%0 Generic %1 drmanishkumarsaini2013signal %A Dr. Manish Kumar Saini, Deepak Sindhu %B 2013 Mobile Communication - II %D 2013 %E Shankaranarayanan, Dr. %I ACEEE (A Computer division of IDES) %K Edge detection image processing %T Signal Processing, Statistical and Learning Machine Techniques for Edge Detection %U http://searchdl.org/public/book_series/LSCS/3/27.pdf %X A review of published articles on edge detection is given in this paper. It includes some definitions and different methods of edge detection in different classes. The relation between different classes is given in this review and also gives some calculations about their performance and application. The edge detection methods are the combination of image differentiation, image smoothing and a post processing for edge labelling. A filter is used for image smoothing, due to which noise is reduced, numerical calculation is regularized, and to improve the accuracy and the reliability, it provide a parametric representation that works as mathematical microscope, that will examine it in different scales. To represent the strength and position of edges and their orientation, the image differentiation gives information of intensity transition in the image. To inhibit the false edges, produce a uniform contour of objects, and associate the expanded ones, the edge labelling calls for post processing.

@conference{drmanishkumarsaini2013signal, abstract = {A review of published articles on edge detection is given in this paper. It includes some definitions and different methods of edge detection in different classes. The relation between different classes is given in this review and also gives some calculations about their performance and application. The edge detection methods are the combination of image differentiation, image smoothing and a post processing for edge labelling. A filter is used for image smoothing, due to which noise is reduced, numerical calculation is regularized, and to improve the accuracy and the reliability, it provide a parametric representation that works as mathematical microscope, that will examine it in different scales. To represent the strength and position of edges and their orientation, the image differentiation gives information of intensity transition in the image. To inhibit the false edges, produce a uniform contour of objects, and associate the expanded ones, the edge labelling calls for post processing.}, added-at = {2014-02-05T06:03:59.000+0100}, author = {Dr. Manish Kumar Saini, Deepak Sindhu}, biburl = {https://www.bibsonomy.org/bibtex/29d3e3a58f58dc33787b68b8fb58c3f10/idescitation}, booktitle = {2013 Mobile Communication - II}, editor = {Shankaranarayanan, Dr.}, interhash = {3c1882dc1378b303d5c6ef71a033abb8}, intrahash = {9d3e3a58f58dc33787b68b8fb58c3f10}, keywords = {Edge detection image processing}, organization = {Institute of Doctors Engineers and Scientists}, publisher = {ACEEE (A Computer division of IDES)}, timestamp = {2014-02-05T06:03:59.000+0100}, title = {Signal Processing, Statistical and Learning Machine Techniques for Edge Detection}, url = {http://searchdl.org/public/book_series/LSCS/3/27.pdf}, year = 2013 }