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Aura Hernandez-Sabate; Meritxell Joanpere; Nuria Gorgorio; Lluis Albarracin |
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Title |
Mathematics learning opportunities when playing a Tower Defense Game |
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2015 |
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International Journal of Serious Games |
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IJSG |
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2 |
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4 |
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57-71 |
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Tower Defense game; learning opportunities; mathematics; problem solving; game design |
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A qualitative research study is presented herein with the purpose of identifying mathematics learning opportunities in students between 10 and 12 years old while playing a commercial version of a Tower Defense game. These learning opportunities are understood as mathematicisable moments of the game and involve the establishment of relationships between the game and mathematical problem solving. Based on the analysis of these mathematicisable moments, we conclude that the game can promote problem-solving processes and learning opportunities that can be associated with different mathematical contents that appears in mathematics curricula, thought it seems that teacher or new game elements might be needed to facilitate the processes. |
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ADAS; 600.076;IAM |
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Admin @ si @ HJG2015 |
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2730 |
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Author |
Jose Carlos Rubio; Joan Serrat; Antonio Lopez; Daniel Ponsa |
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Title |
Multiple target tracking for intelligent headlights control |
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2012 |
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IEEE Transactions on Intelligent Transportation Systems |
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TITS |
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13 |
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2 |
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594-605 |
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Intelligent Headlights |
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Intelligent vehicle lighting systems aim at automatically regulating the headlights' beam to illuminate as much of the road ahead as possible while avoiding dazzling other drivers. A key component of such a system is computer vision software that is able to distinguish blobs due to vehicles' headlights and rear lights from those due to road lamps and reflective elements such as poles and traffic signs. In a previous work, we have devised a set of specialized supervised classifiers to make such decisions based on blob features related to its intensity and shape. Despite the overall good performance, there remain challenging that have yet to be solved: notably, faint and tiny blobs corresponding to quite distant vehicles. In fact, for such distant blobs, classification decisions can be taken after observing them during a few frames. Hence, incorporating tracking could improve the overall lighting system performance by enforcing the temporal consistency of the classifier decision. Accordingly, this paper focuses on the problem of constructing blob tracks, which is actually one of multiple-target tracking (MTT), but under two special conditions: We have to deal with frequent occlusions, as well as blob splits and merges. We approach it in a novel way by formulating the problem as a maximum a posteriori inference on a Markov random field. The qualitative (in video form) and quantitative evaluation of our new MTT method shows good tracking results. In addition, we will also see that the classification performance of the problematic blobs improves due to the proposed MTT algorithm. |
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1524-9050 |
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ADAS |
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Admin @ si @ RLP2012; ADAS @ adas @ rsl2012g |
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1877 |
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Sergio Vera; Debora Gil; Antonio Lopez; Miguel Angel Gonzalez Ballester |
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Title |
Multilocal Creaseness Measure |
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2012 |
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The Insight Journal |
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IJ |
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Ridges, Valley, Creaseness, Structure Tensor, Skeleton, |
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This document describes the implementation using the Insight Toolkit of an algorithm for detecting creases (ridges and valleys) in N-dimensional images, based on the Local Structure Tensor of the image. In addition to the filter used to calculate the creaseness image, a filter for the computation of the structure tensor is also included in this submission. |
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Alma IT Systems |
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english |
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english |
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IAM;ADAS; |
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IAM @ iam @ VGL2012 |
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1840 |
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Author |
Fernando Barrera; Felipe Lumbreras; Angel Sappa |
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Title |
Multispectral Piecewise Planar Stereo using Manhattan-World Assumption |
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2013 |
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Pattern Recognition Letters |
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PRL |
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34 |
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1 |
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52-61 |
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Multispectral stereo rig; Dense disparity maps from multispectral stereo; Color and infrared images |
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This paper proposes a new framework for extracting dense disparity maps from a multispectral stereo rig. The system is constructed with an infrared and a color camera. It is intended to explore novel multispectral stereo matching approaches that will allow further extraction of semantic information. The proposed framework consists of three stages. Firstly, an initial sparse disparity map is generated by using a cost function based on feature matching in a multiresolution scheme. Then, by looking at the color image, a set of planar hypotheses is defined to describe the surfaces on the scene. Finally, the previous stages are combined by reformulating the disparity computation as a global minimization problem. The paper has two main contributions. The first contribution combines mutual information with a shape descriptor based on gradient in a multiresolution scheme. The second contribution, which is based on the Manhattan-world assumption, extracts a dense disparity representation using the graph cut algorithm. Experimental results in outdoor scenarios are provided showing the validity of the proposed framework. |
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ADAS; 600.054; 600.055; 605.203 |
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Admin @ si @ BLS2013 |
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2245 |
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J.S. Cope; P.Remagnino; S.Mannan; Katerine Diaz; Francesc J. Ferri; P.Wilkin |
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Title |
Reverse Engineering Expert Visual Observations: From Fixations To The Learning Of Spatial Filters With A Neural-Gas Algorithm |
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2013 |
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Expert Systems with Applications |
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EXWA |
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40 |
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17 |
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6707-6712 |
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Neural gas; Expert vision; Eye-tracking; Fixations |
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Human beings can become experts in performing specific vision tasks, for example, doctors analysing medical images, or botanists studying leaves. With sufficient knowledge and experience, people can become very efficient at such tasks. When attempting to perform these tasks with a machine vision system, it would be highly beneficial to be able to replicate the process which the expert undergoes. Advances in eye-tracking technology can provide data to allow us to discover the manner in which an expert studies an image. This paper presents a first step towards utilizing these data for computer vision purposes. A growing-neural-gas algorithm is used to learn a set of Gabor filters which give high responses to image regions which a human expert fixated on. These filters can then be used to identify regions in other images which are likely to be useful for a given vision task. The algorithm is evaluated by learning filters for locating specific areas of plant leaves. |
