Eyecanseecan.blogspot.com Website Review

training a robot to detect soda cans against a cluttered background. This project will hopefully make steps towards mobile generic object I tried applying the keypoint-matching portion of the SIFT paper to an input image but I did not achieve very good results. I matched two input images to my database of several hundred different soda-can images and typically achieved results as shown in the first picture. This seems to indicate that when a cluttered background is present, matching individual keypoints does not produce reliable results. The second image shows all of the keypoints found in the input image, which is around 400. It appears that keypoints are being generated for each soda-can in the input image but these are not being determined to be the closest-matching keypoints in the scene. In order to further determine what the problem is, I tried matching a database image with a frame containing exactly the same image of the soda can in the database. Though this is definitely not a way to test a Computer Vision algorithm for clas*ification ability, it gave some indication about why the poor matching results were occurring. Even though the same image was used, not all of the points were matched. This indicates that an*lyzing the constellation of points is very important when dealing with a cluttered background. Otherwise, the background could have so much variety to it that keypoints arising in it would match better than keypoints in the correct object. Lowe describes in his SIFT paper a process of using clusters of results from a generalized Hough Transform. Although Lowe achieved good matching results using just his keypoint matching algorithm, he used high-resolution images where the keypoints within the objects in question were numerous and apparently more distinguishable from the background, as seen with a book-matching example at this link: The next step that I take might be to implement the generalized Hough Transform that Lowe mentions, followed by a pose-estimation. One