CMSC 733 Project
نویسنده
چکیده
To represent the detected corners in a compact way, we use the following procedures: (1) Smooth the entire image using 5 × 5 Gaussian kernel with standard deviation 1. (2) Padding the image with a margin 20. (3) For each corner response, we crop a 41× 41 region, and reshape it to a one-dimensional vector. (4) Normalize the vector to zero mean and unit standard deviation. If we directly match the features obtained in this way, the result would be the one shown in Fig. 2. We can observe some mismatch from the figure. To overcome this effect, we apply the RANSAC algorithm on those initial matches. We iteratively choose four pairs from those matches, and compute corresponding homography. The sum of squared distances (SSDs) are evaluated at the target image, for each feature point in the target image, if SSD is less than 1, it is chosen as an inlier. Finally, we accept the best homography with the most inliers. The final homography will be computed using those inliers. The result after refining those pairs is shown in Fig. 3
منابع مشابه
CMSC 733 Project 1 Report
This is a report of the Project 1: Myautopano. In this project, we implemented an end-to-end pipeline to do image panorama stitching of images. This report will describe the basic idea of the algorithm using in this project and the corresponding results.
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