000167916 001__ 167916
000167916 005__ 20190316235159.0
000167916 0247_ $$2doi$$a10.5075/epfl-thesis-5166
000167916 02470 $$2urn$$aurn:nbn:ch:bel-epfl-thesis5166-0
000167916 02471 $$2nebis$$a6549719
000167916 037__ $$aTHESIS
000167916 041__ $$aeng
000167916 088__ $$a5166
000167916 245__ $$aObject Duplicate Detection
000167916 269__ $$a2011
000167916 260__ $$aLausanne$$bEPFL$$c2011
000167916 300__ $$a258
000167916 336__ $$aTheses
000167916 520__ $$aWith the technological evolution of digital acquisition  and storage technologies, millions of images and video  sequences are captured every day and shared in online  services. One way of exploring this huge volume of images and  videos is through searching a particular object depicted in  images or videos by making use of object duplicate detection.  Therefore, need of research on object duplicate detection is  validated by several image and video retrieval applications,  such as tag propagation, augmented reality, surveillance,  mobile visual search, and television statistic measurement.  Object duplicate detection is detecting visually same or very  similar object to a query. Input is not restricted to an  image, it can be several images from an object or even it can  be a video. This dissertation describes the author's contribution to  solve problems on object duplicate detection in computer  vision. A novel graph-based approach is introduced for 2D and  3D object duplicate detection in still images. Graph model is  used to represent the 3D spatial information of the object  based on the local features extracted from training images so  that an explicit and complex 3D object modeling is avoided.  Therefore, improved performance can be achieved in comparison  to existing methods in terms of both robustness and  computational complexity. Our method is shown to be robust in  detecting the same objects even when images containing the  objects are taken from very different viewpoints or  distances. Furthermore, we apply our object duplicate  detection method to video, where the training images are  added iteratively to the video sequence in order to  compensate for 3D view variations, illumination changes and  partial occlusions. Finally, we show several mobile applications for object  duplicate detection, such as object recognition based museum  guide, money recognition or flower recognition. General  object duplicate detection may fail to detection chess  figures, however considering context, like chess board  position and height of the chess figure, detection can be  more accurate. We show that user interaction further improves  image retrieval compared to pure content-based methods  through a game, called Epitome.
000167916 6531_ $$aobject duplicate detection
000167916 6531_ $$aimage analysis
000167916 6531_ $$amobile visual search
000167916 6531_ $$agraph matching
000167916 700__ $$0242658$$aVajda, Péter$$g182052
000167916 720_2 $$0240223$$aEbrahimi, Touradj$$edir.$$g105043
000167916 8564_ $$s10544008$$uhttps://infoscience.epfl.ch/record/167916/files/EPFL_TH5166.pdf$$yTexte intégral / Full text$$zTexte intégral / Full text
000167916 909C0 $$0252077$$pMMSPL
000167916 909CO $$ooai:infoscience.tind.io:167916$$pSTI$$pthesis$$pthesis-bn2018$$pDOI$$qDOI2$$qGLOBAL_SET
000167916 918__ $$aSTI$$cIEL$$dEDIC2005-2015
000167916 919__ $$aGR-EB
000167916 920__ $$b2011
000167916 970__ $$a5166/THESES
000167916 973__ $$aEPFL$$sPUBLISHED
000167916 980__ $$aTHESIS