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Victor Ponce; Sergio Escalera; Marc Perez; Oriol Janes; Xavier Baro |
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Title |
Non-Verbal Communication Analysis in Victim-Offender Mediations |
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Journal Article |
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2015 |
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Pattern Recognition Letters |
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PRL |
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67 |
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1 |
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19-27 |
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Victim–Offender Mediation; Multi-modal human behavior analysis; Face and gesture recognition; Social signal processing; Computer vision; Machine learning |
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We present a non-invasive ambient intelligence framework for the semi-automatic analysis of non-verbal communication applied to the restorative justice field. We propose the use of computer vision and social signal processing technologies in real scenarios of Victim–Offender Mediations, applying feature extraction techniques to multi-modal audio-RGB-depth data. We compute a set of behavioral indicators that define communicative cues from the fields of psychology and observational methodology. We test our methodology on data captured in real Victim–Offender Mediation sessions in Catalonia. We define the ground truth based on expert opinions when annotating the observed social responses. Using different state of the art binary classification approaches, our system achieves recognition accuracies of 86% when predicting satisfaction, and 79% when predicting both agreement and receptivity. Applying a regression strategy, we obtain a mean deviation for the predictions between 0.5 and 0.7 in the range [1–5] for the computed social signals. |
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HuPBA;MV |
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Admin @ si @ PEP2015 |
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2583 |
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Author |
Daniel Ponsa; Antonio Lopez; Joan Serrat; Felipe Lumbreras; T. Graf |
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Title |
Multiple Vehicle 3D Tracking Using an Unscented Kalman Filter |
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Miscellaneous |
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2005 |
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Proceedings of the 8th International IEEE Conference on Intelligent Transportation Systems, 1108–1113, ISBN:0–7803–9216–7 |
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vehicle detection |
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Vienna (Austria) |
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ADAS @ adas @ PLS2005 |
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615 |
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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 |
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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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ADAS @ adas @ PoL2007a |
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785 |
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Daniel Ponsa; Antonio Lopez |
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Title |
Cascade of Classifiers for Vehicle Detection |
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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 |
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ADAS @ adas @ PoL2007c |
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935 |
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Author |
Daniel Ponsa; Joan Serrat; Antonio Lopez |
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Title |
On-board image-based vehicle detection and tracking |
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2011 |
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Transactions of the Institute of Measurement and Control |
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TIM |
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33 |
Issue |
7 |
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783-805 |
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Keywords |
vehicle detection |
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In this paper we present a computer vision system for daytime vehicle detection and localization, an essential step in the development of several types of advanced driver assistance systems. It has a reduced processing time and high accuracy thanks to the combination of vehicle detection with lane-markings estimation and temporal tracking of both vehicles and lane markings. Concerning vehicle detection, our main contribution is a frame scanning process that inspects images according to the geometry of image formation, and with an Adaboost-based detector that is robust to the variability in the different vehicle types (car, van, truck) and lighting conditions. In addition, we propose a new method to estimate the most likely three-dimensional locations of vehicles on the road ahead. With regards to the lane-markings estimation component, we have two main contributions. First, we employ a different image feature to the other commonly used edges: we use ridges, which are better suited to this problem. Second, we adapt RANSAC, a generic robust estimation method, to fit a parametric model of a pair of lane markings to the image features. We qualitatively assess our vehicle detection system in sequences captured on several road types and under very different lighting conditions. The processed videos are available on a web page associated with this paper. A quantitative evaluation of the system has shown quite accurate results (a low number of false positives and negatives) at a reasonable computation time. |
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ADAS |
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ADAS @ adas @ PSL2011 |
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1413 |
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Author |
Mohamed Ilyes Lakhal; Hakan Cevikalp; Sergio Escalera |
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Title |
CRN: End-to-end Convolutional Recurrent Network Structure Applied to Vehicle Classification |
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Conference Article |
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2018 |
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13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications |
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5 |
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137-144 |
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Vehicle Classification; Deep Learning; End-to-end Learning |
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Vehicle type classification is considered to be a central part of Intelligent Traffic Systems. In the recent years, deep learning methods have emerged in as being the state-of-the-art in many computer vision tasks. In this paper, we present a novel yet simple deep learning framework for the vehicle type classification problem. We propose an end-to-end trainable system, that combines convolution neural network for feature extraction and recurrent neural network as a classifier. The recurrent network structure is used to handle various types of feature inputs, and at the same time allows to produce a single or a set of class predictions. In order to assess the effectiveness of our solution, we have conducted a set of experiments in two public datasets, obtaining state of the art results. In addition, we also report results on the newly released MIO-TCD dataset. |
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Funchal; Madeira; Portugal; January 2018 |
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VISAPP |
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HUPBA |
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Admin @ si @ LCE2018a |
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3094 |
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Author |
Aura Hernandez-Sabate; Debora Gil; David Roche; Monica M. S. Matsumoto; Sergio S. Furuie |
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Title |
Inferring the Performance of Medical Imaging Algorithms |
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Conference Article |
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2011 |
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14th International Conference on Computer Analysis of Images and Patterns |
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6854 |
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520-528 |
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Validation, Statistical Inference, Medical Imaging Algorithms. |
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Evaluation of the performance and limitations of medical imaging algorithms is essential to estimate their impact in social, economic or clinical aspects. However, validation of medical imaging techniques is a challenging task due to the variety of imaging and clinical problems involved, as well as, the difficulties for systematically extracting a reliable solely ground truth. Although specific validation protocols are reported in any medical imaging paper, there are still two major concerns: definition of standardized methodologies transversal to all problems and generalization of conclusions to the whole clinical data set.
