet constitutes the refereed lawsuits of the 1st foreign convention on electronic picture Processing and trend acceptance, DPPR 2011, held in Tirunelveli, India, in September 2011. The forty eight revised complete papers have been conscientiously reviewed and chosen from approximately four hundred submissions. The convention introduced jointly prime researchers, engineers and scientists within the area of electronic snapshot Processing and trend popularity. The papers disguise all theoretical and useful elements of the sector and current new advances and present learn ends up in tracks, particularly: electronic picture processing and trend attractiveness, and laptop technological know-how, engineering and data technology.

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Extra resources for Advances in Digital Image Processing and Information Technology: First International Conference on Digital Image Processing and Pattern Recognition, DPPR 2011, Tirunelveli, Tamil Nadu, India, September 23-25, 2011. Proceedings

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Depending on geological deformations on the surface, there is a high possibility that craters form degraded craters rather than circles on the surface. In this paper circular hough transform is used for crater detection. Before crater detection, removal of shadows present in the image is done. Which uses the MSR to enhance the color images can achieve adequate enhancement of shadowed detail and fails to rendition. The good color rendition and more details in shadows derived from the source image and the enhanced image can be combined into a fused image by DWT.

Extraction of these features from lunar image by a human expert is a difficult task and D. Nagamalai, E. Renault, and M. ): DPPR 2011, CCIS 205, pp. 34–43, 2011. , it depends on illumination, surface properties and atmospheric state), the features that are present in the images can be barely visible due to erosion and the shadows present in the images, and uneven illumination, and the represented objects are not well defined. Therefore, it is highly desirable procedure to detect the position, structure, and dimension of each feature is a trustworthy automatic procedure.

Then, the controlling processor triggers the camera to capture the image of the speeding vehicle according to the speed regulations. The vehicle detectors could be categorized into two types: 1) hardware-based detectors and 2) software-based detectors. The former is based on the electromagnetic principles and requires a dedicated hardware. The latter, which is adopted in this paper, performs vehicle detection using complex Image processing techniques over vehicle detectors can be categorized into four main groups: 1) inductive loop detector; 2) laser detector;3)optical detector; and 4) weight detector.

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