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Author |
Juan Ramon Terven Salinas; Joaquin Salas; Bogdan Raducanu |
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Title |
New Opportunities for Computer Vision-Based Assistive Technology Systems for the Visually Impaired |
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Journal Article |
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Year |
2014 |
Publication |
Computer |
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COMP |
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47 |
Issue |
4 |
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52-58 |
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Computing advances and increased smartphone use gives technology system designers greater flexibility in exploiting computer vision to support visually impaired users. Understanding these users' needs will certainly provide insight for the development of improved usability of computing devices. |
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0018-9162 |
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OR;MV |
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Admin @ si @ TSR2014a |
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2317 |
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Author |
Fadi Dornaika; Abdelmalik Moujahid; Bogdan Raducanu |
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Title |
Facial expression recognition using tracked facial actions: Classifier performance analysis |
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Journal Article |
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Year |
2013 |
Publication |
Engineering Applications of Artificial Intelligence |
Abbreviated Journal |
EAAI |
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26 |
Issue |
1 |
Pages |
467-477 |
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Keywords |
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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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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Journal Article |
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Year |
2010 |
Publication |
Image and Vision Computing |
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IMAVIS |
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28 |
Issue |
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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0262-8856 |
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OR;MV |
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BCNPCL @ bcnpcl @ RVL2010 |
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1280 |
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Author |
Bogdan Raducanu; Fadi Dornaika |
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Title |
A Supervised Non-linear Dimensionality Reduction Approach for Manifold Learning |
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Journal Article |
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Year |
2012 |
Publication |
Pattern Recognition |
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PR |
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45 |
Issue |
6 |
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2432-2444 |
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IF= 2.61
IF=2.61 (2010)
In this paper we introduce a novel supervised manifold learning technique called Supervised Laplacian Eigenmaps (S-LE), which makes use of class label information to guide the procedure of non-linear dimensionality reduction by adopting the large margin concept. The graph Laplacian is split into two components: within-class graph and between-class graph to better characterize the discriminant property of the data. Our approach has two important characteristics: (i) it adaptively estimates the local neighborhood surrounding each sample based on data density and similarity and (ii) the objective function simultaneously maximizes the local margin between heterogeneous samples and pushes the homogeneous samples closer to each other.
Our approach has been tested on several challenging face databases and it has been conveniently compared with other linear and non-linear techniques, demonstrating its superiority. Although we have concentrated in this paper on the face recognition problem, the proposed approach could also be applied to other category of objects characterized by large variations in their appearance (such as hand or body pose, for instance. |
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Elsevier |
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0031-3203 |
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OR; MV |
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Admin @ si @ RaD2012a |
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1884 |
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Author |
Jorge Bernal; F. Javier Sanchez; Fernando Vilariño |
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Title |
Towards Automatic Polyp Detection with a Polyp Appearance Model |
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Journal Article |
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Year |
2012 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
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Volume |
45 |
Issue |
9 |
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3166-3182 |
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Keywords |
Colonoscopy,PolypDetection,RegionSegmentation,SA-DOVA descriptot |
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This work aims at the automatic polyp detection by using a model of polyp appearance in the context of the analysis of colonoscopy videos. Our method consists of three stages: region segmentation, region description and region classification. The performance of our region segmentation method guarantees that if a polyp is present in the image, it will be exclusively and totally contained in a single region. The output of the algorithm also defines which regions can be considered as non-informative. We define as our region descriptor the novel Sector Accumulation-Depth of Valleys Accumulation (SA-DOVA), which provides a necessary but not sufficient condition for the polyp presence. Finally, we classify our segmented regions according to the maximal values of the SA-DOVA descriptor. Our preliminary classification results are promising, especially when classifying those parts of the image that do not contain a polyp inside. |
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Elsevier |
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0031-3203 |
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800 |
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IbPRIA |
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MV;SIAI |
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Admin @ si @ BSV2012; IAM @ iam |
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1997 |
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