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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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Stefan Ameling, Stephan Wirth, Dietrich Paulus, Gerard Lacey, & Fernando Vilariño. (2009). Texture-based Polyp Detection in Colonoscopy. In Proc. BILDVERARBEITUNG FÜR DIE MEDIZIN.
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Fernando Vilariño, & Gerard Lacey. (2009). QUALITY ASSESSMENT IN COLONOSCOPY New challenges through computer vision-based systems. In in Proc. 3rd International Conference on Biomedical Electronics and Devices.
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Petia Radeva, Jordi Vitria, Fernando Vilariño, Panagiota Spyridonos, Fernando Azpiroz, Juan Malagelada, et al. (2009). Cascade analysis for intestinal contraction detection. US Patent Office.
Abstract: A method and system cascade analysisi for intestinal contraction detection is provided by extracting from image frames captured in-vivo. The method and system also relate to the detection of turbid liquids in intestinal tracts, to automatic detection of video image frames taken in the gastrointestinal tract including a field of view obstructed by turbid media, and more particulary, to extraction of image data obstructed by turbid media.
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S. Chanda, Umapada Pal, & Oriol Ramos Terrades. (2009). Word-Wise Thai and Roman Script Identification. TALIP - ACM Transactions on Asian Language Information Processing, 1–21.
Abstract: In some Thai documents, a single text line of a printed document page may contain words of both Thai and Roman scripts. For the Optical Character Recognition (OCR) of such a document page it is better to identify, at first, Thai and Roman script portions and then to use individual OCR systems of the respective scripts on these identified portions. In this article, an SVM-based method is proposed for identification of word-wise printed Roman and Thai scripts from a single line of a document page. Here, at first, the document is segmented into lines and then lines are segmented into character groups (words). In the proposed scheme, we identify the script of a character group combining different character features obtained from structural shape, profile behavior, component overlapping information, topological properties, and water reservoir concept, etc. Based on the experiment on 10,000 data (words) we obtained 99.62% script identification accuracy from the proposed scheme.
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Mathieu Nicolas Delalandre, Jean-Yves Ramel, Ernest Valveny, & Muhammad Muzzamil Luqman. (2009). A Performance Characterization Algorithm for Symbol Localization. In 8th IAPR International Workshop on Graphics Recognition (pp. 3–11). Springer.
Abstract: In this paper we present an algorithm for performance characterization of symbol localization systems. This algorithm is aimed to be a more “reliable” and “open” solution to characterize the performance. To achieve that, it exploits only single points as the result of localization and offers the possibility to reconsider the localization results provided by a system. We use the information about context in groundtruth, and overall localization results, to detect the ambiguous localization results. A probability score is computed for each matching between a localization point and a groundtruth region, depending on the spatial distribution of the other regions in the groundtruth. Final characterization is given with detection rate/probability score plots, describing the sets of possible interpretations of the localization results, according to a given confidence rate. We present experimentation details along with the results for the symbol localization system of [1], exploiting a synthetic dataset of architectural floorplans and electrical diagrams (composed of 200 images and 3861 symbols).
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Arnau Ramisa, Shrihari Vasudevan, David Aldavert, Ricardo Toledo, & Ramon Lopez de Mantaras. (2009). Evaluation of the SIFT Object Recognition Method in Mobile Robots: Frontiers in Artificial Intelligence and Applications. In 12th International Conference of the Catalan Association for Artificial Intelligence (Vol. 202, pp. 9–18).
Abstract: General object recognition in mobile robots is of primary importance in order to enhance the representation of the environment that robots will use for their reasoning processes. Therefore, we contribute reduce this gap by evaluating the SIFT Object Recognition method in a challenging dataset, focusing on issues relevant to mobile robotics. Resistance of the method to the robotics working conditions was found, but it was limited mainly to well-textured objects.
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