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F. Javier Sanchez, Jorge Bernal, Cristina Sanchez Montes, Cristina Rodriguez de Miguel, & Gloria Fernandez Esparrach. (2017). Bright spot regions segmentation and classification for specular highlights detection in colonoscopy videos. MVAP - Machine Vision and Applications, , 1–20.
Abstract: A novel specular highlights detection method in colonoscopy videos is presented. The method is based on a model of appearance de ning specular
highlights as bright spots which are highly contrasted with respect to adjacent regions. Our approach proposes two stages; segmentation, and then classi cation
of bright spot regions. The former de nes 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 classi cation stage performed by a linear SVM classi er 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.
Keywords: Specular highlights; bright spot regions segmentation; region classification; colonoscopy
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Fosca De Iorio, Carolina Malagelada, Fernando Azpiroz, M. Maluenda, C. Violanti, Laura Igual, et al. (2009). Intestinal motor activity, endoluminal motion and transit. NEUMOT - Neurogastroenterology & Motility, 21(12), 1264–e119.
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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Carolina Malagelada, F.De Lorio, Santiago Segui, S. Mendez, Michal Drozdzal, Jordi Vitria, et al. (2012). Functional gut disorders or disordered gut function? Small bowel dysmotility evidenced by an original technique. NEUMOT - Neurogastroenterology & Motility, 24(3), 223–230.
Abstract: JCR Impact Factor 2010: 3.349
Background This study aimed to determine the proportion of cases with abnormal intestinal motility among patients with functional bowel disorders. To this end, we applied an original method, previously developed in our laboratory, for analysis of endoluminal images obtained by capsule endoscopy. This novel technology is based on computer vision and machine learning techniques.
Methods The endoscopic capsule (Pillcam SB1; Given Imaging, Yokneam, Israel) was administered to 80 patients with functional bowel disorders and 70 healthy subjects. Endoluminal image analysis was performed with a computer vision program developed for the evaluation of contractile events (luminal occlusions and radial wrinkles), non-contractile patterns (open tunnel and smooth wall patterns), type of content (secretions, chyme) and motion of wall and contents. Normality range and discrimination of abnormal cases were established by a machine learning technique. Specifically, an iterative classifier (one-class support vector machine) was applied in a random population of 50 healthy subjects as a training set and the remaining subjects (20 healthy subjects and 80 patients) as a test set.
Key Results The classifier identified as abnormal 29% of patients with functional diseases of the bowel (23 of 80), and as normal 97% of healthy subjects (68 of 70) (P < 0.05 by chi-squared test). Patients identified as abnormal clustered in two groups, which exhibited either a hyper- or a hypodynamic motility pattern. The motor behavior was unrelated to clinical features.
Conclusions & Inferences With appropriate methodology, abnormal intestinal motility can be demonstrated in a significant proportion of patients with functional bowel disorders, implying a pathologic disturbance of gut physiology.
Keywords: capsule endoscopy;computer vision analysis;machine learning technique;small bowel motility
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Hugo Jair Escalante, Victor Ponce, Sergio Escalera, Xavier Baro, Alicia Morales-Reyes, & Jose Martinez-Carranza. (2017). Evolving weighting schemes for the Bag of Visual Words. Neural Computing and Applications - Neural Computing and Applications, 28(5), 925–939.
Abstract: The Bag of Visual Words (BoVW) is an established representation in computer vision. Taking inspiration from text mining, this representation has proved
to be very effective in many domains. However, in most cases, standard term-weighting schemes are adopted (e.g.,term-frequency or TF-IDF). It remains open the question of whether alternative weighting schemes could boost the
performance of methods based on BoVW. More importantly, it is unknown whether it is possible to automatically learn and determine effective weighting schemes from
scratch. This paper brings some light into both of these unknowns. On the one hand, we report an evaluation of the most common weighting schemes used in text mining, but rarely used in computer vision tasks. Besides, we propose an evolutionary algorithm capable of automatically learning weighting schemes for computer vision problems. We report empirical results of an extensive study in several computer vision problems. Results show the usefulness of the proposed method.
Keywords: Bag of Visual Words; Bag of features; Genetic programming; Term-weighting schemes; Computer vision
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Mario Rojas, David Masip, A. Todorov, & Jordi Vitria. (2011). Automatic Prediction of Facial Trait Judgments: Appearance vs. Structural Models. Plos - PloS one, 6(8), e23323.
Abstract: JCR Impact Factor 2010: 4.411
Evaluating other individuals with respect to personality characteristics plays a crucial role in human relations and it is the focus of attention for research in diverse fields such as psychology and interactive computer systems. In psychology, face perception has been recognized as a key component of this evaluation system. Multiple studies suggest that observers use face information to infer personality characteristics. Interactive computer systems are trying to take advantage of these findings and apply them to increase the natural aspect of interaction and to improve the performance of interactive computer systems. Here, we experimentally test whether the automatic prediction of facial trait judgments (e.g. dominance) can be made by using the full appearance information of the face and whether a reduced representation of its structure is sufficient. We evaluate two separate approaches: a holistic representation model using the facial appearance information and a structural model constructed from the relations among facial salient points. State of the art machine learning methods are applied to a) derive a facial trait judgment model from training data and b) predict a facial trait value for any face. Furthermore, we address the issue of whether there are specific structural relations among facial points that predict perception of facial traits. Experimental results over a set of labeled data (9 different trait evaluations) and classification rules (4 rules) suggest that a) prediction of perception of facial traits is learnable by both holistic and structural approaches; b) the most reliable prediction of facial trait judgments is obtained by certain type of holistic descriptions of the face appearance; and c) for some traits such as attractiveness and extroversion, there are relationships between specific structural features and social perceptions
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