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H. Chouaib, Salvatore Tabbone, Oriol Ramos Terrades, F. Cloppet, N. Vincent, & A.T. Thierry Paquet. (2008). Sélection de Caractéristiques à partir d'un algorithme génétique et d'une combinaison de classifieurs Adaboost. In Colloque International Francophone sur l'Ecrit et le Document (pp. 181–186).
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T.O. Nguyen, Salvatore Tabbone, Oriol Ramos Terrades, & A.T. Thierry. (2008). Proposition d'un descripteur de formes et du modèle vectoriel pour la recherche de symboles. In Colloque International Francophone sur l'Ecrit et le Document (pp. 79–84).
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Salvatore Tabbone, Oriol Ramos Terrades, & S. Barrat. (2008). Histogram of radon transform. A useful descriptor for shape retrieval. In 19th International Conference on Pattern Recognition (pp. 1–4).
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Yainuvis Socarras, Sebastian Ramos, David Vazquez, Antonio Lopez, & Theo Gevers. (2013). Adapting Pedestrian Detection from Synthetic to Far Infrared Images. In ICCV Workshop on Visual Domain Adaptation and Dataset Bias. Sydney, Australy.
Abstract: We present different techniques to adapt a pedestrian classifier trained with synthetic images and the corresponding automatically generated annotations to operate with far infrared (FIR) images. The information contained in this kind of images allow us to develop a robust pedestrian detector invariant to extreme illumination changes.
Keywords: Domain Adaptation; Far Infrared; Pedestrian Detection
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V.C.Kieu, Alicia Fornes, M. Visani, N.Journet, & Anjan Dutta. (2013). The ICDAR/GREC 2013 Music Scores Competition on Staff Removal. In 10th IAPR International Workshop on Graphics Recognition.
Abstract: The first competition on music scores that was organized at ICDAR and GREC in 2011 awoke the interest of researchers, who participated both at staff removal and writer identification tasks. In this second edition, we propose a staff removal competition where we simulate old music scores. Thus, we have created a new set of images, which contain noise and 3D distortions. This paper describes the distortion methods, metrics, the participant’s methods and the obtained results.
Keywords: Competition; Music scores; Staff Removal
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M. Visani, V.C.Kieu, Alicia Fornes, & N.Journet. (2013). The ICDAR 2013 Music Scores Competition: Staff Removal. In 12th International Conference on Document Analysis and Recognition (pp. 1439–1443).
Abstract: The first competition on music scores that was organized at ICDAR in 2011 awoke the interest of researchers, who participated both at staff removal and writer identification tasks. In this second edition, we focus on the staff removal task and simulate a real case scenario: old music scores. For this purpose, we have generated a new set of images using two kinds of degradations: local noise and 3D distortions. This paper describes the dataset, distortion methods, evaluation metrics, the participant's methods and the obtained results.
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Miguel Angel Bautista, Oriol Pujol, Xavier Baro, & Sergio Escalera. (2011). Introducing the Separability Matrix for Error Correcting Output Codes Coding. In Carlo Sansone, Josef Kittler, & Fabio Roli (Eds.), 10th International Conference on Multiple Classifier Systems (Vol. 6713, pp. 227–236). LNCS. Springer-Verlag Berlin, Heidelberg.
Abstract: Error Correcting Output Codes (ECOC) have demonstrate to be a powerful tool for treating multi-class problems. Nevertheless, predefined ECOC designs may not benefit from Error-correcting principles for particular multi-class data. In this paper, we introduce the Separability matrix as a tool to study and enhance designs for ECOC coding. In addition, a novel problem-dependent coding design based on the Separability matrix is tested over a wide set of challenging multi-class problems, obtaining very satisfactory results.
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Ruth Aylett, Ginevra Castellano, Bogdan Raducanu, Ana Paiva, & Marc Hanheide. (2011). Long-term socially perceptive and interactive robot companions: challenges and future perspectives. In 13th International Conference on Multimodal Interaction (pp. 323–326). ACM.
Abstract: This paper gives a brief overview of the challenges for multi-model perception and generation applied to robot companions located in human social environments. It reviews the current position in both perception and generation and the immediate technical challenges and goes on to consider the extra issues raised by embodiment and social context. Finally, it briefly discusses the impact of systems that must function continually over months rather than just for a few hours.
Keywords: human-robot interaction, multimodal interaction, social robotics
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Fadi Dornaika, Alireza Bosaghzadeh, & Bogdan Raducanu. (2012). LSDA Solution Schemes for Modelless 3D Head Pose Estimation. In IEEE Workshop on the Applications of Computer Vision (pp. 393–398).
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Bogdan Raducanu, & Fadi Dornaika. (2012). Appearance-based Face Recognition Using A Supervised Manifold Learning Framework. In IEEE Workshop on the Applications of Computer Vision (pp. 465–470). IEEE Xplore.
