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Javier Varona, Jordi Gonzalez, Ignasi Rius, & Juan J. Villanueva. (2008). Importance of Detection for Video Surveillance Applications. Optical Engineering, vol. 47(8), 087201/1–9.
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Bogdan Raducanu, & Jordi Vitria. (2008). Learning to Learn: From Smarts Machines to Intelligent Machines. PRL - Patter Recognition Letters, 1024–1032.
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Agata Lapedriza, Santiago Segui, David Masip, & Jordi Vitria. (2008). A Sparse Bayesian Approach for Joint Feature Selection and Classifier Learning. Pattern Analysis and Applications, Special Issue: Non–Parametric Distance–Based Classification Techniques and Their Applications,, 299–308.
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F. Pla, Petia Radeva, & Jordi Vitria. (2008). Non-parametric distance-based classification techniques and their applications. Pattern Analysis and Applications, Special Issue: Non–Parametric Distance–Based Classification Techniques and Their Applications, 223–225.
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Bogdan Raducanu, & Jordi Vitria. (2008). Online Nonparametric Discriminant Analysis for Incremental Subspace Learning and Recognition. Pattern Analysis and Applications. Special Issue: Non–Parametric Distance–Based Classification Techniques and Their Applications, 259–268.
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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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Jose Antonio Rodriguez, Florent Perronnin, Gemma Sanchez, & Josep Llados. (2008). Unsupervised writer style adaptation for handwritten word spotting. In Pattern Recognition. 19th International Conference on, IBM Best Student Paper Award..
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Xavier Otazu, Maria Vanrell, & C. Alejandro Parraga. (2008). Colour induction effects are modelled by a low-level multiresolution wavelet framework. Perception 37(Suppl.): 107.
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Miquel Ferrer. (2008). Theory and Algorithms on the Median Graph. Application to Graph-based Classification and Clustering (Francesc Serratosa Casanelles, & Ernest Valveny, Eds.). Ph.D. thesis, , .
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Sergio Escalera. (2008). Coding and Decoding Design of ECOCs for Multi-class Pattern and Object Recognition A (Petia Radeva, & Oriol Pujol, Eds.). Ph.D. thesis, Ediciones Graficas Rey, .
Abstract: Many real problems require multi-class decisions. In the Pattern Recognition field,
many techniques have been proposed to deal with the binary problem. However,
the extension of many 2-class classifiers to the multi-class case is a hard task. In
this sense, Error-Correcting Output Codes (ECOC) demonstrated to be a powerful
tool to combine any number of binary classifiers to model multi-class problems. But
there are still many open issues about the capabilities of the ECOC framework. In
this thesis, the two main stages of an ECOC design are analyzed: the coding and
the decoding steps. We present different problem-dependent designs. These designs
take advantage of the knowledge of the problem domain to minimize the number
of classifiers, obtaining a high classification performance. On the other hand, we
analyze the ECOC codification in order to define new decoding rules that take full
benefit from the information provided at the coding step. Moreover, as a successful
classification requires a rich feature set, new feature detection/extraction techniques
are presented and evaluated on the new ECOC designs. The evaluation of the new
methodology is performed on different real and synthetic data sets: UCI Machine
Learning Repository, handwriting symbols, traffic signs from a Mobile Mapping System, Intravascular Ultrasound images, Caltech Repository data set or Chaga’s disease
data set. The results of this thesis show that significant performance improvements
are obtained on both traditional coding and decoding ECOC designs when the new
coding and decoding rules are taken into account.
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Matthias S. Keil, Agata Lapedriza, David Masip, & Jordi Vitria. (2008). Preferred Spatial Frequencies for Human Face Processing Are Associated with Optimal Class Discrimination in the Machine. PLoS ONE 3(7):e2590, DOI:10.1371/journal.pone.0002590.
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Jaume Garcia, Francesc Carreras, Sandra Pujades, & Debora Gil. (2008). Regional motion patterns for the Left Ventricle function assessment. In Proc. 19th Int. Conf. Pattern Recognition ICPR 2008 (pp. 1–4).
Abstract: Regional scores (e.g. strain, perfusion) of the Left Ventricle (LV) functionality are playing an increasing role in the diagnosis of cardiac diseases. A main limitation is the lack of normality models for complementary scores oriented to assessment of the LV integrity. This paper introduces an original framework based on a parametrization of the LV domain, which allows comparison across subjects of local physiological measures of different nature. We compute regional normality patterns in a feature space characterizing the LV function. We show the consistency of the model for the regional motion on healthy and hypokinetic pathological cases
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Jaume Garcia, Debora Gil, A.Bajo, M.J.Ledesma-Carbayo, & C.SantaMarta. (2008). Influence of the temporal resolution on the quantification of displacement fields in cardiac magnetic resonance tagged images. In Alan Murray (Ed.), Proc. Computers in Cardiology (Vol. 35, pp. 785–788).
Abstract: It is difficult to acquire tagged cardiac MR images with a high temporal and spatial resolution using clinical MR scanners. However, if such images are used for quantifying scores based on motion, it is essential a resolution as high as possible. This paper explores the influence of the temporal resolution of a tagged series on the quantification of myocardial dynamic parameters. To such purpose we have designed a SPAMM (Spatial Modulation of Magnetization) sequence allowing acquisition of sequences at simple and double temporal resolution. Sequences are processed to compute myocardial motion by an automatic technique based on the tracking of the harmonic phase of tagged images (the Harmonic Phase Flow, HPF). The results have been compared to manual tracking of myocardial tags. The error in displacement fields for double resolution sequences reduces 17%.
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Aura Hernandez-Sabate, David Rotger, & Debora Gil. (2008). Image-based ECG sampling of IVUS sequences. In Proc. IEEE Ultrasonics Symp. IUS 2008 (pp. 1330–1333).
Abstract: Longitudinal motion artifacts in IntraVascular UltraSound (IVUS) sequences hinders a properly 3D reconstruction and vessel measurements. Most of current techniques base on the ECG signal to obtain a gated pullback without the longitudinal artifact by using a specific hardware or the ECG signal itself. The potential of IVUS images processing for phase retrieval still remains little explored. In this paper, we present a fast forward image-based algorithm to approach ECG sampling. Inspired on the fact that maximum and minimum lumen areas are related to end-systole and end-diastole, our cardiac phase retrieval is based on the analysis of tissue density of mass along the sequence. The comparison between automatic and manual phase retrieval (0.07 ± 0.07 mm. of error) encourages a deep validation contrasting with ECG signals.
Keywords: Longitudinal Motion; Image-based ECG-gating; Fourier analysis
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Robert Benavente, Laura Igual, & Fernando Vilariño. (2008). Current Challenges in Computer Vision.
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