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Juan Ramon Terven Salinas; Joaquin Salas; Bogdan Raducanu |
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Estado del Arte en Sistemas de Vision Artificial para Personas Invidentes |
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2013 |
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Komputer Sapiens |
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KS |
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1 |
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20-25 |
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OR;MV |
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Admin @ si @ TSR2013 |
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2231 |
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David Masip; Michael S. North ; Alexander Todorov; Daniel N. Osherson |
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Title |
Automated Prediction of Preferences Using Facial Expressions |
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2014 |
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PloS one |
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Plos |
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9 |
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2 |
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e87434 |
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We introduce a computer vision problem from social cognition, namely, the automated detection of attitudes from a person's spontaneous facial expressions. To illustrate the challenges, we introduce two simple algorithms designed to predict observers’ preferences between images (e.g., of celebrities) based on covert videos of the observers’ faces. The two algorithms are almost as accurate as human judges performing the same task but nonetheless far from perfect. Our approach is to locate facial landmarks, then predict preference on the basis of their temporal dynamics. The database contains 768 videos involving four different kinds of preferences. We make it publically available. |
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OR;MV |
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Admin @ si @ MNT2014 |
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2453 |
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R. Clariso; David Masip; A. Rius |
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Student projects empowering mobile learning in higher education |
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2014 |
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Revista de Universidad y Sociedad del Conocimiento |
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RUSC |
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11 |
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192-207 |
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1698-580X |
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OR;MV |
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Admin @ si @ CMR2014 |
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2619 |
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Laura Igual; Joan Carles Soliva; Sergio Escalera; Roger Gimeno; Oscar Vilarroya; Petia Radeva |
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Automatic Brain Caudate Nuclei Segmentation and Classification in Diagnostic of Attention-Deficit/Hyperactivity Disorder |
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2012 |
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Computerized Medical Imaging and Graphics |
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CMIG |
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36 |
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8 |
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591-600 |
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Automatic caudate segmentation; Attention-Deficit/Hyperactivity Disorder; Diagnostic test; Machine learning; Decision stumps; Dissociated dipoles |
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We present a fully automatic diagnostic imaging test for Attention-Deficit/Hyperactivity Disorder diagnosis assistance based on previously found evidences of caudate nucleus volumetric abnormalities. The proposed method consists of different steps: a new automatic method for external and internal segmentation of caudate based on Machine Learning methodologies; the definition of a set of new volume relation features, 3D Dissociated Dipoles, used for caudate representation and classification. We separately validate the contributions using real data from a pediatric population and show precise internal caudate segmentation and discrimination power of the diagnostic test, showing significant performance improvements in comparison to other state-of-the-art methods. |
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OR; HuPBA; MILAB |
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Admin @ si @ ISE2012 |
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2143 |
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Carolina Malagelada; Michal Drozdzal; Santiago Segui; Sara Mendez; Jordi Vitria; Petia Radeva; Javier Santos; Anna Accarino; Juan R. Malagelada; Fernando Azpiroz |
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Classification of functional bowel disorders by objective physiological criteria based on endoluminal image analysis |
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2015 |
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American Journal of Physiology-Gastrointestinal and Liver Physiology |
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AJPGI |
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309 |
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6 |
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G413--G419 |
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capsule endoscopy; computer vision analysis; functional bowel disorders; intestinal motility; machine learning |
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We have previously developed an original method to evaluate small bowel motor function based on computer vision analysis of endoluminal images obtained by capsule endoscopy. Our aim was to demonstrate intestinal motor abnormalities in patients with functional bowel disorders by endoluminal vision analysis. Patients with functional bowel disorders (n = 205) and healthy subjects (n = 136) ingested the endoscopic capsule (Pillcam-SB2, Given-Imaging) after overnight fast and 45 min after gastric exit of the capsule a liquid meal (300 ml, 1 kcal/ml) was administered. Endoluminal image analysis was performed by computer vision and machine learning techniques to define the normal range and to identify clusters of abnormal function. After training the algorithm, we used 196 patients and 48 healthy subjects, completely naive, as test set. In the test set, 51 patients (26%) were detected outside the normal range (P < 0.001 vs. 3 healthy subjects) and clustered into hypo- and hyperdynamic subgroups compared with healthy subjects. Patients with hypodynamic behavior (n = 38) exhibited less luminal closure sequences (41 ± 2% of the recording time vs. 61 ± 2%; P < 0.001) and more static sequences (38 ± 3 vs. 20 ± 2%; P < 0.001); in contrast, patients with hyperdynamic behavior (n = 13) had an increased proportion of luminal closure sequences (73 ± 4 vs. 61 ± 2%; P = 0.029) and more high-motion sequences (3 ± 1 vs. 0.5 ± 0.1%; P < 0.001). Applying an original methodology, we have developed a novel classification of functional gut disorders based on objective, physiological criteria of small bowel function. |
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American Physiological Society |
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MILAB; OR;MV |
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Admin @ si @ MDS2015 |
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2666 |
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