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Pau Baiget, Carles Fernandez, Xavier Roca, & Jordi Gonzalez. (2007). Automatic Learning of Conceptual Knowledge for the Interpretation of Human Behavior in Video Sequences. In 3rd Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA 2007), J. Marti et al. (Eds.) LNCS 4477:507–514.
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Eduard Vazquez, Ramon Baldrich, Javier Vazquez, & Maria Vanrell. (2007). Topological histogram reduction towards colour segmentation. In 3rd Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA 2007), J. Marti et al. (Eds.) LNCS 4477:55–62.
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Fadi Dornaika, & Angel Sappa. (2007). SFM for Planar Scenes: a Direct and Robust Approach. In book chapter: Informatics in Control, Automation and Robotics II, Ed. J. Filipe, J. Ferrier, J. Cetto and M. Carvalho, pp. 129–136. (best papers ICINCO 2005).
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Javier Vazquez, Maria Vanrell, Anna Salvatella, & Eduard Vazquez. (2007). A colour space based on the image content. In Artificial Intelligence Research and Development, C. Angulo and L. Godo, pp 205–212 IOS Press.
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Santiago Segui, Laura Igual, Petia Radeva, Carolina Malagelada, Fernando Azpiroz, & Jordi Vitria. (2007). A Semi-Supervised Learning Method for Motility Disease Diagnostic. In Progress in Pattern Recognition, Image Analysis and Applications, 12th Iberoamerican Congress on Pattern (CIARP 2007), LCNS 4756:773–782, ISBN 978–3–540–76724–4.
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Meritxell Vinyals, Arnau Ramisa, & Ricardo Toledo. (2007). An Evaluation of an Object Recognition Schema using Multiple Region Detectors. In Artificial Intelligence Research and Development, 163:213–222, ISBN: 978–1–58603–798–7, Proceedings of the 10th International Conference of the ACIA (CCIA’07).
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Antonio Lopez, W. Niessen, Joan Serrat, K. Nikolay, B. Ter Haar Romeny, Juan J. Villanueva, et al. (2000). New improvements in the multiscale analysis of trabecular bone patterns. In Pattern Recognition and Applications (pp. 251–260). IOS Press.
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Sergio Escalera, Oriol Pujol, & Petia Radeva. (2007). Robust Complex Salient Regions. In 3rd Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA 2007), J. Marti et al. (Eds.) LNCS 4478:113–121.
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David Masip, Agata Lapedriza, & Jordi Vitria. (2007). Measuring External Face Appearance for Face Classification. In Face Recognition, Ed. Kresimir Delac and Mislav Grgic, pp. 287–307, ISBN 978–3–902613–03–5, I–Tech Education and Publishing.
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Oriol Ramos Terrades, Ernest Valveny, & Salvatore Tabbone. (2008). On the Combination of Ridgelets Descriptors for Symbol Recognition. In Graphics Recognition: Recent Advances and New Oportunities, W. Lius, J. Llados, J.M. Ogier, LNCS 5046:104–113.
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Fadi Dornaika, & Bogdan Raducanu. (2008). Facial Expression Recognition for HCI Applications. In Rabuñal (Ed.), Encyclopedia of Artificial Intelligence (Vol. II, 625–631). IGI–Global Publisher.
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Josep Llados, & Marçal Rusiñol. (2014). Graphics Recognition Techniques. In D. Doermann, & K. Tombre (Eds.), Handbook of Document Image Processing and Recognition (Vol. D, pp. 489–521). Springer London.
Abstract: This chapter describes the most relevant approaches for the analysis of graphical documents. The graphics recognition pipeline can be splitted into three tasks. The low level or lexical task extracts the basic units composing the document. The syntactic level is focused on the structure, i.e., how graphical entities are constructed, and involves the location and classification of the symbols present in the document. The third level is a functional or semantic level, i.e., it models what the graphical symbols do and what they mean in the context where they appear. This chapter covers the lexical level, while the next two chapters are devoted to the syntactic and semantic level, respectively. The main problems reviewed in this chapter are raster-to-vector conversion (vectorization algorithms) and the separation of text and graphics components. The research and industrial communities have provided standard methods achieving reasonable performance levels. Hence, graphics recognition techniques can be considered to be in a mature state from a scientific point of view. Additionally this chapter provides insights on some related problems, namely, the extraction and recognition of dimensions in engineering drawings, and the recognition of hatched and tiled patterns. Both problems are usually associated, even integrated, in the vectorization process.
Keywords: Dimension recognition; Graphics recognition; Graphic-rich documents; Polygonal approximation; Raster-to-vector conversion; Texture-based primitive extraction; Text-graphics separation
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Niki Aifanti, Angel Sappa, N. Grammalidis, & Sotiris Malassiotis. (2009). Advances in Tracking and Recognition of Human Motion. In Encyclopedia of Information Science and Technology (Vol. I, 65–71).
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David Geronimo, Angel Sappa, & Antonio Lopez. (2010). Stereo-based Candidate Generation for Pedestrian Protection Systems. In Binocular Vision: Development, Depth Perception and Disorders (189–208). NOVA Publishers.
Abstract: This chapter describes a stereo-based algorithm that provides candidate image windows to a latter 2D classification stage in an on-board pedestrian detection system. The proposed algorithm, which consists of three stages, is based on the use of both stereo imaging and scene prior knowledge (i.e., pedestrians are on the ground) to reduce the candidate searching space. First, a successful road surface fitting algorithm provides estimates on the relative ground-camera pose. This stage directs the search toward the road area thus avoiding irrelevant regions like the sky. Then, three different schemes are used to scan the estimated road surface with pedestrian-sized windows: (a) uniformly distributed through the road surface (3D); (b) uniformly distributed through the image (2D); (c) not uniformly distributed but according to a quadratic function (combined 2D-3D). Finally, the set of candidate windows is reduced by analyzing their 3D content. Experimental results of the proposed algorithm, together with statistics of searching space reduction are provided.
Keywords: Pedestrian Detection
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Thierry Brouard, A. Delaplace, Muhammad Muzzamil Luqman, H. Cardot, & Jean-Yves Ramel. (2010). Design of Evolutionary Methods Applied to the Learning of Bayesian Nerwork Structures. In Ahmed Rebai (Ed.), Bayesian Network (pp. 13–37). Sciyo.
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