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R. Valenti; N. Sebe; Theo Gevers |
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Title |
What are you looking at? Improving Visual gaze Estimation by Saliency |
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2012 |
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International Journal of Computer Vision |
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IJCV |
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98 |
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3 |
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324-334 |
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Impact factor 2010: 5.15
Impact factor 2011/12?: 5.36
In this paper we present a novel mechanism to obtain enhanced gaze estimation for subjects looking at a scene or an image. The system makes use of prior knowledge about the scene (e.g. an image on a computer screen), to define a probability map of the scene the subject is gazing at, in order to find the most probable location. The proposed system helps in correcting the fixations which are erroneously estimated by the gaze estimation device by employing a saliency framework to adjust the resulting gaze point vector. The system is tested on three scenarios: using eye tracking data, enhancing a low accuracy webcam based eye tracker, and using a head pose tracker. The correlation between the subjects in the commercial eye tracking data is improved by an average of 13.91%. The correlation on the low accuracy eye gaze tracker is improved by 59.85%, and for the head pose tracker we obtain an improvement of 10.23%. These results show the potential of the system as a way to enhance and self-calibrate different visual gaze estimation systems. |
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ALTRES;ISE |
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no |
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Admin @ si @ VSG2012 |
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1848 |
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Xavier Boix; Josep M. Gonfaus; Joost Van de Weijer; Andrew Bagdanov; Joan Serrat; Jordi Gonzalez |
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Title |
Harmony Potentials: Fusing Global and Local Scale for Semantic Image Segmentation |
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Journal Article |
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2012 |
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International Journal of Computer Vision |
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IJCV |
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96 |
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1 |
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83-102 |
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The Hierarchical Conditional Random Field(HCRF) model have been successfully applied to a number of image labeling problems, including image segmentation. However, existing HCRF models of image segmentation do not allow multiple classes to be assigned to a single region, which limits their ability to incorporate contextual information across multiple scales.
At higher scales in the image, this representation yields an oversimpli ed model since multiple classes can be reasonably expected to appear within large regions. This simpli ed model particularly limits the impact of information at higher scales. Since class-label information at these scales is usually more reliable than at lower, noisier scales, neglecting this information is undesirable. To
address these issues, we propose a new consistency potential for image labeling problems, which we call the harmony potential. It can encode any possible combi-
nation of labels, penalizing only unlikely combinations of classes. We also propose an e ective sampling strategy over this expanded label set that renders tractable the underlying optimization problem. Our approach obtains state-of-the-art results on two challenging, standard benchmark datasets for semantic image segmentation: PASCAL VOC 2010, and MSRC-21. |
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CAT;ISE;CIC;ADAS |
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Admin @ si @ BGW2012 |
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1718 |
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Y. Mori; M.Misawa; Jorge Bernal; M. Bretthauer; S.Kudo; A. Rastogi; Gloria Fernandez Esparrach |
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Title |
Artificial Intelligence for Disease Diagnosis-the Gold Standard Challenge |
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2022 |
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Gastrointestinal Endoscopy |
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96 |
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2 |
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370-372 |
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Admin @ si @ MMB2022 |
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3701 |
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Jose Manuel Alvarez; Theo Gevers; Antonio Lopez |
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Title |
Learning photometric invariance for object detection |
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Journal Article |
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2010 |
Publication |
International Journal of Computer Vision |
Abbreviated Journal |
IJCV |
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90 |
Issue |
1 |
Pages |
45-61 |
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Keywords |
road detection |
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Abstract |
Impact factor: 3.508 (the last available from JCR2009SCI). Position 4/103 in the category Computer Science, Artificial Intelligence. Quartile
Color is a powerful visual cue in many computer vision applications such as image segmentation and object recognition. However, most of the existing color models depend on the imaging conditions that negatively affect the performance of the task at hand. Often, a reflection model (e.g., Lambertian or dichromatic reflectance) is used to derive color invariant models. However, this approach may be too restricted to model real-world scenes in which different reflectance mechanisms can hold simultaneously.
Therefore, in this paper, we aim to derive color invariance by learning from color models to obtain diversified color invariant ensembles. First, a photometrical orthogonal and non-redundant color model set is computed composed of both color variants and invariants. Then, the proposed method combines these color models to arrive at a diversified color ensemble yielding a proper balance between invariance (repeatability) and discriminative power (distinctiveness). To achieve this, our fusion method uses a multi-view approach to minimize the estimation error. In this way, the proposed method is robust to data uncertainty and produces properly diversified color invariant ensembles. Further, the proposed method is extended to deal with temporal data by predicting the evolution of observations over time.
Experiments are conducted on three different image datasets to validate the proposed method. Both the theoretical and experimental results show that the method is robust against severe variations in imaging conditions. The method is not restricted to a certain reflection model or parameter tuning, and outperforms state-of-the-art detection techniques in the field of object, skin and road recognition. Considering sequential data, the proposed method (extended to deal with future observations) outperforms the other methods |
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Springer US |
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ADAS @ adas @ AGL2010c |
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1451 |
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Author |
A. Gijsenij; Theo Gevers; Joost Van de Weijer |
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Title |
Generalized Gamut Mapping using Image Derivative Structures for Color Constancy |
Type |
Journal Article |
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Year |
2010 |
Publication |
International Journal of Computer Vision |
Abbreviated Journal |
IJCV |
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Volume |
86 |
Issue |
2-3 |
Pages |
127-139 |
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The gamut mapping algorithm is one of the most promising methods to achieve computational color constancy. However, so far, gamut mapping algorithms are restricted to the use of pixel values to estimate the illuminant. Therefore, in this paper, gamut mapping is extended to incorporate the statistical nature of images. It is analytically shown that the proposed gamut mapping framework is able to include any linear filter output. The main focus is on the local n-jet describing the derivative structure of an image. It is shown that derivatives have the advantage over pixel values to be invariant to disturbing effects (i.e. deviations of the diagonal model) such as saturated colors and diffuse light. Further, as the n-jet based gamut mapping has the ability to use more information than pixel values alone, the combination of these algorithms are more stable than the regular gamut mapping algorithm. Different methods of combining are proposed. Based on theoretical and experimental results conducted on large scale data sets of hyperspectral, laboratory and realworld scenes, it can be derived that (1) in case of deviations of the diagonal model, the derivative-based approach outperforms the pixel-based gamut mapping, (2) state-of-the-art algorithms are outperformed by the n-jet based gamut mapping, (3) the combination of the different n-jet based gamut |
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Kluwer Academic Publishers Hingham, MA, USA |
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CAT @ cat @ GGW2010 |
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1274 |
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