{"action":"created","issue":{"url":"https://api.github.com/repos/xylar/acciv/issues/2","labels_url":"https://api.github.com/repos/xylar/acciv/issues/2/labels{/name}","comments_url":"https://api.github.com/repos/xylar/acciv/issues/2/comments","events_url":"https://api.github.com/repos/xylar/acciv/issues/2/events","html_url":"https://github.com/xylar/acciv/issues/2","id":92106520,"number":2,"title":"Negative search range in first pass, fails?","user":{"login":"gabyx","id":647437,"avatar_url":"https://avatars.githubusercontent.com/u/647437?v=3","gravatar_id":"","url":"https://api.github.com/users/gabyx","html_url":"https://github.com/gabyx","followers_url":"https://api.github.com/users/gabyx/followers","following_url":"https://api.github.com/users/gabyx/following{/other_user}","gists_url":"https://api.github.com/users/gabyx/gists{/gist_id}","starred_url":"https://api.github.com/users/gabyx/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/gabyx/subscriptions","organizations_url":"https://api.github.com/users/gabyx/orgs","repos_url":"https://api.github.com/users/gabyx/repos","events_url":"https://api.github.com/users/gabyx/events{/privacy}","received_events_url":"https://api.github.com/users/gabyx/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"milestone":null,"comments":7,"created_at":"2015-06-30T14:16:46Z","updated_at":"2015-06-30T18:55:39Z","closed_at":null,"body":"When I use a negative search range  [-2 -1 -2 2]. in the x direction, because my flow has only negative x translation in **first** pass (where ther is no image advection used)\r\nACCIV terminates with \"pass failed\" and\r\nit says that there are no tie points found and the pass fails...?\r\n\r\n\r\n**output**\r\n\r\nUsing flat geometry\r\nDo pass test/pass1\r\nbeginning pass\r\nparameters read\r\ncreated work directory\r\ngrid read\r\nimage times read\r\nmaximum time separating valid image pairs: 0.001\r\ncorrelating images\r\n image pair 0 1\r\n  separation time: 0.001000\r\n  range scaling: 1.000000\r\n  search range [-2 -1 -2 2]\r\n10%\r\n20%\r\n30%\r\n40%\r\n50%\r\n60%\r\n70%\r\n80%\r\n90%\r\nNo tie points were found.\r\nimages correlated\r\nPass failed!"},"comment":{"url":"https://api.github.com/repos/xylar/acciv/issues/comments/117301646","html_url":"https://github.com/xylar/acciv/issues/2#issuecomment-117301646","issue_url":"https://api.github.com/repos/xylar/acciv/issues/2","id":117301646,"user":{"login":"xylar","id":4179064,"avatar_url":"https://avatars.githubusercontent.com/u/4179064?v=3","gravatar_id":"","url":"https://api.github.com/users/xylar","html_url":"https://github.com/xylar","followers_url":"https://api.github.com/users/xylar/followers","following_url":"https://api.github.com/users/xylar/following{/other_user}","gists_url":"https://api.github.com/users/xylar/gists{/gist_id}","starred_url":"https://api.github.com/users/xylar/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/xylar/subscriptions","organizations_url":"https://api.github.com/users/xylar/orgs","repos_url":"https://api.github.com/users/xylar/repos","events_url":"https://api.github.com/users/xylar/events{/privacy}","received_events_url":"https://api.github.com/users/xylar/received_events","type":"User","site_admin":false},"created_at":"2015-06-30T18:55:39Z","updated_at":"2015-06-30T18:55:39Z","body":"Now to your second comment/questions.\r\n\r\nI can see why the multiple passes vs. multiple \"days\" might be confusing.  The paper I wrote discusses the difference between images separated by \"short\" times and \"long times\".  Images separated by \"short\" times can be correlated with each other without any trouble.  The velocity field doesn't have any strong shearing, rotation or divergence over short times that will make features hard to follow.  Over \"long\" times, CIV on its own will have a hard time finding correlations without some help from advection.  So in order to correlate images separated by \"long\" times, you have to first get a guess at the velocity field from images seprated by \"short\" times and use that to advect the images separated by \"long\" times to a common point in time.\r\n\r\nAll those tricks are likely more trouble than they're worth for your problem.  My guess is that frames separated by less than 10 ms (maybe even more) are going to be easy to correlate.  So you should not worry too much about the concept of days. You can just try out using 2, 3, 4, 5, ... consecutive images together and figure out a multiple pass setup that works well.  Let's say you find that 5 images gives you the best results.  Then you can just do the same procedure with images 2-6 that you just did with 1-5 and you will get a velocity field that's appropriate for 1 frame later (but averaged over 5 frames).\r\n\r\nIf that doesn't make sense, I can see if I can set up an example."}}