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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 |
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Admin @ si @ HJG2015 |
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2730 |
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Author |
Alejandro Gonzalez Alzate; Sebastian Ramos; David Vazquez; Antonio Lopez; Jaume Amores |
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Title |
Spatiotemporal Stacked Sequential Learning for Pedestrian Detection |
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2015 |
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Pattern Recognition and Image Analysis, Proceedings of 7th Iberian Conference , ibPRIA 2015 |
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3-12 |
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SSL; Pedestrian Detection |
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Pedestrian classifiers decide which image windows contain a pedestrian. In practice, such classifiers provide a relatively high response at neighbor windows overlapping a pedestrian, while the responses around potential false positives are expected to be lower. An analogous reasoning applies for image sequences. If there is a pedestrian located within a frame, the same pedestrian is expected to appear close to the same location in neighbor frames. Therefore, such a location has chances of receiving high classification scores during several frames, while false positives are expected to be more spurious. In this paper we propose to exploit such correlations for improving the accuracy of base pedestrian classifiers. In particular, we propose to use two-stage classifiers which not only rely on the image descriptors required by the base classifiers but also on the response of such base classifiers in a given spatiotemporal neighborhood. More specifically, we train pedestrian classifiers using a stacked sequential learning (SSL) paradigm. We use a new pedestrian dataset we have acquired from a car to evaluate our proposal at different frame rates. We also test on a well known dataset: Caltech. The obtained results show that our SSL proposal boosts detection accuracy significantly with a minimal impact on the computational cost. Interestingly, SSL improves more the accuracy at the most dangerous situations, i.e. when a pedestrian is close to the camera. |
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Santiago de Compostela; España; June 2015 |
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IbPRIA |
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ADAS; 600.057; 600.054; 600.076 |
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GRV2015; ADAS @ adas @ GRV2015 |
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2454 |
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Naveen Onkarappa; Angel Sappa |
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Synthetic sequences and ground-truth flow field generation for algorithm validation |
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2015 |
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Multimedia Tools and Applications |
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MTAP |
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74 |
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9 |
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3121-3135 |
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Ground-truth optical flow; Synthetic sequence; Algorithm validation |
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Research in computer vision is advancing by the availability of good datasets that help to improve algorithms, validate results and obtain comparative analysis. The datasets can be real or synthetic. For some of the computer vision problems such as optical flow it is not possible to obtain ground-truth optical flow with high accuracy in natural outdoor real scenarios directly by any sensor, although it is possible to obtain ground-truth data of real scenarios in a laboratory setup with limited motion. In this difficult situation computer graphics offers a viable option for creating realistic virtual scenarios. In the current work we present a framework to design virtual scenes and generate sequences as well as ground-truth flow fields. Particularly, we generate a dataset containing sequences of driving scenarios. The sequences in the dataset vary in different speeds of the on-board vision system, different road textures, complex motion of vehicle and independent moving vehicles in the scene. This dataset enables analyzing and adaptation of existing optical flow methods, and leads to invention of new approaches particularly for driver assistance systems. |
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Springer US |
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1380-7501 |
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ADAS; 600.055; 601.215; 600.076 |
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Admin @ si @ OnS2014b |
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2472 |
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Author |
Monica Piñol; Angel Sappa; Ricardo Toledo |
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Title |
Adaptive Feature Descriptor Selection based on a Multi-Table Reinforcement Learning Strategy |
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2015 |
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Neurocomputing |
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NEUCOM |
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150 |
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A |
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106–115 |
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Reinforcement learning; Q-learning; Bag of features; Descriptors |
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This paper presents and evaluates a framework to improve the performance of visual object classification methods, which are based on the usage of image feature descriptors as inputs. The goal of the proposed framework is to learn the best descriptor for each image in a given database. This goal is reached by means of a reinforcement learning process using the minimum information. The visual classification system used to demonstrate the proposed framework is based on a bag of features scheme, and the reinforcement learning technique is implemented through the Q-learning approach. The behavior of the reinforcement learning with different state definitions is evaluated. Additionally, a method that combines all these states is formulated in order to select the optimal state. Finally, the chosen actions are obtained from the best set of image descriptors in the literature: PHOW, SIFT, C-SIFT, SURF and Spin. Experimental results using two public databases (ETH and COIL) are provided showing both the validity of the proposed approach and comparisons with state of the art. In all the cases the best results are obtained with the proposed approach. |
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ADAS; 600.055; 600.076 |
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Admin @ si @ PST2015 |
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2473 |
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Author |
Miguel Oliveira; Victor Santos; Angel Sappa |
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Title |
Multimodal Inverse Perspective Mapping |
