000175537 001__ 175537
000175537 005__ 20190316235323.0
000175537 020__ $$a978-1-4673-1228-8
000175537 02470 $$2ISI$$a000309166200065
000175537 037__ $$aCONF
000175537 245__ $$aFREAK: Fast Retina Keypoint
000175537 269__ $$a2012
000175537 260__ $$bIeee$$c2012$$aNew York
000175537 300__ $$a8
000175537 336__ $$aConference Papers
000175537 490__ $$aIEEE Conference on Computer Vision and Pattern Recognition
000175537 500__ $$aCVPR 2012 Open Source Award Winner
000175537 520__ $$aA large number of vision applications rely on matching keypoints across images. The last decade featured an arms-race towards faster and more robust keypoints and association algorithms: Scale Invariant Feature Transform (SIFT), Speed-up Robust Feature (SURF), and more recently Binary Robust Invariant Scalable Keypoints (BRISK) to name a few. These days, the deployment of vision algorithms on smart phones and embedded devices with low memory and computation complexity has even upped the ante: the goal is to make descriptors faster to compute, more compact while remaining robust to scale, rotation and noise. To best address the current requirements, we propose a novel keypoint descriptor inspired by the human visual system and more precisely the retina, coined Fast Retina Keypoint (FREAK). A cascade of binary strings is computed by efficiently comparing image intensities over a retinal sampling pattern. Our experiments show that FREAKs are in general faster to compute with lower memory load and also more robust than SIFT, SURF or BRISK. They are thus competitive alternatives to existing keypoints in particular for embedded applications.
000175537 6531_ $$aKeypoint
000175537 6531_ $$aimage matching
000175537 6531_ $$abinary descriptor
000175537 6531_ $$aretina
000175537 6531_ $$alts2
000175537 6531_ $$aaward
000175537 700__ $$0242925$$g129343$$aAlahi, Alexandre
000175537 700__ $$0(EPFLAUTH)178723$$g178723$$aOrtiz, Raphaël
000175537 700__ $$aVandergheynst, Pierre$$g120906$$0240428
000175537 7112_ $$dJune 16-21, 2012$$cRhode Island, Providence, USA$$aIEEE Conference on Computer Vision and Pattern Recognition
000175537 773__ $$tIEEE Conference on Computer Vision and Pattern Recognition
000175537 8564_ $$uhttps://infoscience.epfl.ch/record/175537/files/2069.pdf$$zn/a$$s3494023$$yn/a
000175537 8564_ $$uhttps://infoscience.epfl.ch/record/175537/files/c5.jpg$$zn/a$$s9553$$yn/a
000175537 909C0 $$xU10380$$0252392$$pLTS2
000175537 909C0 $$0252606$$pVITA$$xU13529
000175537 909CO $$qGLOBAL_SET$$pconf$$pSTI$$pENAC$$ooai:infoscience.tind.io:175537
000175537 917Z8 $$x129343
000175537 917Z8 $$x129343
000175537 917Z8 $$x129343
000175537 917Z8 $$x173008
000175537 937__ $$aEPFL-CONF-175537
000175537 973__ $$rREVIEWED$$sPUBLISHED$$aEPFL
000175537 980__ $$aCONF