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Each detector is tested on the BSDS500 image set and 34 3D scanned, and manmade models using rotation in the X, Y, and Z axis as well as scale in the X,Y axis. Varying degrees of noise on the models are also tested. Results demonstrate the differing performance and behaviour of each detector across the evaluated transformations, which may assist computer vision practitioners in choosing the right detector for their application.<\/p>","DOI":"10.4018\/ijsi.2014010107","type":"journal-article","created":{"date-parts":[[2014,8,1]],"date-time":"2014-08-01T16:18:05Z","timestamp":1406909885000},"page":"86-105","source":"Crossref","is-referenced-by-count":1,"title":["Automated Evaluation of Interest Point Detectors"],"prefix":"10.4018","volume":"2","author":[{"given":"Simon R.","family":"Lang","sequence":"first","affiliation":[{"name":"School of Computer Science, Engineering and Mathematics, Flinders University, Adelaide, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Martin H.","family":"Luerssen","sequence":"additional","affiliation":[{"name":"School of Computer Science, Engineering and Mathematics, Flinders University, Adelaide, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David M. 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