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Bogdan Raducanu; Jordi Vitria; Ales Leonardis |
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Title |
Online pattern recognition and machine learning techniques for computer-vision: Theory and applications |
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2010 |
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Image and Vision Computing |
Abbreviated Journal |
IMAVIS |
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28 |
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7 |
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1063–1064 |
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(Editorial for the Special Issue on Online pattern recognition and machine learning techniques)
In real life, visual learning is supposed to be a continuous process. This paradigm has found its way also in artificial vision systems. There is an increasing trend in pattern recognition represented by online learning approaches, which aims at continuously updating the data representation when new information arrives. Starting with a minimal dataset, the initial knowledge is expanded by incorporating incoming instances, which may have not been previously available or foreseen at the system’s design stage. An interesting characteristic of this strategy is that the train and test phases take place simultaneously. Given the increasing interest in this subject, the aim of this special issue is to be a landmark event in the development of online learning techniques and their applications with the hope that it will capture the interest of a wider audience and will attract even more researchers. We received 19 contributions, of which 9 have been accepted for publication, after having been subjected to usual peer review process. |
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Elsevier |
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0262-8856 |
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OR;MV |
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BCNPCL @ bcnpcl @ RVL2010 |
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1280 |
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Bogdan Raducanu; Jordi Vitria |
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Title |
Learning to Learn: From Smarts Machines to Intelligent Machines |
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Year |
2008 |
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Patter Recognition Letters |
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PRL |
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29 |
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8 |
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1024–1032 |
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OR;MV |
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BCNPCL @ bcnpcl @ RaV2008a |
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950 |
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Author |
Fadi Dornaika; Bogdan Raducanu |
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Title |
Three-Dimensional Face Pose Detection and Tracking Using Monocular Videos: Tool and Application |
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2009 |
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IEEE Transactions on Systems, Man and Cybernetics part B |
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TSMCB |
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39 |
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4 |
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935–944 |
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Recently, we have proposed a real-time tracker that simultaneously tracks the 3-D head pose and facial actions in monocular video sequences that can be provided by low quality cameras. This paper has two main contributions. First, we propose an automatic 3-D face pose initialization scheme for the real-time tracker by adopting a 2-D face detector and an eigenface system. Second, we use the proposed methods-the initialization and tracking-for enhancing the human-machine interaction functionality of an AIBO robot. More precisely, we show how the orientation of the robot's camera (or any active vision system) can be controlled through the estimation of the user's head pose. Applications based on head-pose imitation such as telepresence, virtual reality, and video games can directly exploit the proposed techniques. Experiments on real videos confirm the robustness and usefulness of the proposed methods. |
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OR;MV |
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BCNPCL @ bcnpcl @ DoR2009a |
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1218 |
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Bogdan Raducanu; Jordi Vitria |
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Title |
Face Recognition by Artificial Vision Systems: A Cognitive Perspective |
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2008 |
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International Journal of Pattern Recognition and Artificial Intelligence |
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IJPRAI |
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22 |
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5 |
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899–913 |
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OR;MV |
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BCNPCL @ bcnpcl @ RaV2008b |
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1007 |
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Author |
Bogdan Raducanu; Fadi Dornaika |
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Title |
Texture-independent recognition of facial expressions in image snapshots and videos |
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2013 |
Publication |
Machine Vision and Applications |
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MVA |
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24 |
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4 |
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811-820 |
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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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Springer-Verlag |
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0932-8092 |
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OR; 600.046; 605.203;MV |
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Admin @ si @ RaD2013 |
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2230 |
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