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Author |
Mohammad Rouhani; Angel Sappa |
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Title |
Relaxing the 3L Algorithm for an Accurate Implicit Polynomial Fitting |
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Conference Article |
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Year |
2010 |
Publication |
23rd IEEE Conference on Computer Vision and Pattern Recognition |
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3066-3072 |
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This paper presents a novel method to increase the accuracy of linear fitting of implicit polynomials. The proposed method is based on the 3L algorithm philosophy. The novelty lies on the relaxation of the additional constraints, already imposed by the 3L algorithm. Hence, the accuracy of the final solution is increased due to the proper adjustment of the expected values in the aforementioned additional constraints. Although iterative, the proposed approach solves the fitting problem within a linear framework, which is independent of the threshold tuning. Experimental results, both in 2D and 3D, showing improvements in the accuracy of the fitting are presented. Comparisons with both state of the art algorithms and a geometric based one (non-linear fitting), which is used as a ground truth, are provided. |
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San Francisco; CA; USA; June 2010 |
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1063-6919 |
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978-1-4244-6984-0 |
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CVPR |
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ADAS |
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ADAS @ adas @ RoS2010a |
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1303 |
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Author |
Mohammad Rouhani; Angel Sappa |
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Title |
A Novel Approach to Geometric Fitting of Implicit Quadrics |
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Conference Article |
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Year |
2009 |
Publication |
8th International Conference on Advanced Concepts for Intelligent Vision Systems |
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5807 |
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121–132 |
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This paper presents a novel approach for estimating the geometric distance from a given point to the corresponding implicit quadric curve/surface. The proposed estimation is based on the height of a tetrahedron, which is used as a coarse but reliable estimation of the real distance. The estimated distance is then used for finding the best set of quadric parameters, by means of the Levenberg-Marquardt algorithm, which is a common framework in other geometric fitting approaches. Comparisons of the proposed approach with previous ones are provided to show both improvements in CPU time as well as in the accuracy of the obtained results. |
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Bordeaux, France |
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Springer Berlin Heidelberg |
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0302-9743 |
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978-3-642-04696-4 |
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ACIVS |
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ADAS @ adas @ RoS2009 |
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1194 |
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Gemma Roig; Xavier Boix; R. de Nijs; Sebastian Ramos; K. Kühnlenz; Luc Van Gool |
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Title |
Active MAP Inference in CRFs for Efficient Semantic Segmentation |
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Conference Article |
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Year |
2013 |
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15th IEEE International Conference on Computer Vision |
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2312 - 2319 |
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Semantic Segmentation |
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Most MAP inference algorithms for CRFs optimize an energy function knowing all the potentials. In this paper, we focus on CRFs where the computational cost of instantiating the potentials is orders of magnitude higher than MAP inference. This is often the case in semantic image segmentation, where most potentials are instantiated by slow classifiers fed with costly features. We introduce Active MAP inference 1) to on-the-fly select a subset of potentials to be instantiated in the energy function, leaving the rest of the parameters of the potentials unknown, and 2) to estimate the MAP labeling from such incomplete energy function. Results for semantic segmentation benchmarks, namely PASCAL VOC 2010 [5] and MSRC-21 [19], show that Active MAP inference achieves similar levels of accuracy but with major efficiency gains. |
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Sydney; Australia; December 2013 |
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1550-5499 |
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ICCV |
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ADAS; 600.057 |
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Call Number ![sorted by Call Number field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
ADAS @ adas @ RBN2013 |
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2377 |
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Author |
Petia Radeva; Joan Serrat |
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Title |
Rubber Snake: Implementation on Signed Distance Potential. |
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Conference Article |
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1993 |
Publication |
Vision Conference |
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187-194 |
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Zurich, Switzerland. |
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ADAS;MILAB |
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ADAS @ adas @ RaS1993 |
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170 |
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Author |
Daniel Ponsa; Antonio Lopez |
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Title |
Cascade of Classifiers for Vehicle Detection |
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Conference Article |
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2007 |
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Advanced Concepts for Intelligent Vision Systems, LNCS 4678, volume 1, pp. 980–989 |
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vehicle detection |
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Delft (Netherlands) |
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ADAS @ adas @ PoL2007c |
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935 |
