Image RectificationThe images for later process have to be rectified, in other words, the corresponding points from the two images are located on the same rows. In our coursework, the images provided are already taken by moving the camera horizontally, therefore assuring the images been rectified already. But just in case, we can still implement this algorithm in our course work to guarantee the result is decent.Proposal代写Dense Corresponding Matching:In Dense correspondence matching, dense correspondence is established at the pixel level in the two images. We first calculate the SIFT images by calculating a SIFT descriptor for each pixel. If scale maps are enabled, then the scale for the SIFT descriptor will be taken from the image’s scale map. Then we will make a 2-sided SIFT-flow for the selected image pairs. Apply geometric filtering. Using RANSAC on the matches of each image pair to find the corresponding Fundamental Matrix.Disparity Map: Proposal代写To calculate the disparity map between two stereo images, we first load the images and convert them into greyscale. Then, we can use stereo anaglyph, use red-scan stereo glasses to view images in 3D. Then compute the disparity map using the disparity function I found in MATLAB. The algorithm for calculating the disparity map chosen is ‘Semi-Global Block Matching’. The algorithm would compute the disparity by comparing the sum of absolute difference of each block of pixels in the images. Then, it would force the similar disparity on the neighboring blocks. As compared to another algorithm called BlockMatching, this algorithm will return a more complete result in disparity estimate. As for a better illustration of the disparity map result, we can display disparity range for the ‘imshow’ in MATLAB.Proposal代写Proposal代写更多其他： 作业加急 作业帮助 加拿大代写 北美cs代写 北美代写 北美作业代写 数据分析代写  assignment代写 C++代写 CS代写合作平台：315代写 315代写  写手招聘 Essay代写

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