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Aura Hernandez-Sabate; Lluis Albarracin; F. Javier Sanchez |
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Title |
Graph-Based Problem Explorer: A Software Tool to Support Algorithm Design Learning While Solving the Salesperson Problem |
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2020 |
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Mathematics |
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MATH |
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20 |
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8(9) |
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1595 |
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STEM education; Project-based learning; Coding; software tool |
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In this article, we present a sequence of activities in the form of a project in order to promote
learning on design and analysis of algorithms. The project is based on the resolution of a real problem, the salesperson problem, and it is theoretically grounded on the fundamentals of mathematical modelling. In order to support the students’ work, a multimedia tool, called Graph-based Problem Explorer (GbPExplorer), has been designed and refined to promote the development of computer literacy in engineering and science university students. This tool incorporates several modules to allow coding different algorithmic techniques solving the salesman problem. Based on an educational design research along five years, we observe that working with GbPExplorer during the project provides students with the possibility of representing the situation to be studied in the form of graphs and analyze them from a computational point of view. |
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September 2020 |
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IAM; ISE |
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3722 |
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Noha Elfiky; Fahad Shahbaz Khan; Joost Van de Weijer; Jordi Gonzalez |
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Title |
Discriminative Compact Pyramids for Object and Scene Recognition |
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2012 |
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Pattern Recognition |
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PR |
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45 |
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4 |
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1627-1636 |
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Spatial pyramids have been successfully applied to incorporating spatial information into bag-of-words based image representation. However, a major drawback is that it leads to high dimensional image representations. In this paper, we present a novel framework for obtaining compact pyramid representation. First, we investigate the usage of the divisive information theoretic feature clustering (DITC) algorithm in creating a compact pyramid representation. In many cases this method allows us to reduce the size of a high dimensional pyramid representation up to an order of magnitude with little or no loss in accuracy. Furthermore, comparison to clustering based on agglomerative information bottleneck (AIB) shows that our method obtains superior results at significantly lower computational costs. Moreover, we investigate the optimal combination of multiple features in the context of our compact pyramid representation. Finally, experiments show that the method can obtain state-of-the-art results on several challenging data sets. |
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0031-3203 |
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ISE; CAT;CIC |
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Admin @ si @ EKW2012 |
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1807 |
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Author |
R. Valenti; Theo Gevers |
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Title |
Accurate Eye Center Location through Invariant Isocentric Patterns |
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2012 |
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IEEE Transaction on Pattern Analysis and Machine Intelligence |
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TPAMI |
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34 |
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9 |
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1785-1798 |
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Impact factor 2010: 5.308
Impact factor 2011/12?: 5.96
Locating the center of the eyes allows for valuable information to be captured and used in a wide range of applications. Accurate eye center location can be determined using commercial eye-gaze trackers, but additional constraints and expensive hardware make these existing solutions unattractive and impossible to use on standard (i.e., visible wavelength), low-resolution images of eyes. Systems based solely on appearance are proposed in the literature, but their accuracy does not allow us to accurately locate and distinguish eye centers movements in these low-resolution settings. Our aim is to bridge this gap by locating the center of the eye within the area of the pupil on low-resolution images taken from a webcam or a similar device. The proposed method makes use of isophote properties to gain invariance to linear lighting changes (contrast and brightness), to achieve in-plane rotational invariance, and to keep low-computational costs. To further gain scale invariance, the approach is applied to a scale space pyramid. In this paper, we extensively test our approach for its robustness to changes in illumination, head pose, scale, occlusion, and eye rotation. We demonstrate that our system can achieve a significant improvement in accuracy over state-of-the-art techniques for eye center location in standard low-resolution imagery. |
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0162-8828 |
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ALTRES;ISE |
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Admin @ si @ VaG 2012a |
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1849 |
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Author |
Marco Pedersoli; Andrea Vedaldi; Jordi Gonzalez; Xavier Roca |
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A coarse-to-fine approach for fast deformable object detection |
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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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5 |
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1844-1853 |
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We present a method that can dramatically accelerate object detection with part based models. The method is based on the observation that the cost of detection is likely to be dominated by the cost of matching each part to the image, and not by the cost of computing the optimal configuration of the parts as commonly assumed. Therefore accelerating detection requires minimizing the number of
part-to-image comparisons. To this end we propose a multiple-resolutions hierarchical part based model and a corresponding coarse-to-fine inference procedure that recursively eliminates from the search space unpromising part
placements. The method yields a ten-fold speedup over the standard dynamic programming approach and is complementary to the cascade-of-parts approach of [9]. Compared to the latter, our method does not have parameters to be determined empirically, which simplifies its use during the training of the model. Most importantly, the two techniques can be combined to obtain a very significant speedup, of two orders of magnitude in some cases. We evaluate our method extensively on the PASCAL VOC and INRIA datasets, demonstrating a very high increase in the detection speed with little degradation of the accuracy. |
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ISE; 600.078; 602.005; 605.001; 302.012 |
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Admin @ si @ PVG2015 |
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2628 |
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Author |
Wenjuan Gong; Xuena Zhang; Jordi Gonzalez; Andrews Sobral; Thierry Bouwmans; Changhe Tu; El-hadi Zahzah |
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Title |
Human Pose Estimation from Monocular Images: A Comprehensive Survey |
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Journal Article |
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2016 |
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Sensors |
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SENS |
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16 |
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12 |
Pages |
1966 |
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Keywords |
human pose estimation; human bodymodels; generativemethods; discriminativemethods; top-down methods; bottom-up methods |
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Human pose estimation refers to the estimation of the location of body parts and how they are connected in an image. Human pose estimation from monocular images has wide applications (e.g., image indexing). Several surveys on human pose estimation can be found in the literature, but they focus on a certain category; for example, model-based approaches or human motion analysis, etc. As far as we know, an overall review of this problem domain has yet to be provided. Furthermore, recent advancements based on deep learning have brought novel algorithms for this problem. In this paper, a comprehensive survey of human pose estimation from monocular images is carried out including milestone works and recent advancements. Based on one standard pipeline for the solution of computer vision problems, this survey splits the problem into several modules: feature extraction and description, human body models, and modeling
methods. Problem modeling methods are approached based on two means of categorization in this survey. One way to categorize includes top-down and bottom-up methods, and another way includes generative and discriminative methods. Considering the fact that one direct application of human pose estimation is to provide initialization for automatic video surveillance, there are additional sections for motion-related methods in all modules: motion features, motion models, and motion-based methods. Finally, the paper also collects 26 publicly available data sets for validation and provides error measurement methods that are frequently used. |
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ISE; 600.098; 600.119 |
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Admin @ si @ GZG2016 |
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2933 |
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