We claim that both issues would be fully solved if we had a statistical model relating ground truth and the output of computational imaging techniques. Such a statistical model could conclude to what extent the algorithm behaves like the ground truth from the analysis of a sampling of the validation data set. We present a statistical inference framework reporting the agreement and describing the relationship of two quantities. We show its transversality by applying it to validation of two different tasks: contour segmentation and landmark correspondence. |
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Sevilla |
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Springer-Verlag Berlin Heidelberg |
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Berlin |
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Pedro Real; Daniel Diaz-Pernil; Helena Molina-Abril; Ainhoa Berciano; Walter Kropatsch |
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LNCS |
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CAIP |
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IAM; ADAS |
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IAM @ iam @ HGR2011 |
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1676 |
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Author |
Debora Gil; Oriol Rodriguez-Leon; Petia Radeva; Aura Hernandez-Sabate |
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Title |
Assessing Artery Motion Compensation in IVUS |
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Book Chapter |
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2007 |
Publication |
Computer Analysis Of Images And Patterns |
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LNCS |
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4673 |
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213-220 |
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validation standards; quality measures; IVUS motion compensation; conservation laws; Fourier development |
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Cardiac dynamics suppression is a main issue for visual improvement and computation of tissue mechanical properties in IntraVascular UltraSound (IVUS). Although in recent times several motion compensation techniques have arisen, there is a lack of objective evaluation of motion reduction in in vivo pullbacks. We consider that the assessment protocol deserves special attention for the sake of a clinical applicability as reliable as possible. Our work focuses on defining a quality measure and a validation protocol assessing IVUS motion compensation. On the grounds of continuum mechanics laws we introduce a novel score measuring motion reduction in in vivo sequences. Synthetic experiments validate the proposed score as measure of motion parameters accuracy; while results in in vivo pullbacks show its reliability in clinical cases. |
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Springerlink |
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Heidelberg |
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Lecture Notes in Computer Science |
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978-3-540-74271-5 |
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IAM;MILAB |
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IAM @ iam @ GRR2007 |
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1540 |
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Aura Hernandez-Sabate; Debora Gil; Albert Teis |
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How Do Conservation Laws Define a Motion Suppression Score in In-Vivo Ivus Sequences? |
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Conference Article |
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2007 |
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Proc. IEEE Ultrasonics Symp |
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2231-2234 |
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validation standards; IVUS motion compensation; conservation laws. |
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Evaluation of arterial tissue biomechanics for diagnosis and treatment of cardiovascular diseases is an active research field in the biomedical imaging processing area. IntraVascular UltraSound (IVUS) is a unique tool for such assessment since it reflects tissue morphology and deformation. A proper quantification and visualization of both properties is hindered by vessel structures misalignments introduced by cardiac dynamics. This has encouraged development of IVUS motion compensation techniques. However, there is a lack of an objective evaluation of motion reduction ensuring a reliable clinical application This work reports a novel score, the Conservation of Density Rate (CDR), for validation of motion compensation in in-vivo pullbacks. Synthetic experiments validate the proposed score as measure of motion parameters accuracy; while results in in vivo pullbacks show its reliability in clinical cases. |
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IAM |
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IAM @ iam @ HTG2007 |
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1550 |
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Cesar de Souza; Adrien Gaidon; Eleonora Vig; Antonio Lopez |
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System and method for video classification using a hybrid unsupervised and supervised multi-layer architecture |
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2018 |
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US9946933B2 |
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US9946933B2 |
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A computer-implemented video classification method and system are disclosed. The method includes receiving an input video including a sequence of frames. At least one transformation of the input video is generated, each transformation including a sequence of frames. For the input video and each transformation, local descriptors are extracted from the respective sequence of frames. The local descriptors of the input video and each transformation are aggregated to form an aggregated feature vector with a first set of processing layers learned using unsupervised learning. An output classification value is generated for the input video, based on the aggregated feature vector with a second set of processing layers learned using supervised learning. |
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ADAS; 600.118 |
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Admin @ si @ SGV2018 |
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3255 |
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