Abstract: Many natural image sets, depicting objects whose appearance is changing due to motion, pose or light variations, can be considered samples of a low-dimension nonlinear manifold embedded in the high-dimensional observation space (the space of all possible images). The main contribution of our work is represented by a Supervised Laplacian Eigemaps (S-LE) algorithm, which exploits the class label information for mapping the original data in the embedded space. Our proposed approach benefits from two important properties: i) it is discriminative, and ii) it adaptively selects the neighbors of a sample without using any predefined neighborhood size. Experiments were conducted on four face databases and the results demonstrate that the proposed algorithm significantly outperforms many linear and non-linear embedding techniques. Although we've focused on the face recognition problem, the proposed approach could also be extended to other category of objects characterized by large variance in their appearance.
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Antonio Hernandez, Carlos Primo, & Sergio Escalera. (2011). Automatic user interaction correction via Multi-label Graph cuts. In In ICCV 2011 1st IEEE International Workshop on Human Interaction in Computer Vision HICV (pp. 1276–1281).
Abstract: Most applications in image segmentation requires from user interaction in order to achieve accurate results. However, user wants to achieve the desired segmentation accuracy reducing effort of manual labelling. In this work, we extend standard multi-label α-expansion Graph Cut algorithm so that it analyzes the interaction of the user in order to modify the object model and improve final segmentation of objects. The approach is inspired in the fact that fast user interactions may introduce some pixel errors confusing object and background. Our results with different degrees of user interaction and input errors show high performance of the proposed approach on a multi-label human limb segmentation problem compared with classical α-expansion algorithm.
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Miguel Reyes, Gabriel Dominguez, & Sergio Escalera. (2011). Feature Weighting in Dynamic Time Warping for Gesture Recognition in Depth Data. In 1st IEEE Workshop on Consumer Depth Cameras for Computer Vision (pp. 1182–1188).
Abstract: We present a gesture recognition approach for depth video data based on a novel Feature Weighting approach within the Dynamic Time Warping framework. Depth features from human joints are compared through video sequences using Dynamic Time Warping, and weights are assigned to features based on inter-intra class gesture variability. Feature Weighting in Dynamic Time Warping is then applied for recognizing begin-end of gestures in data sequences. The obtained results recognizing several gestures in depth data show high performance compared with classical Dynamic Time Warping approach.
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Santiago Segui, Michal Drozdzal, Petia Radeva, & Jordi Vitria. (2012). An Integrated Approach to Contextual Face Detection. In 1st International Conference on Pattern Recognition Applications and Methods (pp. 143–150). Springer.
Abstract: Face detection is, in general, based on content-based detectors. Nevertheless, the face is a non-rigid object with well defined relations with respect to the human body parts. In this paper, we propose to take benefit of the context information in order to improve content-based face detections. We propose a novel framework for integrating multiple content- and context-based detectors in a discriminative way. Moreover, we develop an integrated scoring procedure that measures the ’faceness’ of each hypothesis and is used to discriminate the detection results. Our approach detects a higher rate of faces while minimizing the number of false detections, giving an average increase of more than 10% in average precision when comparing it to state-of-the art face detectors
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Patricia Marquez, Debora Gil, & Aura Hernandez-Sabate. (2012). Error Analysis for Lucas-Kanade Based Schemes. In 9th International Conference on Image Analysis and Recognition (Vol. 7324, pp. 184–191). LNCS. Springer-Verlag Berlin Heidelberg.
Abstract: Optical flow is a valuable tool for motion analysis in medical imaging sequences. A reliable application requires determining the accuracy of the computed optical flow. This is a main challenge given the absence of ground truth in medical sequences. This paper presents an error analysis of Lucas-Kanade schemes in terms of intrinsic design errors and numerical stability of the algorithm. Our analysis provides a confidence measure that is naturally correlated to the accuracy of the flow field. Our experiments show the higher predictive value of our confidence measure compared to existing measures.
Keywords: Optical flow, Confidence measure, Lucas-Kanade, Cardiac Magnetic Resonance
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Albert Andaluz, Francesc Carreras, Cristina Santa Marta, & Debora Gil. (2012). Myocardial torsion estimation with Tagged-MRI in the OsiriX platform. In Wiro Niessen(Erasmus MC) and Marc Modat(UCL) (Ed.), ISBI Workshop on Open Source Medical Image Analysis software. IEEE.
Abstract: Myocardial torsion (MT) plays a crucial role in the assessment of the functionality of the
left ventricle. For this purpose, the IAM group at the CVC has developed the Harmonic Phase Flow (HPF) plugin for the Osirix DICOM platform . We have validated its funcionalty on sequences acquired using different protocols and including healthy and pathological cases. Results show similar torsion trends for SPAMM acquisitions, with pathological cases introducing expected deviations from the ground truth. Finally, we provide the plugin free of charge at http://iam.cvc.uab.es
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