000167916 001__ 167916
000167916 005__ 20181205220053.0
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 $$ooai:infoscience.tind.io:167916$$pthesis$$pthesis-bn2018$$pDOI$$pSTI$$qDOI2
000167916 918__ $$aSTI$$cIEL$$dEDIC2005-2015
000167916 919__ $$aGR-EB
000167916 920__ $$b2011
000167916 970__ $$a5166/THESES
000167916 973__ $$aEPFL$$sPUBLISHED
000167916 980__ $$aTHESIS