Fernando Vilariño, Panagiota Spyridonos, Jordi Vitria, C. Malagelada, & Petia Radeva. (2006). Linear Radial Patterns Characterization for Automatic Detection of Tonic Intestinal Contractions. In .F. Mart ́ınez-Trinidad et al (Ed.), 11th Iberoamerican Congress on Pattern Recognition (Vol. 4225, 178–187). LNCS. Berlin Heidelberg: Springer Verlag.
Abstract: This work tackles the categorization of general linear radial patterns by means of the valleys and ridges detection and the use of descriptors of directional information, which are provided by steerable filters in different regions of the image. We successfully apply our proposal in the specific case of automatic detection of tonic contractions in video capsule endoscopy, which represent a paradigmatic example of linear radial patterns.
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Fernando Vilariño, Panagiota Spyridonos, Jordi Vitria, C. Malagelada, & Petia Radeva. (2006). A Machine Learning framework using SOMs: Applications in the Intestinal Motility Assessment. In J.P. Martinez–Trinidad et al (Ed.), 11th Iberoamerican Congress on Pattern Recognition (Vol. 4225, 188–197). LNCS. Berlin-Heidelberg: Springer Verlag.
Abstract: Small Bowel Motility Assessment by means of Wireless Capsule Video Endoscopy constitutes a novel clinical methodology in which a capsule with a micro-camera attached to it is swallowed by the patient, emitting a RF signal which is recorded as a video of its trip throughout the gut. In order to overcome the main drawbacks associated with this technique -mainly related to the large amount of visualization time required-, our efforts have been focused on the development of a machine learning system, built up in sequential stages, which provides the specialists with the useful part of the video, rejecting those parts not valid for analysis. We successfully used Self Organized Maps in a general semi-supervised framework with the aim of tackling the different learning stages of our system. The analysis of the diverse types of images and the automatic detection of intestinal contractions is performed under the perspective of intestinal motility assessment in a clinical environment.
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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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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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Karla Lizbeth Caballero, Joel Barajas, Oriol Pujol, J. Mauri, & Petia Radeva. (2006). Using Radio Frequency Reconstructed IVUS Images in Tissue Classification.
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David Rotger, Petia Radeva, & O. Rodriguez. (2006). Vessel Tortuosity Extraction from IVUS Images.
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Josep Llados. (2006). Computer Vision: Progress of Research and Development ( J. Llados(ed.), Ed.).
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Ellen J.L. Brunenberg, Oriol Pujol, Bart M. Ter Haar Romeny, & Petia Radeva. (2006). Automatic IVUS Segmentation of Atherosclerotic Plaque with Stop & Go Snake.
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Joaquin Salas, P. Martinez, & Jordi Gonzalez. (2006). Background Updating with the Use of Intrinsic Curves. In International Conference on Image Analysis and Recognition (ICIAR´06), LNCS 4141 (A. Campilho et al., eds.), 1: 731–742, ISBN 978–3–540–44891–4.
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