David Augusto Rojas. (2009). Colouring Local Feature Detection for Matching (Vol. 133). Master's thesis, , Bellaterra, Barcelona.
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Juan Diego Gomez. (2009). Toward Robust Myocardial Blush Grade Estimation in Contrast Angiography (Vol. 134). Master's thesis, , Bellaterra, Barcelona.
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Olivier Penacchio. (2009). Relative Density of L, M, S photoreceptors in the Human Retina (Vol. 135). Master's thesis, , Bellaterra, Barcelona.
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Xavier Boix. (2009). Learning Conditional Random Fields for Stereo (Vol. 136). Master's thesis, , Bellaterra, Barcelona.
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Shida Beigpour. (2009). Physics-based Reflectance Estimation Applied to Recoloring (Vol. 137). Master's thesis, , Bellaterra, Barcelona.
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Jaume Gibert. (2009). Learning structural representations and graph matching paradigms in the context of object recognition (Vol. 143). Master's thesis, , .
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Jose Carlos Rubio. (2009). Graph matching based on graphical models with application to vehicle tracking and classification at night (Vol. 144). Master's thesis, , Bellaterra, Barcelona.
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Farshad Nourbakhsh. (2009). Colour logo recognition (Vol. 145). Master's thesis, , Bellaterra, Barcelona.
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Enric Sala. (2009). Off-line person-dependent signature verification (Vol. 146). Master's thesis, , Bellaterra, Barcelona.
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Wenjuan Gong. (2009). Action priors for human pose tracking by particle filter. Master's thesis, , Bellaterra, Barcelona.
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Diego Alejandro Cheda. (2009). Monocular egomotion estimation for ADAS application (Vol. 148). Ph.D. thesis, , Bellaterra, Barcelona.
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Javier Marin. (2009). Virtual learning for real testing (Vol. 150). Master's thesis, , bell.
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Ivet Rafegas. (2013). Exploring Low-Level Vision Models. Case Study: Saliency Prediction (Vol. 175). Master's thesis, , .
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Francesco Brughi. (2013). Artistic Heritage Motive Retrieval: an Explorative Study (Vol. 176). Master's thesis, , .
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Thierry Brouard, Jordi Gonzalez, Caifeng Shan, Massimo Piccardi, & Larry S. Davis. (2014). Special issue on background modeling for foreground detection in real-world dynamic scenes. MVAP - Machine Vision and Applications, 25(5), 1101–1103.
Abstract: Although background modeling and foreground detection are not mandatory steps for computer vision applications, they may prove useful as they separate the primal objects usually called “foreground” from the remaining part of the scene called “background”, and permits different algorithmic treatment in the video processing field such as video surveillance, optical motion capture, multimedia applications, teleconferencing and human–computer interfaces. Conventional background modeling methods exploit the temporal variation of each pixel to model the background, and the foreground detection is made using change detection. The last decade witnessed very significant publications on background modeling but recently new applications in which background is not static, such as recordings taken from mobile devices or Internet videos, need new developments to detect robustly moving objects in challenging environments. Thus, effective methods for robustness to deal both with dynamic backgrounds, i
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