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Ivan Huerta, Ariel Amato, Jordi Gonzalez, & Juan J. Villanueva. (2008). Fusing Edge Cues to Handle Colour Problems in Image Segmentation. In Articulated Motion and Deformable Objects, 5th International Conference (Vol. 5098, 279–288). LNCS.
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Pau Baiget, Xavier Roca, & Jordi Gonzalez. (2008). Autonomous Virtual Agents for Performance Evaluation of Tracking Algorithms. In Articulated Motion and Deformable Objects, 5th International Conference AMDO 2008, (Vol. 5098, pp. 299–308). LNCS.
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Bhaskar Chakraborty, Marco Pedersoli, & Jordi Gonzalez. (2008). View-Invariant Human Action Detection using Component-Wise HMM of Body Parts. In Articulated Motion and Deformable Objects, 5th International Conference (Vol. 5098, 208–217). LNCS.
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Carme Julia, Angel Sappa, Felipe Lumbreras, & Antonio Lopez. (2008). Recovery of Surface Normals and Reflectance from Different Lighting Conditions. In 5th International Conference on Image Analysis and Recognition (Vol. 5112, 315–325). LNCS.
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Carlo Gatta, Oriol Pujol, Oriol Rodriguez-Leor, J. Mauri, & Petia Radeva. (2008). Robust Image-based IVUS Pullbacks Gating. In Proceedings 11th International ConferenceMedical Image Computing and Computer–Assisted Intervention (Vol. 5242, 518–525). LNCS.
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Antonio Lopez, J. Hilgenstock, A. Busse, Ramon Baldrich, Felipe Lumbreras, & Joan Serrat. (2008). Nightime Vehicle Detecion for Intelligent Headlight Control. In Advanced Concepts for Intelligent Vision Systems, 10th International Conference, Proceedings, (Vol. 5259, 113–124). LNCS.
Keywords: Intelligent Headlights; vehicle detection
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Eduard Vazquez, Joost Van de Weijer, & Ramon Baldrich. (2008). Image Segmentation in the Presence of Shadows and Highligts. In 10th European Conference on Computer Vision (Vol. 5305, 1–14). LNCS.
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Miquel Ferrer, Ernest Valveny, F. Serratosa, & Horst Bunke. (2008). Exact Median Graph Computation via Graph Embedding. In 12th International Workshop on Structural and Syntactic Pattern Recognition (Vol. 5324, 15–24). LNCS.
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Bogdan Raducanu, & Fadi Dornaika. (2008). Dynamic Vs. Static Recognition of Facial Expressions. In Rabuñal (Ed.), Ambient Intelligence. European Conference (Vol. 5355, 13–25). LNCS.
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Sergio Escalera, Oriol Pujol, & Petia Radeva. (2009). Recoding Error-Correcting Output Codes. In 8th International Workshop of Multiple Classifier Systems (Vol. 5519, 11–21). Springer Berlin Heidelberg.
Abstract: One of the most widely applied techniques to deal with multi- class categorization problems is the pairwise voting procedure. Recently, this classical approach has been embedded in the Error-Correcting Output Codes framework (ECOC). This framework is based on a coding step, where a set of binary problems are learnt and coded in a matrix, and a decoding step, where a new sample is tested and classified according to a comparison with the positions of the coded matrix. In this paper, we present a novel approach to redefine without retraining, in a problem-dependent way, the one-versus-one coding matrix so that the new coded information increases the generalization capability of the system. Moreover, the final classification can be tuned with the inclusion of a weighting matrix in the decoding step. The approach has been validated over several UCI Machine Learning repository data sets and two real multi-class problems: traffic sign and face categorization. The results show that performance improvements are obtained when comparing the new approach to one of the best ECOC designs (one-versus-one). Furthermore, the novel methodology obtains at least the same performance than the one-versus-one ECOC design.
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Oriol Pujol, Eloi Puertas, & Carlo Gatta. (2009). Multi-scale Stacked Sequential Learning. In 8th International Workshop of Multiple Classifier Systems (Vol. 5519, 262–271). Springer Berlin Heidelberg.
