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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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Stefan Ameling, Stephan Wirth, Dietrich Paulus, Gerard Lacey, & Fernando Vilariño. (2009). Texture-based Polyp Detection in Colonoscopy. Proc. BILDVERARBEITUNG FÜR DIE MEDIZIN, .
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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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Jorge Bernal. (2014). Polyp Localization and Segmentation in Colonoscopy Images by Means of a Model of Appearance for Polyps. ELCVIA - Electronic Letters on Computer Vision and Image Analysis, 13(2), 9–10.
Abstract: Colorectal cancer is the fourth most common cause of cancer death worldwide and its survival rate depends on the stage in which it is detected on hence the necessity for an early colon screening. There are several screening techniques but colonoscopy is still nowadays the gold standard, although it has some drawbacks such as the miss rate. Our contribution, in the field of intelligent systems for colonoscopy, aims at providing a polyp localization and a polyp segmentation system based on a model of appearance for polyps. To develop both methods we define a model of appearance for polyps, which describes a polyp as enclosed by intensity valleys. The novelty of our contribution resides on the fact that we include in our model aspects of the image formation and we also consider the presence of other elements from the endoluminal scene such as specular highlights and blood vessels, which have an impact on the performance of our methods. In order to develop our polyp localization method we accumulate valley information in order to generate energy maps, which are also used to guide the polyp segmentation. Our methods achieve promising results in polyp localization and segmentation. As we want to explore the usability of our methods we present a comparative analysis between physicians fixations obtained via an eye tracking device and our polyp localization method. The results show that our method is indistinguishable to novice physicians although it is far from expert physicians.
Keywords: Colonoscopy; polyp localization; polyp segmentation; Eye-tracking
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