By Sudipta Roy, Nidul Sinha, Asoke Kr. Sen (auth.), Dhinaharan Nagamalai, Eric Renault, Murugan Dhanuskodi (eds.)
This e-book constitutes the refereed court cases 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 rigorously reviewed and chosen from approximately four hundred submissions. The convention introduced jointly major researchers, engineers and scientists within the area of electronic photo Processing and development reputation. The papers conceal all theoretical and sensible points of the sector and current new advances and present examine ends up in tracks, particularly: electronic snapshot processing and trend attractiveness, and laptop technology, engineering and knowledge technology.
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Extra info 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
Still other algorithms are used for identification of craters. However, the results presented were far from being good enough to convince the deep space exploration research group to adopt many of these approaches. Multiple boundary based approaches and merged to obtain the results . In the first approach they have used images that are classified considering illumination. 36 V. Vani When shady and illuminated pattern is recognized, they fit a circle to the surrounding edges. Although abrupt brightness changes may reveal a lot about the surface under consideration, the presence of sensor data with correct illumination is often an unrealistic assumption to make.
The Euclidean, Bhattacharya and Mahalanobis distances are used and given in equations (1) to (3) where q is the query image and t is the target image , . D Euc (q i , t i ) = DB q, t n i =1 ∑ (q i − t i ) 2 ∑ (1) ln | ∑ ∑ / | |∑ ||∑ | (2) DMaha (q,t) = (μq −μt )T −1(μq −μt ) (3) where µ is the mean and ∑ is the covariance matrix. 4 Multi Class Classification Using SVM-Pair Wise Coupling Multi class classification means assigning a class from one of the k predefined classes for each image under consideration.
After the source image is enhanced by MSR, the enhanced image and the source image are fused into a fused image. The fused image has more information and its color is more adapt to the human visual system The circular Hough transform with shadow removalis able to automatically select a small set of features characterizing thepresence/absence of craters in an image. However, by increasing radius range large size craters can also be identified. The further work should improve the choice method of seed and use some intelligent methods to decide the size of crater candidate area, and plan to evaluate the performance of this algorithm on other terrains of Mars and also on other planetary surfaces.