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Author |
Juan Ramon Terven Salinas; Joaquin Salas; Bogdan Raducanu |
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Title |
Estado del Arte en Sistemas de Vision Artificial para Personas Invidentes |
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2013 |
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Komputer Sapiens |
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KS |
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1 |
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20-25 |
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OR;MV |
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no |
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Admin @ si @ TSR2013 |
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2231 |
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David Masip; Michael S. North ; Alexander Todorov; Daniel N. Osherson |
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Title |
Automated Prediction of Preferences Using Facial Expressions |
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Journal Article |
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2014 |
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PloS one |
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Plos |
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9 |
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2 |
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e87434 |
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We introduce a computer vision problem from social cognition, namely, the automated detection of attitudes from a person's spontaneous facial expressions. To illustrate the challenges, we introduce two simple algorithms designed to predict observers’ preferences between images (e.g., of celebrities) based on covert videos of the observers’ faces. The two algorithms are almost as accurate as human judges performing the same task but nonetheless far from perfect. Our approach is to locate facial landmarks, then predict preference on the basis of their temporal dynamics. The database contains 768 videos involving four different kinds of preferences. We make it publically available. |
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OR;MV |
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no |
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Admin @ si @ MNT2014 |
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2453 |
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Author |
R. Clariso; David Masip; A. Rius |
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Title |
Student projects empowering mobile learning in higher education |
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2014 |
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Revista de Universidad y Sociedad del Conocimiento |
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RUSC |
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11 |
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192-207 |
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1698-580X |
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OR;MV |
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Admin @ si @ CMR2014 |
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2619 |
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Author |
B. Zhou; Agata Lapedriza; J. Xiao; A. Torralba; A. Oliva |
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Title |
Learning Deep Features for Scene Recognition using Places Database |
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Conference Article |
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2014 |
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28th Annual Conference on Neural Information Processing Systems |
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487-495 |
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Montreal; Canada; December 2014 |
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NIPS |
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OR;MV |
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no |
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Admin @ si @ ZLX2014 |
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2621 |
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Author |
Agata Lapedriza; David Masip; David Sanchez |
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Title |
Emotions Classification using Facial Action Units Recognition |
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Conference Article |
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2014 |
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17th International Conference of the Catalan Association for Artificial Intelligence |
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269 |
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55-64 |
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In this work we build a system for automatic emotion classification from image sequences. We analyze subtle changes in facial expressions by detecting a subset of 12 representative facial action units (AUs). Then, we classify emotions based on the output of these AUs classifiers, i.e. the presence/absence of AUs. We base the AUs classification upon a set of spatio-temporal geometric and appearance features for facial representation, fusing them within the emotion classifier. A decision tree is trained for emotion classifying, making the resulting model easy to interpret by capturing the combination of AUs activation that lead to a particular emotion. For Cohn-Kanade database, the proposed system classifies 7 emotions with a mean accuracy of near 90%, attaining a similar recognition accuracy in comparison with non-interpretable models that are not based in AUs detection. |
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978-1-61499-451-0 |
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CCIA |
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OR;MV |
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no |
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Admin @ si @ LMS2014 |
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2622 |
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Author |
David Sanchez-Mendoza; David Masip; Agata Lapedriza |
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Title |
Emotion recognition from mid-level features |
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Journal Article |
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2015 |
Publication |
Pattern Recognition Letters |
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PRL |
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67 |
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Part 1 |
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66–74 |
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Facial expression; Emotion recognition; Action units; Computer vision |
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In this paper we present a study on the use of Action Units as mid-level features for automatically recognizing basic and subtle emotions. We propose a representation model based on mid-level facial muscular movement features. We encode these movements dynamically using the Facial Action Coding System, and propose to use these intermediate features based on Action Units (AUs) to classify emotions. AUs activations are detected fusing a set of spatiotemporal geometric and appearance features. The algorithm is validated in two applications: (i) the recognition of 7 basic emotions using the publicly available Cohn-Kanade database, and (ii) the inference of subtle emotional cues in the Newscast database. In this second scenario, we consider emotions that are perceived cumulatively in longer periods of time. In particular, we Automatically classify whether video shoots from public News TV channels refer to Good or Bad news. To deal with the different video lengths we propose a Histogram of Action Units and compute it using a sliding window strategy on the frame sequences. Our approach achieves accuracies close to human perception. |
