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M. Bressan, David Guillamet, & Jordi Vitria. (2003). Using an ICA Representation of Local Color Histograms for Object Recognition. Pattern Recognition, 36(3):691–701 (IF: 1.611).
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M. Bressan, David Guillamet, & Jordi Vitria. (2004). Multiclass Object Recognition using Class-Conditional Independent Component Analisis. Cybernetics and Systems, 35/1:35–61 (IF: 0.768).
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M. Bressan, & Jordi Vitria. (2001). Independent Modes of Variation in Point Distribution Models.
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M. Bressan, & Jordi Vitria. (2002). Feature Subset Selection in an ICA Space.
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M. Bressan, & Jordi Vitria. (2002). Improving Naive Bayes using Class Conditional ICA.
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M. Bressan, & Jordi Vitria. (2002). Independent Component Analysis and Naïve Bayes Classification. Proceedings of the Second IASTED International Conference Visualilzation, Imaging and Image Proceesing VIIP 2002: 496–501., .
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M. Bressan, & Jordi Vitria. (2002). Improving Naive Bayes using Class Condicitonal ICA..
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M. Bressan, & Jordi Vitria. (2003). Independent Feature Selection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(10): 1312–1317 (IF: 3.823).
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M. Bressan, & Jordi Vitria. (2003). Nonparametric Discriminant Analysis and Nearest Neighbor Classification. PRL - Pattern Recognition Letters, 24(15), 2743–2749.
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M. Campos-Taberner, Adriana Romero, Carlo Gatta, & Gustavo Camps-Valls. (2015). Shared feature representations of LiDAR and optical images: Trading sparsity for semantic discrimination. In IEEE International Geoscience and Remote Sensing Symposium IGARSS2015 (pp. 4169–4172).
Abstract: This paper studies the level of complementary information conveyed by extremely high resolution LiDAR and optical images. We pursue this goal following an indirect approach via unsupervised spatial-spectral feature extraction. We used a recently presented unsupervised convolutional neural network trained to enforce both population and lifetime spar-sity in the feature representation. We derived independent and joint feature representations, and analyzed the sparsity scores and the discriminative power. Interestingly, the obtained results revealed that the RGB+LiDAR representation is no longer sparse, and the derived basis functions merge color and elevation yielding a set of more expressive colored edge filters. The joint feature representation is also more discriminative when used for clustering and topological data visualization.
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M. Cruz, Cristhian A. Aguilera-Carrasco, Boris X. Vintimilla, Ricardo Toledo, & Angel Sappa. (2015). Cross-spectral image registration and fusion: an evaluation study. In 2nd International Conference on Machine Vision and Machine Learning.
Abstract: This paper presents a preliminary study on the registration and fusion of cross-spectral imaging. The objective is to evaluate the validity of widely used computer vision approaches when they are applied at different
spectral bands. In particular, we are interested in merging images from the infrared (both long wave infrared: LWIR and near infrared: NIR) and visible spectrum (VS). Experimental results with different data sets are presented.
Keywords: multispectral imaging; image registration; data fusion; infrared and visible spectra
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M. Danelljan, Fahad Shahbaz Khan, Michael Felsberg, & Joost Van de Weijer. (2014). Adaptive color attributes for real-time visual tracking. In 27th IEEE Conference on Computer Vision and Pattern Recognition (pp. 1090–1097).
Abstract: Visual tracking is a challenging problem in computer vision. Most state-of-the-art visual trackers either rely on luminance information or use simple color representations for image description. Contrary to visual tracking, for object
recognition and detection, sophisticated color features when combined with luminance have shown to provide excellent performance. Due to the complexity of the tracking problem, the desired color feature should be computationally
efficient, and possess a certain amount of photometric invariance while maintaining high discriminative power.
This paper investigates the contribution of color in a tracking-by-detection framework. Our results suggest that color attributes provides superior performance for visual tracking. We further propose an adaptive low-dimensional
variant of color attributes. Both quantitative and attributebased evaluations are performed on 41 challenging benchmark color sequences. The proposed approach improves the baseline intensity-based tracker by 24% in median distance precision. Furthermore, we show that our approach outperforms
state-of-the-art tracking methods while running at more than 100 frames per second.
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M. Gomez, J. Mauri, E. Fernandez-Nofrerias, Oriol Rodriguez-Leor, Carme Julia, David Rotger, et al. (2002). Una nova aplicacio informatica per a la correlacio d imatges angiografiques i d ecografia intracoronaria. Revista de la Societat Catalana de Cardiologia, 4(4): 42, XIV Congres de la Societat Catalana de Cardiologia..
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M. Gomez, J. Mauri, E. Fernandez-Nofrerias, Oriol Rodriguez-Leor, Carme Julia, Misael Rosales, et al. (2002). Modelo fisico para la simulacion de ultrasonido Intravascular. XXXVIII Congreso Nacional de la Sociedad Española de Cardiologia..
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M. Gomez, J. Mauri, E. Fernandez-Nofrerias, Oriol Rodriguez-Leor, Carme Julia, Oriol Pujol, et al. (2002). Diferenciacion de las estructuras del vaso coronario mediante el procesamiento de imagenes y el analisis de las diferentes texturas a partir de la ecografia intracoronaria. XXXVIII Congreso Nacional de la Sociedad Española de Cardiologia.
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