Collect the coordinates of all the inliers and the corresponding points in image 2, giving you pair. Here, computed using the apply_homography function given to you. If the distance (or squared distance) between the matching point in image 2 and the estimated point is below some threshold, then we call it an inlier. Apply the homography to the coordinates of each point from image 1.Do not use OpenCV’s cv2.findHomography function, because RANSAC is included in its implementation. Use the function est_homography that is provided to you. Select the coordinates (not matrix indices) of four feature pairs (at random), from image 1, from image 2. To remove incorrect matches, we will use a robust method called Random Sampling Consensus or RANSAC to compute the homography. We now have matched all the features correspondences but not all matches will be right. You can use the drawMatches function provided in the startup code, which is simply a wrapper around cv2.drawMatches, to visualize the corresponding features. You will be left with only the confident feature correspondences and these points will be used to estimate the transformation between the 2 images also called Homography. Take the ratio of best match (lowest distance) to the second best match (second lowest distance) and if this is below some ratio keep the matched pair otherwise reject it. Pick a point in image 1, compute the distance (or squared distance) between all points in image 2. In computer vision terms, this step is referred to as finding feature correspondences within the 2 images. Now, you want to match the feature points in the two images so you can stitch the images together. In the previous step, you encoded each key point by a feature vector. Standardization is used to remove bias and some illumination effect. Standardize this vector to have zero mean and variance of 1 (This can be done by subtracting all values by mean and then dividing by the standard deviation of all the components). Now, sub-sample the blurred output (this reduces the dimension) to using a spacing of pixels. Now apply Gaussian blur (feel free to play around with the parameters, check out the image filtering section of OpenCV’s documentation). In essence, you will get a lot more corners than you should! ANMS will try to find corners which are local maxima. However, when you take a real image, the corner is never perfectly sharp, each corner might get a lot of hits out of the corners-we want to choose only the best corners after ANMS. the “strong” corners, using the function imregionalmax provided in the startup code. You can find the local maxima of the corner response, i.e. To find particular strong corners that are spread across the image, first we need to find corners. You can visualize the output using a surface plot. The output is a matrix of corner scores: the higher the score, the higher the probability of that pixel being a corner. Corners in the image can be detected using cv2.cornerHarris function with the appropriate parameters. The objective of this step is to detect corners such that they are equally distributed across the image in order to avoid weird artifacts in warping. Adaptive Non-Maximal Suppression (or ANMS) 3: Sample image set for panorama stitchingĢ.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |