Binary Morphology Operator To Extract Binary Edge Of An Image
A. Pushpa Latha1,P. Krishna Chaithanya2
1Arepalli Pushpa Latha, Computational Engineering in ECE, RGUKT, IIIT, Nuzvid, (A.P) India.
2Pallapolu Krishna Chaitanya, Computational Engineering in ECE, RGUKT, IIIT, (A.P) India.
Manuscript received on July 23, 2013. | Revised Manuscript received on August 10, 2013. | Manuscript published on August 30, 2013. | PP: 26-30 | Volume-2, Issue-6, August 2013.  | Retrieval Number: F1940082613/2013©BEIESP

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Abstract: Mathematical morphology (MM) is a theory and technique for the analysis and processing of geometrical structures, based on set theory, lattice theory, topology, and random functions. MM is most commonly applied to digital images, but it can be employed as well on graphs, surface meshes, solids, and many other spatial structures. If this mathematical morphology is applied to the binary image or itself a gray scale image then that is called the binary morphology. Digital image Processing is one of the basic and important tool in the image processing and computer vision. In this paper we discuss about the extraction of a digital image edge using different digital image processing techniques. Edge detection is the most common technique for detecting discontinuities in intensity values. The input image or actual image may have some noise that may cause the quality of the digital image. Firstly, wavelet transform is used to remove noises from the image collected. Secondly, some edge detection operators such as Differential edge detection, Log edge detection, canny edge detection and Binary morphology are analyzed. And then according to the simulation results, the advantages and disadvantages of these edge detection operators are compared. It is shown that the Binary morphology operator can obtain better edge feature. Finally, in order to gain clear and integral image profile, the method of ordering closed is given. After experimentation, edge detection method proposed in this paper is feasible.
Keywords: Digital Image Edge detection, wavelet de-noising, differential operators, and binary morphology.