{"action":"created","issue":{"url":"https://api.github.com/repos/kmyid/learn-orientation/issues/4","repository_url":"https://api.github.com/repos/kmyid/learn-orientation","labels_url":"https://api.github.com/repos/kmyid/learn-orientation/issues/4/labels{/name}","comments_url":"https://api.github.com/repos/kmyid/learn-orientation/issues/4/comments","events_url":"https://api.github.com/repos/kmyid/learn-orientation/issues/4/events","html_url":"https://github.com/kmyid/learn-orientation/issues/4","id":330501471,"node_id":"MDU6SXNzdWUzMzA1MDE0NzE=","number":4,"title":"can't get the point in SIFT gradient computation.","user":{"login":"shawnlee103","id":22113072,"node_id":"MDQ6VXNlcjIyMTEzMDcy","avatar_url":"https://avatars0.githubusercontent.com/u/22113072?v=4","gravatar_id":"","url":"https://api.github.com/users/shawnlee103","html_url":"https://github.com/shawnlee103","followers_url":"https://api.github.com/users/shawnlee103/followers","following_url":"https://api.github.com/users/shawnlee103/following{/other_user}","gists_url":"https://api.github.com/users/shawnlee103/gists{/gist_id}","starred_url":"https://api.github.com/users/shawnlee103/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/shawnlee103/subscriptions","organizations_url":"https://api.github.com/users/shawnlee103/orgs","repos_url":"https://api.github.com/users/shawnlee103/repos","events_url":"https://api.github.com/users/shawnlee103/events{/privacy}","received_events_url":"https://api.github.com/users/shawnlee103/received_events","type":"User","site_admin":false},"labels":[],"state":"closed","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":0,"created_at":"2018-06-08T02:51:01Z","updated_at":"2018-06-14T08:06:25Z","closed_at":"2018-06-14T08:06:25Z","author_association":"NONE","body":"According to paper, \"We therefore use a numerical approximation of the gradients: when we form the training data we also compute the descriptors for many possible orientations, every 5 degrees in our current implementation. We can then efficiently compute the derivatives in @g/@theta by numerical differentiation.\"\r\nwhat dose \"numerical differentiation\" means in this step?\r\nhow to do this in code exactly if already get descriptor every 5 degree? \r\nIs it possible to implement in tensorflow?"},"comment":{"url":"https://api.github.com/repos/kmyid/learn-orientation/issues/comments/397208502","html_url":"https://github.com/kmyid/learn-orientation/issues/4#issuecomment-397208502","issue_url":"https://api.github.com/repos/kmyid/learn-orientation/issues/4","id":397208502,"node_id":"MDEyOklzc3VlQ29tbWVudDM5NzIwODUwMg==","user":{"login":"kmyid","id":7147797,"node_id":"MDQ6VXNlcjcxNDc3OTc=","avatar_url":"https://avatars1.githubusercontent.com/u/7147797?v=4","gravatar_id":"","url":"https://api.github.com/users/kmyid","html_url":"https://github.com/kmyid","followers_url":"https://api.github.com/users/kmyid/followers","following_url":"https://api.github.com/users/kmyid/following{/other_user}","gists_url":"https://api.github.com/users/kmyid/gists{/gist_id}","starred_url":"https://api.github.com/users/kmyid/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/kmyid/subscriptions","organizations_url":"https://api.github.com/users/kmyid/orgs","repos_url":"https://api.github.com/users/kmyid/repos","events_url":"https://api.github.com/users/kmyid/events{/privacy}","received_events_url":"https://api.github.com/users/kmyid/received_events","type":"User","site_admin":false},"created_at":"2018-06-14T08:06:25Z","updated_at":"2018-06-14T08:06:25Z","author_association":"OWNER","body":"It just means simple numerical approximation of the derivative. You compute the descriptor every five degrees, and then compute the numerical derivative, as you do with image gradients. \r\n\r\nIn the follow-up works, we've moved towards using learned descriptors, which no longer requires this."}}
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