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Author |
Juan Ramon Terven Salinas; Joaquin Salas; Bogdan Raducanu |
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Title |
Estado del Arte en Sistemas de Vision Artificial para Personas Invidentes |
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Journal |
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Year |
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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no |
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Call Number |
Admin @ si @ TSR2013 |
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2231 |
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Author |
Bogdan Raducanu; D. Gatica-Perez |
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Title |
Inferring competitive role patterns in reality TV show through nonverbal analysis |
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Journal Article |
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Year |
2012 |
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Multimedia Tools and Applications |
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MTAP |
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Volume |
56 |
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1 |
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207-226 |
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This paper introduces a new facet of social media, namely that depicting social interaction. More concretely, we address this problem from the perspective of nonverbal behavior-based analysis of competitive meetings. For our study, we made use of “The Apprentice” reality TV show, which features a competition for a real, highly paid corporate job. Our analysis is centered around two tasks regarding a person's role in a meeting: predicting the person with the highest status, and predicting the fired candidates. We address this problem by adopting both supervised and unsupervised strategies. The current study was carried out using nonverbal audio cues. Our approach is based only on the nonverbal interaction dynamics during the meeting without relying on the spoken words. The analysis is based on two types of data: individual and relational measures. Results obtained from the analysis of a full season of the show are promising (up to 85.7% of accuracy in the first case and up to 92.8% in the second case). Our approach has been conveniently compared with the Influence Model, demonstrating its superiority. |
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Elsevier |
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1380-7501 |
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OR;MV |
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no |
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BCNPCL @ bcnpcl @ RaG2012 |
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1360 |
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Author |
Bogdan Raducanu; Fadi Dornaika |
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Title |
Texture-independent recognition of facial expressions in image snapshots and videos |
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Journal Article |
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2013 |
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Machine Vision and Applications |
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MVA |
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24 |
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4 |
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811-820 |
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This paper addresses the static and dynamic recognition of basic facial expressions. It has two main contributions. First, we introduce a view- and texture-independent scheme that exploits facial action parameters estimated by an appearance-based 3D face tracker. We represent the learned facial actions associated with different facial expressions by time series. Second, we compare this dynamic scheme with a static one based on analyzing individual snapshots and show that the former performs better than the latter. We provide evaluations of performance using three subspace learning techniques: linear discriminant analysis, non-parametric discriminant analysis and support vector machines. |
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Springer-Verlag |
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0932-8092 |
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OR; 600.046; 605.203;MV |
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Admin @ si @ RaD2013 |
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2230 |
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F. Javier Sanchez; Jorge Bernal; Cristina Sanchez Montes; Cristina Rodriguez de Miguel; Gloria Fernandez Esparrach |
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Title |
Bright spot regions segmentation and classification for specular highlights detection in colonoscopy videos |
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Journal Article |
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2017 |
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Machine Vision and Applications |
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MVAP |
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1-20 |
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Keywords |
Specular highlights; bright spot regions segmentation; region classification; colonoscopy |
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Abstract |
A novel specular highlights detection method in colonoscopy videos is presented. The method is based on a model of appearance dening specular
highlights as bright spots which are highly contrasted with respect to adjacent regions. Our approach proposes two stages; segmentation, and then classication
of bright spot regions. The former denes a set of candidate regions obtained through a region growing process with local maxima as initial region seeds. This process creates a tree structure which keeps track, at each growing iteration, of the region frontier contrast; nal regions provided depend on restrictions over contrast value. Non-specular regions are ltered through a classication stage performed by a linear SVM classier using model-based features from each region. We introduce a new validation database with more than 25; 000 regions along with their corresponding pixel-wise annotations. We perform a comparative study against other approaches. Results show that our method is superior to other approaches, with our segmented regions being
closer to actual specular regions in the image. Finally, we also present how our methodology can also be used to obtain an accurate prediction of polyp histology. |
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MV; 600.096; 600.175 |
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Admin @ si @ SBS2017 |
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2975 |
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Author |
Fosca De Iorio; Carolina Malagelada; Fernando Azpiroz; M. Maluenda; C. Violanti; Laura Igual; Jordi Vitria; Juan R. Malagelada |
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Title |
Intestinal motor activity, endoluminal motion and transit |
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Journal Article |
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Year |
2009 |
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Neurogastroenterology & Motility |
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NEUMOT |
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21 |
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12 |
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1264–e119 |
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Abstract |
A programme for evaluation of intestinal motility has been recently developed based on endoluminal image analysis using computer vision methodology and machine learning techniques. Our aim was to determine the effect of intestinal muscle inhibition on wall motion, dynamics of luminal content and transit in the small bowel. Fourteen healthy subjects ingested the endoscopic capsule (Pillcam, Given Imaging) in fasting conditions. Seven of them received glucagon (4.8 microg kg(-1) bolus followed by a 9.6 microg kg(-1) h(-1) infusion during 1 h) and in the other seven, fasting activity was recorded, as controls. This dose of glucagon has previously shown to inhibit both tonic and phasic intestinal motor activity. Endoluminal image and displacement was analyzed by means of a computer vision programme specifically developed for the evaluation of muscular activity (contractile and non-contractile patterns), intestinal contents, endoluminal motion and transit. Thirty-minute periods before, during and after glucagon infusion were analyzed and compared with equivalent periods in controls. No differences were found in the parameters measured during the baseline (pretest) periods when comparing glucagon and control experiments. During glucagon infusion, there was a significant reduction in contractile activity (0.2 +/- 0.1 vs 4.2 +/- 0.9 luminal closures per min, P < 0.05; 0.4 +/- 0.1 vs 3.4 +/- 1.2% of images with radial wrinkles, P < 0.05) and a significant reduction of endoluminal motion (82 +/- 9 vs 21 +/- 10% of static images, P < 0.05). Endoluminal image analysis, by means of computer vision and machine learning techniques, can reliably detect reduced intestinal muscle activity and motion. |
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OR;MILAB;MV |
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BCNPCL @ bcnpcl @ DMA2009 |
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1251 |
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