Using Epipolar Geometry Applying the Bit- Plane on Images Using Mat Lab

In this paper we show some results on Epipolar Geometry applying on various images using MATLAB. The Fundamental matrix is give to the depth information on the images we get the critical view of images that can be transfer in to the Grayscale image in to binary image. Matlab is an ideal tool for simulating digital communication system, its gates easy scripting language and excellent data visualization capabilities. Images breaking them up in to their bitplanes images are collecting their pixel values the grey value of each image of an 8bit image an 8bit binary word. The binary image will have only given the two values 0 and 1 and hence the image shows best clarity results is fine. The Epipolar geometry is a key point in computer vision and the fundamental matrix estimation, computer vision and the fundamental matrix estimation is the unique way to compute it. Key wordsEpipolar Geometry, Fundamental Matrix, Matlab and Robust methods etc. Advances in Computational Research ISSN: 0975-3273 & E-ISSN: 0975-9085, Volume 4, Issue 1, 2012 Introduction Epipolar geometry estimates the fundamental matrix have been proposed which can be classified into the linear methods and iterative methods. They deal with bad point localization due to noise in image segmentation and robust techniques that eliminate the outliers due to false matching. Epipolar geometry allows us to clarify what information is needed in order to perform the search for corresponding elements only along image lines. The practical importance of Epipolar geometry is based on the fact that the Epipolar plane intersects each image in a line called Epipolar line. We are applying bit plane on image and those image can be changed his bit point with the best clear results. Grayscale image can be transformed into a sequence of binary images by breaking them up into their bit-planes. If we consider the grey value of each image pixel on an 8-bit as an 8-bit binary word, then those the images 0th bit plane consists of the last bit of each grey value. Hence the image has least effect (least significant bit plane). The 7th bit plane consists of the first bit in each value; it is called the most significant bit, and the plane consisting of those bits. If we take a gray scale image, we start by making it a matrix of type double, this means we can perform asthmatic on the values. Example: >>c=imread (‘xyz.jpg’); >>cd =double©; We now isolate the bit planes by simply dividing the matrix cd by successive power of 2, throwing away the remainder and seeing if the image bit is 0 or 1. We can apply some mod function on that images and then getting the result of the image with the grey scale. Comparison of Epipolar lines Epipolar lines serves as qualitative check of the correctness of our result. Following figures show the method of using Matlab can be comparison to the applying with geometry of the images. The grayscale image will be getting most clarity of picture. We show some example of the quality of the fundamental matrix from the vision of images. The Epipolar lines should be passing exactly Citation: Shaikh A.J., Kureshi Anis and Manza R.R. (2012) Using Epipolar Geometry Applying the BitPlane on Images Using Mat Lab. Advances in Computational Research, ISSN: 0975-3273 & E-ISSN: 0975-9085, Volume 4, Issue 1, pp.-34-37. Copyright: Copyright©2012 Shaikh A.J., et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.