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0957-4174 |
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ADAS |
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Admin @ si @ CRM2013 |
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2438 |
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Author |
Fadi Dornaika; Angel Sappa |
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Title |
A Featureless and Stochastic Approach to On-board Stereo Vision System Pose |
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2009 |
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Image and Vision Computing |
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IMAVIS |
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27 |
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9 |
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1382–1393 |
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On-board stereo vision system; Pose estimation; Featureless approach; Particle filtering; Image warping |
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This paper presents a direct and stochastic technique for real-time estimation of on-board stereo head’s position and orientation. Unlike existing works which rely on feature extraction either in the image domain or in 3D space, our proposed approach directly estimates the unknown parameters from the stream of stereo pairs’ brightness. The pose parameters are tracked using the particle filtering framework which implicitly enforces the smoothness constraints on the estimated parameters. The proposed technique can be used with a driver assistance applications as well as with augmented reality applications. Extended experiments on urban environments with different road geometries are presented. Comparisons with a 3D data-based approach are presented. Moreover, we provide a performance study aiming at evaluating the accuracy of the proposed approach. |
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ADAS |
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ADAS @ adas @ DoS2009b |
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1152 |
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Daniel Ponsa; Antonio Lopez |
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Variance reduction techniques in particle-based visual contour Tracking |
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2009 |
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Pattern Recognition |
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PR |
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42 |
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11 |
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2372–2391 |
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Contour tracking; Active shape models; Kalman filter; Particle filter; Importance sampling; Unscented particle filter; Rao-Blackwellization; Partitioned sampling |
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This paper presents a comparative study of three different strategies to improve the performance of particle filters, in the context of visual contour tracking: the unscented particle filter, the Rao-Blackwellized particle filter, and the partitioned sampling technique. The tracking problem analyzed is the joint estimation of the global and local transformation of the outline of a given target, represented following the active shape model approach. The main contributions of the paper are the novel adaptations of the considered techniques on this generic problem, and the quantitative assessment of their performance in extensive experimental work done. |
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ADAS |
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ADAS @ adas @ PoL2009a |
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1168 |
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Angel Sappa; P. Carvajal; Cristhian A. Aguilera-Carrasco; Miguel Oliveira; Dennis Romero; Boris X. Vintimilla |
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Wavelet based visible and infrared image fusion: a comparative study |
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2016 |
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Sensors |
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SENS |
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16 |
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6 |
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1-15 |
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Image fusion; fusion evaluation metrics; visible and infrared imaging; discrete wavelet transform |
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This paper evaluates different wavelet-based cross-spectral image fusion strategies adopted to merge visible and infrared images. The objective is to find the best setup independently of the evaluation metric used to measure the performance. Quantitative performance results are obtained with state of the art approaches together with adaptations proposed in the current work. The options evaluated in the current work result from the combination of different setups in the wavelet image decomposition stage together with different fusion strategies for the final merging stage that generates the resulting representation. Most of the approaches evaluate results according to the application for which they are intended for. Sometimes a human observer is selected to judge the quality of the obtained results. In the current work, quantitative values are considered in order to find correlations between setups and performance of obtained results; these correlations can be used to define a criteria for selecting the best fusion strategy for a given pair of cross-spectral images. The whole procedure is evaluated with a large set of correctly registered visible and infrared image pairs, including both Near InfraRed (NIR) and Long Wave InfraRed (LWIR). |
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ADAS; 600.086; 600.076 |
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Admin @ si @SCA2016 |
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2807 |
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Enric Marti; J.Roncaries; Debora Gil; Aura Hernandez-Sabate; Antoni Gurgui; Ferran Poveda |
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PBL On Line: A proposal for the organization, part-time monitoring and assessment of PBL group activities |
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2015 |
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Journal of Technology and Science Education |
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JOTSE |
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5 |
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2 |
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87-96 |
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IAM; ADAS; 600.076; 600.075 |
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Admin @ si @ MRG2015 |
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2608 |
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Idoia Ruiz; Joan Serrat |
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Hierarchical Novelty Detection for Traffic Sign Recognition |
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2022 |
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Sensors |
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SENS |
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22 |
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12 |
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4389 |
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Novelty detection; hierarchical classification; deep learning; traffic sign recognition; autonomous driving; computer vision |
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Recent works have made significant progress in novelty detection, i.e., the problem of detecting samples of novel classes, never seen during training, while classifying those that belong to known classes. However, the only information this task provides about novel samples is that they are unknown. In this work, we leverage hierarchical taxonomies of classes to provide informative outputs for samples of novel classes. We predict their closest class in the taxonomy, i.e., its parent class. We address this problem, known as hierarchical novelty detection, by proposing a novel loss, namely Hierarchical Cosine Loss that is designed to learn class prototypes along with an embedding of discriminative features consistent with the taxonomy. We apply it to traffic sign recognition, where we predict the parent class semantics for new types of traffic signs. Our model beats state-of-the art approaches on two large scale traffic sign benchmarks, Mapillary Traffic Sign Dataset (MTSD) and Tsinghua-Tencent 100K (TT100K), and performs similarly on natural images benchmarks (AWA2, CUB). For TT100K and MTSD, our approach is able to detect novel samples at the correct nodes of the hierarchy with 81% and 36% of accuracy, respectively, at 80% known class accuracy. |
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ADAS; 600.154 |
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Admin @ si @ RuS2022 |
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3684 |
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