{"action":"opened","issue":{"url":"https://api.github.com/repos/irvcaza/TraDetCV16/issues/3","repository_url":"https://api.github.com/repos/irvcaza/TraDetCV16","labels_url":"https://api.github.com/repos/irvcaza/TraDetCV16/issues/3/labels{/name}","comments_url":"https://api.github.com/repos/irvcaza/TraDetCV16/issues/3/comments","events_url":"https://api.github.com/repos/irvcaza/TraDetCV16/issues/3/events","html_url":"https://github.com/irvcaza/TraDetCV16/issues/3","id":186073970,"number":3,"title":"Exercise Sheet 1: Exercise 3","user":{"login":"irvcaza","id":15790161,"avatar_url":"https://avatars.githubusercontent.com/u/15790161?v=3","gravatar_id":"","url":"https://api.github.com/users/irvcaza","html_url":"https://github.com/irvcaza","followers_url":"https://api.github.com/users/irvcaza/followers","following_url":"https://api.github.com/users/irvcaza/following{/other_user}","gists_url":"https://api.github.com/users/irvcaza/gists{/gist_id}","starred_url":"https://api.github.com/users/irvcaza/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/irvcaza/subscriptions","organizations_url":"https://api.github.com/users/irvcaza/orgs","repos_url":"https://api.github.com/users/irvcaza/repos","events_url":"https://api.github.com/users/irvcaza/events{/privacy}","received_events_url":"https://api.github.com/users/irvcaza/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":0,"created_at":"2016-10-29T11:36:28Z","updated_at":"2016-10-29T11:36:28Z","closed_at":null,"body":"**Image Gradients**\r\n\r\n- [ ] First, use the basic derivative filters from the lecture to compute the gradient images in x- and y-direction. The basic derivative filters are: [Eq. 5](https://github.com/irvcaza/TraDetCV16/blob/master/Assignment1/exercise01.pdf). These filter masks are applied separately to the original image, such that we get two seperate gradient images as result.\r\n- [ ] From these two gradient images we now compute the gradient magnitudes and orientations using: [Eq. 6,7](https://github.com/irvcaza/TraDetCV16/blob/master/Assignment1/exercise01.pdf). Visualize the gradient magnitudes and orientations using gray value images.\r\n- [ ] Finally, we want to reduce the noise in the image before computing the gradients: \r\n[Eq. 8,9](https://github.com/irvcaza/TraDetCV16/blob/master/Assignment1/exercise01.pdf).\r\nBut, instead of processing the input image twice we make use of the associativity law.\r\nFor this, first filter the Gaussian convolution mask using the basic derivative filters\r\nand then convolve the image with the resulting mask: [Eq. 10,11](https://github.com/irvcaza/TraDetCV16/blob/master/Assignment1/exercise01.pdf)."}}
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