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Michal Drozdzal; Santiago Segui; Carolina Malagelada; Fernando Azpiroz; Petia Radeva |
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Title |
Adaptable image cuts for motility inspection using WCE |
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Journal Article |
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2013 |
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Computerized Medical Imaging and Graphics |
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CMIG |
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37 |
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1 |
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72-80 |
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The Wireless Capsule Endoscopy (WCE) technology allows the visualization of the whole small intestine tract. Since the capsule is freely moving, mainly by the means of peristalsis, the data acquired during the study gives a lot of information about the intestinal motility. However, due to: (1) huge amount of frames, (2) complex intestinal scene appearance and (3) intestinal dynamics that make difficult the visualization of the small intestine physiological phenomena, the analysis of the WCE data requires computer-aided systems to speed up the analysis. In this paper, we propose an efficient algorithm for building a novel representation of the WCE video data, optimal for motility analysis and inspection. The algorithm transforms the 3D video data into 2D longitudinal view by choosing the most informative, from the intestinal motility point of view, part of each frame. This step maximizes the lumen visibility in its longitudinal extension. The task of finding “the best longitudinal view” has been defined as a cost function optimization problem which global minimum is obtained by using Dynamic Programming. Validation on both synthetic data and WCE data shows that the adaptive longitudinal view is a good alternative to the traditional motility analysis done by video analysis. The proposed novel data representation a new, holistic insight into the small intestine motility, allowing to easily define and analyze motility events that are difficult to spot by analyzing WCE video. Moreover, the visual inspection of small intestine motility is 4 times faster then by means of video skimming of the WCE. |
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MILAB; OR; 600.046; 605.203 |
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Admin @ si @ DSM2012 |
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2151 |
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Fadi Dornaika; Abdelmalik Moujahid; Bogdan Raducanu |
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Facial expression recognition using tracked facial actions: Classifier performance analysis |
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2013 |
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Engineering Applications of Artificial Intelligence |
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EAAI |
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26 |
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1 |
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467-477 |
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Visual face tracking; 3D deformable models; Facial actions; Dynamic facial expression recognition; Human–computer interaction |
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In this paper, we address the analysis and recognition of facial expressions in continuous videos. More precisely, we study classifiers performance that exploit head pose independent temporal facial action parameters. These are provided by an appearance-based 3D face tracker that simultaneously provides the 3D head pose and facial actions. The use of such tracker makes the recognition pose- and texture-independent. Two different schemes are studied. The first scheme adopts a dynamic time warping technique for recognizing expressions where training data are given by temporal signatures associated with different universal facial expressions. The second scheme models temporal signatures associated with facial actions with fixed length feature vectors (observations), and uses some machine learning algorithms in order to recognize the displayed expression. Experiments quantified the performance of different schemes. These were carried out on CMU video sequences and home-made video sequences. The results show that the use of dimension reduction techniques on the extracted time series can improve the classification performance. Moreover, these experiments show that the best recognition rate can be above 90%. |
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Elsevier |
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OR; 600.046;MV |
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Admin @ si @ DMR2013 |
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2185 |
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Petia Radeva; J. Martinez; A. Tovar; X. Binefa; Jordi Vitria; Juan J. Villanueva |
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CORKIDENT: an automatic vision system for real-time inspection of natural products. |
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1999 |
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Wales |
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OR;MILAB;MV |
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BCNPCL @ bcnpcl @ RMT1999 |
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23 |
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Jordi Vitria; Petia Radeva; X. Binefa |
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EigenHistograms: using low dimensional models of color distribution for real time object recognition |
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1999 |
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Ljubliana, Slovenia, Springer-Verlag |
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OR;MILAB;MV |
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BCNPCL @ bcnpcl @ VRB1999a |
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29 |
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Maria Vanrell; Jordi Vitria; Xavier Roca |
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A multidimensional scaling approach to explore the behavior of a texture perception algorithm. |
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1997 |
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Machine Vision and Applications |
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9 |
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262–271 |
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OR;ISE;CIC;MV |
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BCNPCL @ bcnpcl @ VVR1997 |
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35 |
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