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Author |
R. Valenti; Theo Gevers |
![goto web page (via DOI) doi](http://refbase.cvc.uab.es/img/doi.gif)
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Title |
Combining Head Pose and Eye Location Information for Gaze Estimation |
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Journal Article |
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2012 |
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IEEE Transactions on Image Processing |
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TIP |
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21 |
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2 |
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802-815 |
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Abstract |
Impact factor 2010: 2.92
Impact factor 2011/12?: 3.32
Head pose and eye location for gaze estimation have been separately studied in numerous works in the literature. Previous research shows that satisfactory accuracy in head pose and eye location estimation can be achieved in constrained settings. However, in the presence of nonfrontal faces, eye locators are not adequate to accurately locate the center of the eyes. On the other hand, head pose estimation techniques are able to deal with these conditions; hence, they may be suited to enhance the accuracy of eye localization. Therefore, in this paper, a hybrid scheme is proposed to combine head pose and eye location information to obtain enhanced gaze estimation. To this end, the transformation matrix obtained from the head pose is used to normalize the eye regions, and in turn, the transformation matrix generated by the found eye location is used to correct the pose estimation procedure. The scheme is designed to enhance the accuracy of eye location estimations, particularly in low-resolution videos, to extend the operative range of the eye locators, and to improve the accuracy of the head pose tracker. These enhanced estimations are then combined to obtain a novel visual gaze estimation system, which uses both eye location and head information to refine the gaze estimates. From the experimental results, it can be derived that the proposed unified scheme improves the accuracy of eye estimations by 16% to 23%. Furthermore, it considerably extends its operating range by more than 15° by overcoming the problems introduced by extreme head poses. Moreover, the accuracy of the head pose tracker is improved by 12% to 24%. Finally, the experimentation on the proposed combined gaze estimation system shows that it is accurate (with a mean error between 2° and 5°) and that it can be used in cases where classic approaches would fail without imposing restraints on the position of the head. |
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1057-7149 |
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Admin @ si @ VaG 2012b |
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1851 |
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Author |
A. Diplaros; N. Vlassis; Theo Gevers |
![find record details (via OpenURL) openurl](http://refbase.cvc.uab.es/img/xref.gif)
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Title |
A Spatially Constrained Generative Model and an EM Algorithm for Image Segmentation |
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2007 |
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IEEE Transactions on Neural Networks |
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18 |
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3 |
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798-808 |
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Admin @ si @ DVG2007 |
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947 |
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Author |
Ivan Huerta; Marco Pedersoli; Jordi Gonzalez; Alberto Sanfeliu |
![goto web page (via DOI) doi](http://refbase.cvc.uab.es/img/doi.gif)
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Title |
Combining where and what in change detection for unsupervised foreground learning in surveillance |
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Journal Article |
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2015 |
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Pattern Recognition |
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PR |
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48 |
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3 |
Pages ![sorted by First Page field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
709-719 |
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Object detection; Unsupervised learning; Motion segmentation; Latent variables; Support vector machine; Multiple appearance models; Video surveillance |
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Change detection is the most important task for video surveillance analytics such as foreground and anomaly detection. Current foreground detectors learn models from annotated images since the goal is to generate a robust foreground model able to detect changes in all possible scenarios. Unfortunately, manual labelling is very expensive. Most advanced supervised learning techniques based on generic object detection datasets currently exhibit very poor performance when applied to surveillance datasets because of the unconstrained nature of such environments in terms of types and appearances of objects. In this paper, we take advantage of change detection for training multiple foreground detectors in an unsupervised manner. We use statistical learning techniques which exploit the use of latent parameters for selecting the best foreground model parameters for a given scenario. In essence, the main novelty of our proposed approach is to combine the where (motion segmentation) and what (learning procedure) in change detection in an unsupervised way for improving the specificity and generalization power of foreground detectors at the same time. We propose a framework based on latent support vector machines that, given a noisy initialization based on motion cues, learns the correct position, aspect ratio, and appearance of all moving objects in a particular scene. Specificity is achieved by learning the particular change detections of a given scenario, and generalization is guaranteed since our method can be applied to any possible scene and foreground object, as demonstrated in the experimental results outperforming the state-of-the-art. |
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ISE; 600.063; 600.078 |
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Admin @ si @ HPG2015 |
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2589 |
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Author |
Arjan Gijsenij; R. Lu; Theo Gevers; De Xu |
![goto web page (via DOI) doi](http://refbase.cvc.uab.es/img/doi.gif)
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Title |
Color Constancy for Multiple Light Source |
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Journal Article |
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2012 |
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IEEE Transactions on Image Processing |
Abbreviated Journal |
TIP |
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21 |
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2 |
Pages ![sorted by First Page field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
697-707 |
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Impact factor 2010: 2.92
Impact factor 2011/2012?: 3.32
Color constancy algorithms are generally based on the simplifying assumption that the spectral distribution of a light source is uniform across scenes. However, in reality, this assumption is often violated due to the presence of multiple light sources. In this paper, we will address more realistic scenarios where the uniform light-source assumption is too restrictive. First, a methodology is proposed to extend existing algorithms by applying color constancy locally to image patches, rather than globally to the entire image. After local (patch-based) illuminant estimation, these estimates are combined into more robust estimations, and a local correction is applied based on a modified diagonal model. Quantitative and qualitative experiments on spectral and real images show that the proposed methodology reduces the influence of two light sources simultaneously present in one scene. If the chromatic difference between these two illuminants is more than 1° , the proposed framework outperforms algorithms based on the uniform light-source assumption (with error-reduction up to approximately 30%). Otherwise, when the chromatic difference is less than 1° and the scene can be considered to contain one (approximately) uniform light source, the performance of the proposed method framework is similar to global color constancy methods. |
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1057-7149 |
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Admin @ si @ GLG2012a |
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1852 |
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Author |
Arjan Gijsenij; Theo Gevers |
![goto web page (via DOI) doi](http://refbase.cvc.uab.es/img/doi.gif)
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Title |
Color Constancy Using Natural Image Statistics and Scene Semantics |
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Journal Article |
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2011 |
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IEEE Transactions on Pattern Analysis and Machine Intelligence |
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TPAMI |
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33 |
Issue |
4 |
Pages ![sorted by First Page field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
687-698 |
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Existing color constancy methods are all based on specific assumptions such as the spatial and spectral characteristics of images. As a consequence, no algorithm can be considered as universal. However, with the large variety of available methods, the question is how to select the method that performs best for a specific image. To achieve selection and combining of color constancy algorithms, in this paper natural image statistics are used to identify the most important characteristics of color images. Then, based on these image characteristics, the proper color constancy algorithm (or best combination of algorithms) is selected for a specific image. To capture the image characteristics, the Weibull parameterization (e.g., grain size and contrast) is used. It is shown that the Weibull parameterization is related to the image attributes to which the used color constancy methods are sensitive. An MoG-classifier is used to learn the correlation and weighting between the Weibull-parameters and the image attributes (number of edges, amount of texture, and SNR). The output of the classifier is the selection of the best performing color constancy method for a certain image. Experimental results show a large improvement over state-of-the-art single algorithms. On a data set consisting of more than 11,000 images, an increase in color constancy performance up to 20 percent (median angular error) can be obtained compared to the best-performing single algorithm. Further, it is shown that for certain scene categories, one specific color constancy algorithm can be used instead of the classifier considering several algorithms. |
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0162-8828 |
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Admin @ si @ GiG2011 |
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1724 |
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