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Author |
Daniel Ponsa; Antonio Lopez |
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Title |
Feature Selection Based on a New Formulation of the Minimal-Redundancy-Maximal-Relevance Criterion |
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Conference Article |
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2007 |
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3rd Iberian Conference on Pattern Recognition and Image Analysis, LNCS 4477 |
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47-54 |
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Girona (Spain) |
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Call Number ![sorted by Call Number field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
ADAS @ adas @ PoL2007b |
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787 |
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Author |
Daniel Ponsa; Antonio Lopez |
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Title |
Vehicle Trajectory Estimation based on Monocular Vision |
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Conference Article |
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2007 |
Publication |
3rd Iberian Conference on Pattern Recognition and Image Analysis, LNCS 4477 |
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587-594 |
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vehicle detection |
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Girona (Spain) |
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Call Number ![sorted by Call Number field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
ADAS @ adas @ PoL2007a |
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785 |
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Author |
A. Pujol; Felipe Lumbreras; Javier Varona; Juan J. Villanueva |
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Title |
Locating people in indoor scenes for real applications. |
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Conference Article |
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2000 |
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15 th International Conference on Pattern Recognition |
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4 |
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632-635 |
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Barcelona. |
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ADAS |
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Call Number ![sorted by Call Number field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
ADAS @ adas @ PLV2000 |
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237 |
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Author |
Naveen Onkarappa; Angel Sappa |
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Title |
On-Board Monocular Vision System Pose Estimation through a Dense Optical Flow |
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Conference Article |
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2010 |
Publication |
7th International Conference on Image Analysis and Recognition |
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6111 |
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230-239 |
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This paper presents a robust technique for estimating on-board monocular vision system pose. The proposed approach is based on a dense optical flow that is robust against shadows, reflections and illumination changes. A RANSAC based scheme is used to cope with the outliers in the optical flow. The proposed technique is intended to be used in driver assistance systems for applications such as obstacle or pedestrian detection. Experimental results on different scenarios, both from synthetic and real sequences, shows usefulness of the proposed approach. |
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Povoa de Varzim (Portugal) |
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Springer Berlin Heidelberg |
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LNCS |
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0302-9743 |
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978-3-642-13771-6 |
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ICIAR |
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ADAS |
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Call Number ![sorted by Call Number field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
ADAS @ adas @ OnS2010 |
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1342 |
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Author |
R. de Nijs; Sebastian Ramos; Gemma Roig; Xavier Boix; Luc Van Gool; K. Kühnlenz. |
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Title |
On-line Semantic Perception Using Uncertainty |
Type |
Conference Article |
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Year |
2012 |
Publication |
International Conference on Intelligent Robots and Systems |
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IROS |
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4185-4191 |
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Semantic Segmentation |
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Abstract |
Visual perception capabilities are still highly unreliable in unconstrained settings, and solutions might not beaccurate in all regions of an image. Awareness of the uncertainty of perception is a fundamental requirement for proper high level decision making in a robotic system. Yet, the uncertainty measure is often sacrificed to account for dependencies between object/region classifiers. This is the case of Conditional Random Fields (CRFs), the success of which stems from their ability to infer the most likely world configuration, but they do not directly allow to estimate the uncertainty of the solution. In this paper, we consider the setting of assigning semantic labels to the pixels of an image sequence. Instead of using a CRF, we employ a Perturb-and-MAP Random Field, a recently introduced probabilistic model that allows performing fast approximate sampling from its probability density function. This allows to effectively compute the uncertainty of the solution, indicating the reliability of the most likely labeling in each region of the image. We report results on the CamVid dataset, a standard benchmark for semantic labeling of urban image sequences. In our experiments, we show the benefits of exploiting the uncertainty by putting more computational effort on the regions of the image that are less reliable, and use more efficient techniques for other regions, showing little decrease of performance |
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IROS |
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ADAS |
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Call Number ![sorted by Call Number field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
ADAS @ adas @ NRR2012 |
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2378 |
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