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Fadi Dornaika, & Bogdan Raducanu. (2008). Facial Expression Recognition for HCI Applications. In Rabuñal (Ed.), Encyclopedia of Artificial Intelligence (Vol. II, 625–631). IGI–Global Publisher.
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Bogdan Raducanu, & Fadi Dornaika. (2008). Dynamic Vs. Static Recognition of Facial Expressions. In Rabuñal (Ed.), Ambient Intelligence. European Conference (Vol. 5355, 13–25). LNCS.
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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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Carlo Gatta, Oriol Pujol, O. 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, O. Rodriguez-Leor, Josefina 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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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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C. Malagelada, Fosca De Iorio, Fernando Azpiroz, Anna Accarino, Santiago Segui, Petia Radeva, et al. (2008). New Insight Into Intestinal Motor Function via Noninvasive Endoluminal Image Analysis. Gastroenterology, 1155–1162.
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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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Sergio Escalera, Oriol Pujol, Eric Laciar, Jordi Vitria, Esther Pueyo, & Petia Radeva. (2008). Coronary Damage Classification of Patients with the Chagas Disease with Error-Correcting Output Codes. In Intelligent Systems, 4th International IEEE Conference, 6–8 setembre 2008. (Vol. 2, 12–17).
Abstract: The Chagaspsila disease is endemic in all Latin America, affecting millions of people in the continent. In order to diagnose and treat the Chagaspsila disease, it is important to detect and measure the coronary damage of the patient. In this paper, we analyze and categorize patients into different groups based on the coronary damage produced by the disease. Based on the features of the heart cycle extracted using high resolution ECG, a multi-class scheme of error-correcting output codes (ECOC) is formulated and successfully applied. The results show that the proposed scheme obtains significant performance improvements compared to previous works and state-of-the-art ECOC designs.
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Agata Lapedriza, David Masip, & Jordi Vitria. (2008). On the Use of Independent Tasks for Face Recognition. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition (1–6).
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Carme Julia, Angel Sappa, Felipe Lumbreras, Joan Serrat, & Antonio Lopez. (2008). An Adapted Alternation Approach for Recommender Systems. In IEEE International Conference on e–Business Engineering, (128–135).
Abstract: This paper presents an adaptation of the alternation technique to tackle the prediction task in recommender systems. These systems are widely considered in electronic commerce to help customers to find products they will probably like or dislike. As the SVD-based approaches, the proposed adapted alternation technique uses all the information stored in the system to find the predictions. The main advantage of this technique with respect to the SVD-based ones is that it can deal with missing data. Furthermore, it has a smaller computational cost. Experimental results with public data sets are provided in order to show the viability of the proposed adapted alternation approach.
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Jose Manuel Alvarez, Antonio Lopez, & Ramon Baldrich. (2008). Illuminant Invariant Model-Based Road Segmentation. In IEEE Intelligent Vehicles Symposium, (1155–1180).
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Fadi Dornaika, & Angel Sappa. (2007). Improving Appearance-Based 3D Face Tracking Using Sparse Stereo Data. In H. Araujo and J. Jorge A. R. J. Braz (Ed.), Advances in Computer Graphics and Computer Vision, (354–366). Springer Verlag.
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Partha Pratim Roy, Umapada Pal, & Josep Llados. (2008). Multi-oriented English Text Line Extraction using Background and Foreground Information. In Proceedings of the 8th IAPR International Workshop on Document Analysis Systems, (315–322).
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Partha Pratim Roy, Umapada Pal, & Josep Llados. (2008). Morphology Based Handwritten Line Segmentation using Foreground and Background Information. In International Conference on Frontiers in Handwriting Recognition, (241–246).
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