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Angel Sappa, & Boris X. Vintimilla. (2006). Edge Point Linking by Means of Global and Local Schemes. In IEEE Int. Conf. on Signal-Image Technology and Internet-Based Systems, Hammamet, Tunisia, December 2006, pp. 551-560..
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Jordi Vitria, M. Bressan, & Petia Radeva. (2006). Bayesian classification of cork stoppers using class-conditional independent component analysis. IEEE Transactions on Systems, Man and Cybernetics (Part C), 36(6).
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Karla Lizbeth Caballero, Joel Barajas, Oriol Pujol, Neus Salvatella, & Petia Radeva. (2006). In-Vivo IVUS Tissue Classification: A Comparison Between RF Signal Analysis and Reconstructed Images. In 11th Iberoamerican Congress on Pattern Recognition (CIARP´06), LNCS 4225: 137–146.
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Fernando Vilariño, Panagiota Spyridonos, Jordi Vitria, Fernando Azpiroz, & Petia Radeva. (2006). Cascade analysis for intestinal contraction detection. In 20th International Congress and exhibition Computer Assisted Radiology and Surgery (pp. 9–10).
Abstract: In this work, we address the study of intestinal contractions in a novel approach based on a machine learning framework to process data from Wireless Capsule Video Endoscopy. Wireless endoscopy represents a unique way to visualize the intestine motility by creating long videos to visualize intestine dynamics. In this paper we argue that to analyze huge amount of wireless endoscopy data and define robust methods for contraction detection we should base our approach on sophisticated machine learning techniques. In particular, we propose a cascade of classifiers in order to remove different physiological phenomenon and obtain the motility pattern of small intestines. Our results show obtaining high specificity and sensitivity rates that highlight the high efficiency of the selected approach and support the feasibility of the proposed methodology in the automatic detection and analysis of intestine contractions.
Keywords: intestine video analysis, anisotropic features, support vector machine, cascade of classifiers
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Fernando Vilariño, Panagiota Spyridonos, Jordi Vitria, Fernando Azpiroz, & Petia Radeva. (2006). Automatic Detection of Intestinal Juices in Wireless Capsule Video Endoscopy. In 18th International Conference on Pattern Recognition (Vol. 4, pp. 719–722).
Abstract: Wireless capsule video endoscopy is a novel and challenging clinical technique, whose major reported drawback relates to the high amount of time needed for video visualization. In this paper, we propose a method for the rejection of the parts of the video resulting not valid for analysis by means of automatic detection of intestinal juices. We applied Gabor filters for the characterization of the bubble-like shape of intestinal juices in fasting patients. Our method achieves a significant reduction in visualization time, with no relevant loss of valid frames. The proposed approach is easily extensible to other image analysis scenarios where the described pattern of bubbles can be found.
Keywords: Clinical diagnosis , Endoscopes , Fluids and secretions , Gabor filters , Hospitals , Image sequence analysis , Intestines , Lighting , Shape , Visualization
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Francisco Javier Orozco, Pau Baiget, Jordi Gonzalez, & Xavier Roca. (2006). Eyelids and Face Tracking in Real-Time.
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Salim Jouili, Salvatore Tabbone, & Ernest Valveny. (2010). Comparing Graph Similarity Measures for Graphical Recognition. In Graphics Recognition. Achievements, Challenges, and Evolution. 8th International Workshop, GREC 2009. Selected Papers (Vol. 6020, pp. 37–48). LNCS. Springer Berlin Heidelberg.
Abstract: In this paper we evaluate four graph distance measures. The analysis is performed for document retrieval tasks. For this aim, different kind of documents are used including line drawings (symbols), ancient documents (ornamental letters), shapes and trademark-logos. The experimental results show that the performance of each graph distance measure depends on the kind of data and the graph representation technique.
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Fadi Dornaika, & Franck Davoine. (2006). On appearance based face and facial action tracking. IEEE Transactions on Circuits and Systems for Video Technology, 16(9): 1838–1853.
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Fadi Dornaika, & J. Ahlberg. (2006). Fitting 3D face models for tracking and active appearance model training. Image and Vision Computing, 24(9): 1010–1024.
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Fadi Dornaika, & Franck Davoine. (2006). Facial expression recognition using auto-regressive models.
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R. Herault, Franck Davoine, Fadi Dornaika, & Y. Grandvalet. (2006). Simultaneous and robust face and facial action tracking.
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David Aldavert. (2006). Visual Simultaneous Localization and Mapping.
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David Geronimo. (2006). Model Features and Horizon Line Estimation for Pedestrian Detection in Advanced Driver Assistance Systems. Master's thesis, , .
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Fernando Vilariño. (2006). A Machine Learning Approach for Intestinal Motility Assessment with Capsule Endoscopy (Petia Radeva, Ed.). Ph.D. thesis, , .
Abstract: Intestinal motility assessment with video capsule endoscopy arises as a novel and challenging clinical fieldwork. This technique is based on the analysis of the patterns of intestinal contractions obtained by labelling all the motility events present in a video provided by a capsule with a wireless micro-camera, which is ingested by the patient. However, the visual analysis of these video sequences presents several im- portant drawbacks, mainly related to both the large amount of time needed for the visualization process, and the low prevalence of intestinal contractions in video.
In this work we propose a machine learning system to automatically detect the intestinal contractions in video capsule endoscopy, driving a very useful but not fea- sible clinical routine into a feasible clinical procedure. Our proposal is divided into two different parts: The first part tackles the problem of the automatic detection of phasic contractions in capsule endoscopy videos. Phasic contractions are dynamic events spanning about 4-5 seconds, which show visual patterns with a high variability. Our proposal is based on a sequential design which involves the analysis of textural, color and blob features with powerful classifiers such as SVM. This approach appears to cope with two basic aims: the reduction of the imbalance rate of the data set, and the modular construction of the system, which adds the capability of including domain knowledge as new stages in the cascade. The second part of the current work tackles the problem of the automatic detection of tonic contractions. Tonic contrac- tions manifest in capsule endoscopy as a sustained pattern of the folds and wrinkles of the intestine, which may be prolonged for an undetermined span of time. Our proposal is based on the analysis of the wrinkle patterns, presenting a comparative study of diverse features and classification methods, and providing a set of appro- priate descriptors for their characterization. We provide a detailed analysis of the performance achieved by our system both in a qualitative and a quantitative way.
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German Ros, J. Guerrero, Angel Sappa, Daniel Ponsa, & Antonio Lopez. (2013). Fast and Robust l1-averaging-based Pose Estimation for Driving Scenarios. In 24th British Machine Vision Conference.
Abstract: Robust visual pose estimation is at the core of many computer vision applications, being fundamental for Visual SLAM and Visual Odometry problems. During the last decades, many approaches have been proposed to solve these problems, being RANSAC one of the most accepted and used. However, with the arrival of new challenges, such as large driving scenarios for autonomous vehicles, along with the improvements in the data gathering frameworks, new issues must be considered. One of these issues is the capability of a technique to deal with very large amounts of data while meeting the realtime
constraint. With this purpose in mind, we present a novel technique for the problem of robust camera-pose estimation that is more suitable for dealing with large amount of data, which additionally, helps improving the results. The method is based on a combination of a very fast coarse-evaluation function and a robust ℓ1-averaging procedure. Such scheme leads to high-quality results while taking considerably less time than RANSAC.
Experimental results on the challenging KITTI Vision Benchmark Suite are provided, showing the validity of the proposed approach.
Keywords: SLAM
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