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Elsevier B.V. |
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0167-8655 |
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OR;MV |
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no |
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Admin @ si @ SML2015 |
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2746 |
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Author |
L. Calvet; A. Ferrer; M. Gomes; A. Juan; David Masip |
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Title |
Combining Statistical Learning with Metaheuristics for the Multi-Depot Vehicle Routing Problem with Market Segmentation |
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Journal Article |
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2016 |
Publication |
Computers & Industrial Engineering |
Abbreviated Journal |
CIE |
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94 |
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93-104 |
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Multi-Depot Vehicle Routing Problem; market segmentation applications; hybrid algorithms; statistical learning |
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In real-life logistics and distribution activities it is usual to face situations in which the distribution of goods has to be made from multiple warehouses or depots to the nal customers. This problem is known as the Multi-Depot Vehicle Routing Problem (MDVRP), and it typically includes two sequential and correlated stages: (a) the assignment map of customers to depots, and (b) the corresponding design of the distribution routes. Most of the existing work in the literature has focused on minimizing distance-based distribution costs while satisfying a number of capacity constraints. However, no attention has been given so far to potential variations in demands due to the tness of the customerdepot mapping in the case of heterogeneous depots. In this paper, we consider this realistic version of the problem in which the depots are heterogeneous in terms of their commercial o er and customers show di erent willingness to consume depending on how well the assigned depot ts their preferences. Thus, we assume that di erent customer-depot assignment maps will lead to di erent customer-expenditure levels. As a consequence, market-segmentation strategiesneed to be considered in order to increase sales and total income while accounting for the distribution costs. To solve this extension of the MDVRP, we propose a hybrid approach that combines statistical learning techniques with a metaheuristic framework. First, a set of predictive models is generated from historical data. These statistical models allow estimating the demand of any customer depending on the assigned depot. Then, the estimated expenditure of each customer is included as part of an enriched objective function as a way to better guide the stochastic local search inside the metaheuristic framework. A set of computational experiments contribute to illustrate our approach and how the extended MDVRP considered here di ers in terms of the proposed solutions from the traditional one. |
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PERGAMON-ELSEVIER SCIENCE LTD |
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CIE |
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0360-8352 |
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OR;MV; |
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no |
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Call Number |
Admin @ si @ CFG2016 |
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2749 |
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Author |
Jose A. Garcia; David Masip; Valerio Sbragaglia; Jacopo Aguzzi |
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Title |
Automated Identification and Tracking of Nephrops norvegicus (L.) Using Infrared and Monochromatic Blue Light |
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Conference Article |
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2016 |
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19th International Conference of the Catalan Association for Artificial Intelligence |
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computer vision; video analysis; object recognition; tracking; behaviour; social; decapod; Nephrops norvegicus |
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Automated video and image analysis can be a very efficient tool to analyze
animal behavior based on sociality, especially in hard access environments
for researchers. The understanding of this social behavior can play a key role in the sustainable design of capture policies of many species. This paper proposes the use of computer vision algorithms to identify and track a specific specie, the Norway lobster, Nephrops norvegicus, a burrowing decapod with relevant commercial value which is captured by trawling. These animals can only be captured when are engaged in seabed excursions, which are strongly related with their social behavior.
This emergent behavior is modulated by the day-night cycle, but their social
interactions remain unknown to the scientific community. The paper introduces an identification scheme made of four distinguishable black and white tags (geometric shapes). The project has recorded 15-day experiments in laboratory pools, under monochromatic blue light (472 nm.) and darkness conditions (recorded using Infra Red light). Using this massive image set, we propose a comparative of state-ofthe-art computer vision algorithms to distinguish and track the different animals’ movements. We evaluate the robustness to the high noise presence in the infrared video signals and free out-of-plane rotations due to animal movement. The experiments show promising accuracies under a cross-validation protocol, being adaptable to the automation and analysis of large scale data. In a second contribution, we created an extensive dataset of shapes (46027 different shapes) from four daily experimental video recordings, which will be available to the community. |
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Barcelona; Spain; October 2016 |
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CCIA |
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OR;MV; |
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no |
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Admin @ si @ GMS2016 |
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2816 |
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Author |
Jose A. Garcia; David Masip; Valerio Sbragaglia; Jacopo Aguzzi |
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Using ORB, BoW and SVM to identificate and track tagged Norway lobster Nephrops Norvegicus (L.) |
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Conference Article |
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2016 |
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3rd International Conference on Maritime Technology and Engineering |
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Sustainable capture policies of many species strongly depend on the understanding of their social behaviour. Nevertheless, the analysis of emergent behaviour in marine species poses several challenges. Usually animals are captured and observed in tanks, and their behaviour is inferred from their dynamics and interactions. Therefore, researchers must deal with thousands of hours of video data. Without loss of generality, this paper proposes a computer
vision approach to identify and track specific species, the Norway lobster, Nephrops norvegicus. We propose an identification scheme were animals are marked using black and white tags with a geometric shape in the center (holed
