|
|
Ignasi Rius, X. Varona, Xavier Roca, & Jordi Gonzalez. (2006). Posture Constraints for Bayesian Human Motion Tracking. In IV Conference on Articulated Motion and Deformable Objects (AMDO´06), LNCS 4069: 414–423.
|
|
|
|
Angel Sappa. (2006). Splitting up Panoramic Range Images into Compact 2½D Representations. International Journal of Imaging Systems and Technology, 16(3): 85–91.
|
|
|
|
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..
|
|
|
|
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).
|
|
|
|
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.
|
|
|
|
Panagiota Spyridonos, Fernando Vilariño, Jordi Vitria, Fernando Azpiroz, & Petia Radeva. (2006). Anisotropic Feature Extraction from Endoluminal Images for Detection of Intestinal Contractions. In and J. Sporring M. N. R. Larsen (Ed.), 9th International Conference on Medical Image Computing and Computer–Assisted Intervention (Vol. 4191, 161–168). LNCS. Berlin Heidelberg: Springer Verlag.
Abstract: Wireless endoscopy is a very recent and at the same time unique technique allowing to visualize and study the occurrence of con- tractions and to analyze the intestine motility. Feature extraction is es- sential for getting efficient patterns to detect contractions in wireless video endoscopy of small intestine. We propose a novel method based on anisotropic image filtering and efficient statistical classification of con- traction features. In particular, we apply the image gradient tensor for mining informative skeletons from the original image and a sequence of descriptors for capturing the characteristic pattern of contractions. Fea- tures extracted from the endoluminal images were evaluated in terms of their discriminatory ability in correct classifying images as either belong- ing to contractions or not. Classification was performed by means of a support vector machine classifier with a radial basis function kernel. Our classification rates gave sensitivity of the order of 90.84% and specificity of the order of 94.43% respectively. These preliminary results highlight the high efficiency of the selected descriptors and support the feasibility of the proposed method in assisting the automatic detection and analysis of contractions.
|
|
|
|
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
|
|
|
|
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
|
|
|
|
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.
|
|
|
|
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.
|
|
|
|
Francisco Javier Orozco, Pau Baiget, Jordi Gonzalez, & Xavier Roca. (2006). Eyelids and Face Tracking in Real-Time.
|
|
|
|
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.
|
|
|
|
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.
|
|
|
|
Fadi Dornaika, & J. Ahlberg. (2006). Fitting 3D face models for tracking and active appearance model training. Image and Vision Computing, 24(9): 1010–1024.
|
|
|
|
Fadi Dornaika, & Franck Davoine. (2006). Facial expression recognition using auto-regressive models.
|
|