Abstract: One of the most widely used assumptions in supervised learning is that data is independent and identically distributed. This assumption does not hold true in many real cases. Sequential learning is the discipline of machine learning that deals with dependent data such that neighboring examples exhibit some kind of relationship. In the literature, there are different approaches that try to capture and exploit this correlation, by means of different methodologies. In this paper we focus on meta-learning strategies and, in particular, the stacked sequential learning approach. The main contribution of this work is two-fold: first, we generalize the stacked sequential learning. This generalization reflects the key role of neighboring interactions modeling. Second, we propose an effective and efficient way of capturing and exploiting sequential correlations that takes into account long-range interactions by means of a multi-scale pyramidal decomposition of the predicted labels. Additionally, this new method subsumes the standard stacked sequential learning approach. We tested the proposed method on two different classification tasks: text lines classification in a FAQ data set and image classification. Results on these tasks clearly show that our approach outperforms the standard stacked sequential learning. Moreover, we show that the proposed method allows to control the trade-off between the detail and the desired range of the interactions.
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Carlo Gatta, Juan Diego Gomez, Francesco Ciompi, Oriol Rodriguez-Leor, & Petia Radeva. (2009). Toward robust myocardial blush grade estimation in contrast angiography. In 4th Iberian Conference on Pattern Recognition and Image Analysis (Vol. 5524, 249–256). LNCS. Springer Berlin Heidelberg.
Abstract: The assessment of Myocardial Blush Grade after primary angioplasty is a precious diagnostic tool to understand if the patient needs further medication or the use of specifics drugs. Unfortunately, the assessment of MBG is difficult for non highly specialized staff. Experimental data show that there is poor correlation between MBG assessment of low and high specialized staff, thus reducing its applicability. This paper proposes a method able to achieve an objective measure of MBG, or a set of parameters that correlates with the MBG. The method tracks the blush area starting from just one single frame tagged by the physician. As a consequence, the blush area is kept isolated from contaminating phenomena such as diaphragm and arteries movements. We also present a method to extract four parameters that are expected to correlate with the MBG. Preliminary results show that the method is capable of extracting interesting information regarding the behavior of the myocardial perfusion.
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Francesco Ciompi, Oriol Pujol, Oriol Rodriguez-Leor, Carlo Gatta, Angel Serrano, & Petia Radeva. (2009). Enhancing In-Vitro IVUS Data for Tissue Characterization. In 4th Iberian Conference on Pattern Recognition and Image Analysis (Vol. 5524, 241–248). LNCS. Springer Berlin Heidelberg.
Abstract: Intravascular Ultrasound (IVUS) data validation is usually performed by comparing post-mortem (in-vitro) IVUS data and corresponding histological analysis of the tissue, obtaining a reliable ground truth. The main drawback of this method is the few number of available study cases due to the complex procedure of histological analysis. In this work we propose a novel semi-supervised approach to enhance the in-vitro training set by including examples from in-vivo coronary plaques data set. For this purpose, a Sequential Floating Forward Selection method is applied on in-vivo data and plaque characterization performances are evaluated by Leave-One-Patient-Out cross-validation technique. Supervised data inclusion improves global classification accuracy from 89.39% to 91.82%.
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Miquel Ferrer, Ernest Valveny, & F. Serratosa. (2009). Median Graph Computation by means of a Genetic Approach Based on Minimum Common Supergraph and Maximum Common Subraph. In 4th Iberian Conference on Pattern Recognition and Image Analysis (Vol. 5524, 346–353). LNCS. Springer Berlin Heidelberg.
Abstract: Given a set of graphs, the median graph has been theoretically presented as a useful concept to infer a representative of the set. However, the computation of the median graph is a highly complex task and its practical application has been very limited up to now. In this work we present a new genetic algorithm for the median graph computation. A set of experiments on real data, where none of the existing algorithms for the median graph computation could be applied up to now due to their computational complexity, show that we obtain good approximations of the median graph. Finally, we use the median graph in a real nearest neighbour classification showing that it leaves the box of the only-theoretical concepts and demonstrating, from a practical point of view, that can be a useful tool to represent a set of graphs.
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Albert Gordo, & Ernest Valveny. (2009). The diagonal split: A pre-segmentation step for page layout analysis & classification. In 4th Iberian Conference on Pattern Recognition and Image Analysis (Vol. 5524, 290–297). LNCS. Springer Berlin Heidelberg.
Abstract: Document classification is an important task in all the processes related to document storage and retrieval. In the case of complex documents, structural features are needed to achieve a correct classification. Unfortunately, physical layout analysis is error prone. In this paper we present a pre-segmentation step based on a divide & conquer strategy that can be used to improve the page segmentation results, independently of the segmentation algorithm used. This pre-segmentation step is evaluated in classification and retrieval using the selective CRLA algorithm for layout segmentation together with a clustering based on the voronoi area diagram, and tested on two different databases, MARG and Girona Archives.
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