triangle, filled triangle, holed circle and filled circle). Using a massive labelled dataset; we extract local features based on the ORB descriptor. These features are a posteriori clustered, and we construct a Bag of Visual Words feature vector per animal. This approximation yields us invariance to rotation
and translation. A SVM classifier achieves generalization results above 99%. In a second contribution, we will make the code and training data publically available. |
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Lisboa; Portugal; July 2016 |
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MARTECH |
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OR;MV; |
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no |
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Admin @ si @ GMS2016b |
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2817 |
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Author |
Alex Goldhoorn; Arnau Ramisa; Ramon Lopez de Mantaras; Ricardo Toledo |
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Title |
Using the Average Landmark Vector Method for Robot Homing |
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Conference Article |
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2007 |
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Artificial Intelligence Research and Development, Proceedings of the 10th International Conference of the ACIA |
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163 |
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331–338 |
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978–1–58603–798–7 |
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CCIA’07 |
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RV;ADAS |
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Admin @ si @ GRL2007 |
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899 |
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Arnau Ramisa; Ramon Lopez de Mantaras; Ricardo Toledo |
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Title |
Comparing Combinations of Feature Regions for Panoramic VSLAM |
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Conference Article |
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2007 |
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4th International Conference on Informatics in Control, Automation and Robotics |
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292–297 |
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Angers (France) |
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ICINCO |
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RV;ADAS |
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Admin @ si @ RLA2007 |
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900 |
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Arnau Ramisa; Adriana Tapus; Ramon Lopez de Mantaras; Ricardo Toledo |
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Mobile Robot Localization using Panoramic Vision and Combination of Feature Region Detectors |
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2008 |
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IEEE International Conference on Robotics and Automation, |
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538–543 |
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Pasadena; CA; USA |
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ICRA |
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RV;ADAS |
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no |
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Admin @ si @ RTL2008 |
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1144 |
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Arnau Ramisa; Alex Goldhoorn; David Aldavert; Ricardo Toledo; Ramon Lopez de Mantaras |
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Combining Invariant Features and the ALV Homing Method for Autonomous Robot Navigation Based on Panoramas |
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2011 |
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Journal of Intelligent and Robotic Systems |
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JIRC |
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64 |
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3-4 |
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625-649 |
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Biologically inspired homing methods, such as the Average Landmark Vector, are an interesting solution for local navigation due to its simplicity. However, usually they require a modification of the environment by placing artificial landmarks in order to work reliably. In this paper we combine the Average Landmark Vector with invariant feature points automatically detected in panoramic images to overcome this limitation. The proposed approach has been evaluated first in simulation and, as promising results are found, also in two data sets of panoramas from real world environments. |
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Springer Netherlands |
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0921-0296 |
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RV;ADAS |
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Admin @ si @ RGA2011 |
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1728 |
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Author |
Arnau Ramisa; David Aldavert; Shrihari Vasudevan; Ricardo Toledo; Ramon Lopez de Mantaras |
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The IIIA30 MObile Robot Object Recognition Datset |
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Conference Article |
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2011 |
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11th Portuguese Robotics Open |
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Object perception is a key feature in order to make mobile robots able to perform high-level tasks. However, research aimed at addressing the constraints and limitations encountered in a mobile robotics scenario, like low image resolution, motion blur or tight computational constraints, is still very scarce. In order to facilitate future research in this direction, in this work we present an object detection and recognition dataset acquired using a mobile robotic platform. As a baseline for the dataset, we evaluated the cascade of weak classifiers object detection method from Viola and Jones. |
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Address |
Lisboa |
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Conference |
Robotica |
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Notes |
RV;ADAS |
Approved |
no |
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Call Number |
Admin @ si @ RAV2011 |
Serial |
1777 |
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| Permanent link to this record |
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Author |
Angel Sappa; M.A. Garcia |
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Title |
Aprendiendo a recrear la realidad en 3D |
Type |
Journal |
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Year |
2007 |
Publication |
UAB Divulga, Revista de Divulgacion Cientifica |
Abbreviated Journal |
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Edition |
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ISSN |
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ISBN |
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Medium |
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Area |
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Expedition |
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Conference |
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Notes |
spreading |
Approved |
no |
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Call Number |
ADAS @ adas @ SaG2007c |
Serial |
1470 |
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| Permanent link to this record |
