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Ellen J.L. Brunenberg, Oriol Pujol, Bart M. Ter Haar Romeny, & Petia Radeva. (2006). Automatic IVUS Segmentation of Atherosclerotic Plaque with Stop & Go Snake.
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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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Sergio Escalera, Oriol Pujol, & Petia Radeva. (2007). Boosted Landmarks of Contextual Descriptors and Forest-ECOC: a Novel Framework to Detect and Classify Objects in Cluttered Scenes.
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Sergio Escalera, Petia Radeva, & Oriol Pujol. (2007). Complex Salient Regions for Computer Vision Problems. In IEEE Conference on Computer Vision and Pattern Recognition Workshop on.
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Sergio Escalera, Oriol Pujol, & Petia Radeva. (2007). Traffic Sign Classification using Error Correcting Techniques. In 2nd International Conference on Computer Vision Theory and Applications (281–285).
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Karla Lizbeth Caballero, Joel Barajas, & Oriol Pujol. (2007). Reconstructing IVUS Images for an Accurate Tissue Classification. In Proceedings of the Second International Conference on Computer Vision Theory and Applications (Vol. Special Sessions, 113–119).
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Oriol Pujol, Sergio Escalera, & Petia Radeva. (2008). An Incremental Node Embedding Technique for Error Correcting Output Codes. PR - Pattern Recognition, 713–725.
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Sergio Escalera, David M.J. Tax, Oriol Pujol, Petia Radeva, & Robert P.W. Duin. (2008). Subclass Problem-Dependent Design for Error-Correcting Output Codes. IEEE Trans. on Pattern Analysis and Machine Intelligence, vol.30(6):1041–1054.
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Sergio Escalera, Oriol Pujol, & Petia Radeva. (2008). Detection of Complex Salient Regions. EURASIP Journal on Advances in Signal Processing, vol. 2008, article ID451389, 11 pages.
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Sergio Escalera, Oriol Pujol, & Petia Radeva. (2008). Sub-Class Error-Correcting Output Codes. In Computer Vision Systems. 6th International Conference (Vol. 5008, 494–504).
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Sergio Escalera, Oriol Pujol, & Petia Radeva. (2008). Loss-Weighted Decoding for Error-Correcting Output Coding. In 3rd International Conference on Computer Vision Theory and Applications, (Vol. 2, 117–122).
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Oriol Rodriguez-Leor, Carlo Gatta, E. Fernandez-Nofrerias, Oriol Pujol, Neus Salvatella, C. Bosch, et al. (2008). Computationally Efficient Image-based IVUS Pullbacks Gating. European Heart Journal, ESC Supplement, Munich, 2008, p. 775.
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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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Carlo Gatta, Oriol Pujol, Oriol Rodriguez-Leor, J. Mauri, & Petia Radeva. (2008). Improved Rigid Registration of Vessel Structures using the Fast Radial Symmetry Transform. In Computer Vision for Intravascular Imaging CVII’08 Workshop Medical Image Computing and Computer–Assisted Intervention , 11th International Conference (128–136).
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Sergio Escalera, Oriol Pujol, J. Mauri, & Petia Radeva. (2008). IVUS Tissue Characterization with Sub-class Error-correcting Output Codes. In Computer Vision and Pattern Recognition Workshops, 2008. CVPR Workshops 2008. IEEE Computer Society Conference on, pp. 1–8, 23–28 juny 2008..
Abstract: Intravascular ultrasound (IVUS) represents a powerful imaging technique to explore coronary vessels and to study their morphology and histologic properties. In this paper, we characterize different tissues based on Radio Frequency, texture-based, slope-based, and combined features. To deal with the classification of multiple tissues, we require the use of robust multi-class learning techniques. In this context, we propose a strategy to model multi-class classification tasks using sub-classes information in the ECOC framework. The new strategy splits the classes into different subsets according to the applied base classifier. Complex IVUS data sets containing overlapping data are learnt by splitting the original set of classes into sub-classes, and embedding the binary problems in a problem-dependent ECOC design. The method automatically characterizes different tissues, showing performance improvements over the state-of-the-art ECOC techniques for different base classifiers and feature sets.
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