2014 |
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R. Clariso, David Masip, & A. Rius. (2014). Student projects empowering mobile learning in higher education. RUSC - Revista de Universidad y Sociedad del Conocimiento, 192–207.
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Santiago Segui, Michal Drozdzal, Ekaterina Zaytseva, Fernando Azpiroz, Petia Radeva, & Jordi Vitria. (2014). Detection of wrinkle frames in endoluminal videos using betweenness centrality measures for images. TITB - IEEE Transactions on Information Technology in Biomedicine, 18(6), 1831–1838.
Abstract: Intestinal contractions are one of the most important events to diagnose motility pathologies of the small intestine. When visualized by wireless capsule endoscopy (WCE), the sequence of frames that represents a contraction is characterized by a clear wrinkle structure in the central frames that corresponds to the folding of the intestinal wall. In this paper we present a new method to robustly detect wrinkle frames in full WCE videos by using a new mid-level image descriptor that is based on a centrality measure proposed for graphs. We present an extended validation, carried out in a very large database, that shows that the proposed method achieves state of the art performance for this task.
Keywords: Wireless Capsule Endoscopy; Small Bowel Motility Dysfunction; Contraction Detection; Structured Prediction; Betweenness Centrality
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2013 |
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Bogdan Raducanu, & Fadi Dornaika. (2013). Texture-independent recognition of facial expressions in image snapshots and videos. MVA - Machine Vision and Applications, 24(4), 811–820.
Abstract: 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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Fadi Dornaika, Abdelmalik Moujahid, & Bogdan Raducanu. (2013). Facial expression recognition using tracked facial actions: Classifier performance analysis. EAAI - Engineering Applications of Artificial Intelligence, 26(1), 467–477.
Abstract: 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%.
Keywords: Visual face tracking; 3D deformable models; Facial actions; Dynamic facial expression recognition; Human–computer interaction
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Juan Ramon Terven Salinas, Joaquin Salas, & Bogdan Raducanu. (2013). Estado del Arte en Sistemas de Vision Artificial para Personas Invidentes. KS - Komputer Sapiens, 20–25.
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