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Jordi Gonzalez, & Thomas B. Moeslund. (2008). Tracking Humans for the Evaluation of their Motion in Image Sequences.
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Juan J. Villanueva. (2008). Visualization, Imaging, and Image Processing,.
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Paramveer S. Dhillon, Francisco Javier Orozco, & Jordi Gonzalez. (2008). Real-Time Monocular Face Tracking Using and Active Camera.
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Eduard Vazquez, & Maria Vanrell. (2008). Eines per al desenvolupament de competencies de enginyeria en un assignatura de Intel·ligencia Artificial.
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Liu Wenyin, Josep Llados, & Jean-Marc Ogier. (2008). Graphics Recognition. Recent Advances and New Opportunities. (Vol. 5046). LNCS.
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Carles Fernandez, Xavier Roca, & Jordi Gonzalez. (2008). Providing Automatic Multilingual Text Generation to Artificial Cognitive Systems.
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X. Varona, Antoni Jaume-i-Capo, Jordi Gonzalez, & Francisco Jose Perales. (2008). Toward Natural Interaction through Visual Recognition of Body Gestures in Real-Time. Interacting with Computers, diu 10,1016/j.intcom.2008.10.001, available on line.
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Robert Benavente, Ernest Valveny, Jaume Garcia, Agata Lapedriza, Miquel Ferrer, & Gemma Sanchez. (2008). Una experiencia de adaptacion al EEES de las asignaturas de programacion en Ingenieria Informatica.
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O. 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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Aymen Azaza. (2018). Context, Motion and Semantic Information for Computational Saliency (Joost Van de Weijer, & Ali Douik, Eds.). Ph.D. thesis, Ediciones Graficas Rey, .
Abstract: The main objective of this thesis is to highlight the salient object in an image or in a video sequence. We address three important—but in our opinion
insufficiently investigated—aspects of saliency detection. Firstly, we start
by extending previous research on saliency which explicitly models the information provided from the context. Then, we show the importance of
explicit context modelling for saliency estimation. Several important works
in saliency are based on the usage of object proposals. However, these methods
focus on the saliency of the object proposal itself and ignore the context.
To introduce context in such saliency approaches, we couple every object
proposal with its direct context. This allows us to evaluate the importance
of the immediate surround (context) for its saliency. We propose several
saliency features which are computed from the context proposals including
features based on omni-directional and horizontal context continuity. Secondly,
we investigate the usage of top-downmethods (high-level semantic
information) for the task of saliency prediction since most computational
methods are bottom-up or only include few semantic classes. We propose
to consider a wider group of object classes. These objects represent important
semantic information which we will exploit in our saliency prediction
approach. Thirdly, we develop a method to detect video saliency by computing
saliency from supervoxels and optical flow. In addition, we apply the
context features developed in this thesis for video saliency detection. The
method combines shape and motion features with our proposed context
features. To summarize, we prove that extending object proposals with their
direct context improves the task of saliency detection in both image and
video data. Also the importance of the semantic information in saliency
estimation is evaluated. Finally, we propose a newmotion feature to detect
saliency in video data. The three proposed novelties are evaluated on standard
saliency benchmark datasets and are shown to improve with respect to
state-of-the-art.
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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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Alfons Juan-Ciscar, & Gemma Sanchez. (2008). PRIS 2008. Pattern Recognition in Information Systems. Proceedings of the 8th international Workshop on Pattern Recognition in Information systems – PRIS 2008, in conjunction with ICEIS 2008.
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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, Ernest Valveny, F. Serratosa, K. Riesen, & Horst Bunke. (2008). An Approximate Algorith for Median Graph Computation using Graph Embedding. In 19th International Conference on Pattern Recognition..
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Dimosthenis Karatzas, Marçal Rusiñol, Coen Antens, & Miquel Ferrer. (2008). Segmentation Robust to the Vignette Effect for Machine Vision Systems. In 19th International Conference on Pattern Recognition.
Abstract: The vignette effect (radial fall-off) is commonly encountered in images obtained through certain image acquisition setups and can seriously hinder automatic analysis processes. In this paper we present a fast and efficient method for dealing with vignetting in the context of object segmentation in an existing industrial inspection setup. The vignette effect is modelled here as a circular, non-linear gradient. The method estimates the gradient parameters and employs them to perform segmentation. Segmentation results on a variety of images indicate that the presented method is able to successfully tackle the vignette effect.
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