000176304 001__ 176304
000176304 005__ 20180913061229.0
000176304 0247_ $$2doi$$a10.1109/TPAMI.2011.206
000176304 022__ $$a0162-8828
000176304 02470 $$2ISI$$a000301747400004
000176304 037__ $$aARTICLE
000176304 245__ $$aGradient Response Maps for Real-Time Detection of Textureless Objects
000176304 269__ $$a2012
000176304 260__ $$bInstitute of Electrical and Electronics Engineers$$c2012
000176304 336__ $$aJournal Articles
000176304 520__ $$aWe present a method for real-time 3D object instance detection that does not require a time-consuming training stage, and can handle untextured objects. At its core, our approach is a novel image representation for template matching designed to be robust to small image transformations. This robustness is based on spread image gradient orientations and allows us to test only a small subset of all possible pixel locations when parsing the image, and to represent a 3D object with a limited set of templates. In addition, we demonstrate that if a dense depth sensor is available we can extend our approach for an even better performance also taking 3D surface normal orientations into account. We show how to take advantage of the architecture of modern computers to build an efficient but very discriminant representation of the input images that can be used to consider thousands of templates in real time. We demonstrate in many experiments on real data that our method is much faster and more robust with respect to background clutter than current state-of-the-art methods.
000176304 6531_ $$aComputer vision
000176304 6531_ $$areal-time detection and object recognition
000176304 6531_ $$atracking
000176304 6531_ $$amultimodality template matching
000176304 6531_ $$aHausdorff Distance
000176304 700__ $$aHinterstoisser, Stefan
000176304 700__ $$aCagniart, Cedric
000176304 700__ $$0241826$$aIlic, Slobodan$$g129257
000176304 700__ $$aSturm, Peter
000176304 700__ $$aNavab, Nassir
000176304 700__ $$0240252$$aFua, Pascal$$g112366
000176304 700__ $$0240235$$aLepetit, Vincent$$g149007
000176304 773__ $$j34$$k5$$q876-888$$tIEEE Transactions on Pattern Analysis and Machine Intelligence
000176304 8564_ $$s1295433$$uhttps://infoscience.epfl.ch/record/176304/files/top.pdf$$yn/a$$zn/a
000176304 909C0 $$0252087$$pCVLAB$$xU10659
000176304 909CO $$ooai:infoscience.tind.io:176304$$pIC$$particle
000176304 917Z8 $$x112366
000176304 917Z8 $$x112366
000176304 937__ $$aEPFL-ARTICLE-176304
000176304 973__ $$aEPFL$$rREVIEWED$$sPUBLISHED
000176304 980__ $$aARTICLE