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A Novel Technique For Edge Detection Using Logical Operations
ISBN-13: 9783659410789 / Angielski / Miękka / 2013 / 72 str.
A Novel Technique For Edge Detection Using Logical Operations
ISBN-13: 9783659410789 / Angielski / Miękka / 2013 / 72 str.
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In today's world digital images play a very significant role. It has a number of applications such as televisions, astronomy, medical research, defense, geographical research etc. Processing of an image means operations done on an image by the computer. An image consists of array of small elements, called pixels which are stored in the digital memory of the computer The book contains the work carried out as a part of research at Master's Degree level. A new technique for edge detection has been proposed in this book. For segmentation and identification of objects in an image edges are found. Edge detection gives generalization of image data. Logical operations give a good mathematical framework to deal with such uncertain information. Experimental results for both synthetic and real world images show that the accuracy of the proposed method. The proposed procedure can be applied to different images which run in polynomial time and give better results if Gaussian noise gets added too. This algorithm can be applied to variety of colored images.
In todays world digital images play a very significant role. It has a number of applications such as televisions, astronomy, medical research, defense, geographical research etc. Processing of an image means operations done on an image by the computer. An image consists of array of small elements, called pixels which are stored in the digital memory of the computer The book contains the work carried out as a part of research at Masters Degree level. A new technique for edge detection has been proposed in this book. For segmentation and identification of objects in an image edges are found. Edge detection gives generalization of image data. Logical operations give a good mathematical framework to deal with such uncertain information. Experimental results for both synthetic and real world images show that the accuracy of the proposed method. The proposed procedure can be applied to different images which run in polynomial time and give better results if Gaussian noise gets added too. This algorithm can be applied to variety of colored images.
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