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2015 |
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Information Fusion |
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IF |
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24 |
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108–121 |
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Inverse perspective mapping; Multimodal sensor fusion; Intelligent vehicles |
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Over the past years, inverse perspective mapping has been successfully applied to several problems in the field of Intelligent Transportation Systems. In brief, the method consists of mapping images to a new coordinate system where perspective effects are removed. The removal of perspective associated effects facilitates road and obstacle detection and also assists in free space estimation. There is, however, a significant limitation in the inverse perspective mapping: the presence of obstacles on the road disrupts the effectiveness of the mapping. The current paper proposes a robust solution based on the use of multimodal sensor fusion. Data from a laser range finder is fused with images from the cameras, so that the mapping is not computed in the regions where obstacles are present. As shown in the results, this considerably improves the effectiveness of the algorithm and reduces computation time when compared with the classical inverse perspective mapping. Furthermore, the proposed approach is also able to cope with several cameras with different lenses or image resolutions, as well as dynamic viewpoints. |
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ADAS; 600.055; 600.076 |
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Admin @ si @ OSS2015c |
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2532 |
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T. Mouats; N. Aouf; Angel Sappa; Cristhian A. Aguilera-Carrasco; Ricardo Toledo |
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Multi-Spectral Stereo Odometry |
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2015 |
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IEEE Transactions on Intelligent Transportation Systems |
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TITS |
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16 |
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3 |
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1210-1224 |
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Egomotion estimation; feature matching; multispectral odometry (MO); optical flow; stereo odometry; thermal imagery |
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In this paper, we investigate the problem of visual odometry for ground vehicles based on the simultaneous utilization of multispectral cameras. It encompasses a stereo rig composed of an optical (visible) and thermal sensors. The novelty resides in the localization of the cameras as a stereo setup rather
than two monocular cameras of different spectrums. To the best of our knowledge, this is the first time such task is attempted. Log-Gabor wavelets at different orientations and scales are used to extract interest points from both images. These are then described using a combination of frequency and spatial information within the local neighborhood. Matches between the pairs of multimodal images are computed using the cosine similarity function based
on the descriptors. Pyramidal Lucas–Kanade tracker is also introduced to tackle temporal feature matching within challenging sequences of the data sets. The vehicle egomotion is computed from the triangulated 3-D points corresponding to the matched features. A windowed version of bundle adjustment incorporating
Gauss–Newton optimization is utilized for motion estimation. An outlier removal scheme is also included within the framework to deal with outliers. Multispectral data sets were generated and used as test bed. They correspond to real outdoor scenarios captured using our multimodal setup. Finally, detailed results validating the proposed strategy are illustrated. |
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1524-9050 |
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ADAS; 600.055; 600.076 |
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Admin @ si @ MAS2015a |
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2533 |
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Joan M. Nuñez; Jorge Bernal; F. Javier Sanchez; Fernando Vilariño |
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Title |
Growing Algorithm for Intersection Detection (GRAID) in branching patterns |
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2015 |
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Machine Vision and Applications |
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MVAP |
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26 |
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2 |
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387-400 |
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Bifurcation ; Crossroad; Intersection ;Retina ; Vessel |
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Analysis of branching structures represents a very important task in fields such as medical diagnosis, road detection or biometrics. Detecting intersection landmarks Becomes crucial when capturing the structure of a branching pattern. We present a very simple geometrical model to describe intersections in branching structures based on two conditions: Bounded Tangency condition (BT) and Shortest Branch (SB) condition. The proposed model precisely sets a geometrical characterization of intersections and allows us to introduce a new unsupervised operator for intersection extraction. We propose an implementation that handles the consequences of digital domain operation that,unlike existing approaches, is not restricted to a particular scale and does not require the computation of the thinned pattern. The new proposal, as well as other existing approaches in the bibliography, are evaluated in a common framework for the first time. The performance analysis is based on two manually segmented image data sets: DRIVE retinal image database and COLON-VESSEL data set, a newly created data set of vascular content in colonoscopy frames. We have created an intersection landmark ground truth for each data set besides comparing our method in the only existing ground truth. Quantitative results confirm that we are able to outperform state-of-the-art performancelevels with the advantage that neither training nor parameter tuning is needed. |
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;SIAI |
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Admin @ si @MBS2015 |
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2777 |
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Fernando Vilariño; Dan Norton; Onur Ferhat |
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Memory Fields: DJs in the Library |
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2015 |
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21 st Symposium of Electronic Arts |
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Vancouver; Canada; August 2015 |
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Admin @ si @VNF2015 |
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2800 |
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