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Fernando Vilariño, Stephan Ameling, Gerard Lacey, Stephen Patchett, & Hugh Mulcahy. (2009). Eye Tracking Search Patterns in Expert and Trainee Colonoscopists: A Novel Method of Assessing Endoscopic Competency? GI - Gastrointestinal Endoscopy, 69(5), 370.
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Rozenn Dhayot, Fernando Vilariño, & Gerard Lacey. (2008). Improving the Quality of Color Colonoscopy Videos. EURASIP JIVP - EURASIP Journal on Image and Video Processing, 139429(1), 1–9.
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Mirko Arnold, Anarta Ghosh, Stephen Ameling, & G Lacey. (2010). Automatic segmentation and inpainting of specular highlights for endoscopic imaging. EURASIP JIVP - EURASIP Journal on Image and Video Processing, 2010(9).
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David Masip, Michael S. North, Alexander Todorov, & Daniel N. Osherson. (2014). Automated Prediction of Preferences Using Facial Expressions. Plos - PloS one, 9(2), e87434.
Abstract: 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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Victor Ponce, Sergio Escalera, Marc Perez, Oriol Janes, & Xavier Baro. (2015). Non-Verbal Communication Analysis in Victim-Offender Mediations. PRL - Pattern Recognition Letters, 67(1), 19–27.
Abstract: We present a non-invasive ambient intelligence framework for the semi-automatic analysis of non-verbal communication applied to the restorative justice field. We propose the use of computer vision and social signal processing technologies in real scenarios of Victim–Offender Mediations, applying feature extraction techniques to multi-modal audio-RGB-depth data. We compute a set of behavioral indicators that define communicative cues from the fields of psychology and observational methodology. We test our methodology on data captured in real Victim–Offender Mediation sessions in Catalonia. We define the ground truth based on expert opinions when annotating the observed social responses. Using different state of the art binary classification approaches, our system achieves recognition accuracies of 86% when predicting satisfaction, and 79% when predicting both agreement and receptivity. Applying a regression strategy, we obtain a mean deviation for the predictions between 0.5 and 0.7 in the range [1–5] for the computed social signals.
Keywords: Victim–Offender Mediation; Multi-modal human behavior analysis; Face and gesture recognition; Social signal processing; Computer vision; Machine learning
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