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Marçal Rusiñol, Dimosthenis Karatzas, & Josep Llados. (2015). Automatic Verification of Properly Signed Multi-page Document Images. In Proceedings of the Eleventh International Symposium on Visual Computing (Vol. 9475, pp. 327–336). LNCS, 9475.
Abstract: In this paper we present an industrial application for the automatic screening of incoming multi-page documents in a banking workflow aimed at determining whether these documents are properly signed or not. The proposed method is divided in three main steps. First individual pages are classified in order to identify the pages that should contain a signature. In a second step, we segment within those key pages the location where the signatures should appear. The last step checks whether the signatures are present or not. Our method is tested in a real large-scale environment and we report the results when checking two different types of real multi-page contracts, having in total more than 14,500 pages.
Keywords: Document Image; Manual Inspection; Signature Verification; Rejection Criterion; Document Flow
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Victor Campmany, Sergio Silva, Juan Carlos Moure, Antoni Espinosa, David Vazquez, & Antonio Lopez. (2015). GPU-based pedestrian detection for autonomous driving. In Programming and Tunning Massive Parallel Systems. PUMPS.
Abstract: Pedestrian detection for autonomous driving has gained a lot of prominence during the last few years. Besides the fact that it is one of the hardest tasks within computer vision, it involves huge computational costs. The real-time constraints in the field are tight, and regular processors are not able to handle the workload obtaining an acceptable ratio of frames per second (fps). Moreover, multiple cameras are required to obtain accurate results, so the need to speed up the process is even higher. Taking the work in [1] as our baseline, we propose a CUDA implementation of a pedestrian detection system. Further, we introduce significant algorithmic adjustments and optimizations to adapt the problem to the GPU architecture. The aim is to provide a system capable of running in real-time obtaining reliable results.
Keywords: Autonomous Driving; ADAS; CUDA; Pedestrian Detection
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Sergio Silva, Victor Campmany, Laura Sellart, Juan Carlos Moure, Antoni Espinosa, David Vazquez, et al. (2015). Autonomous GPU-based Driving. In Programming and Tunning Massive Parallel Systems.
Abstract: Human factors cause most driving accidents; this is why nowadays is common to hear about autonomous driving as an alternative. Autonomous driving will not only increase safety, but also will develop a system of cooperative self-driving cars that will reduce pollution and congestion. Furthermore, it will provide more freedom to handicapped people, elderly or kids.
Autonomous Driving requires perceiving and understanding the vehicle environment (e.g., road, traffic signs, pedestrians, vehicles) using sensors (e.g., cameras, lidars, sonars, and radars), selflocalization (requiring GPS, inertial sensors and visual localization in precise maps), controlling the vehicle and planning the routes. These algorithms require high computation capability, and thanks to NVIDIA GPU acceleration this starts to become feasible.
NVIDIA® is developing a new platform for boosting the Autonomous Driving capabilities that is able of managing the vehicle via CAN-Bus: the Drive™ PX. It has 8 ARM cores with dual accelerated Tegra® X1 chips. It has 12 synchronized camera inputs for 360º vehicle perception, 4G and Wi-Fi capabilities allowing vehicle communications and GPS and inertial sensors inputs for self-localization.
Our research group has been selected for testing Drive™ PX. Accordingly, we are developing a Drive™ PX based autonomous car. Currently, we are porting our previous CPU based algorithms (e.g., Lane Departure Warning, Collision Warning, Automatic Cruise Control, Pedestrian Protection, or Semantic Segmentation) for running in the GPU.
Keywords: Autonomous Driving; ADAS; CUDA
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Xavier Otazu, Olivier Penacchio, & Xim Cerda-Company. (2015). Brightness and colour induction through contextual influences in V1. In Scottish Vision Group 2015 SGV2015 (Vol. 12, pp. 1208–2012).
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Hanne Kause, Aura Hernandez-Sabate, Patricia Marquez, Andrea Fuster, Luc Florack, Hans van Assen, et al. (2015). Confidence Measures for Assessing the HARP Algorithm in Tagged Magnetic Resonance Imaging. In Statistical Atlases and Computational Models of the Heart. Revised selected papers of Imaging and Modelling Challenges 6th International Workshop, STACOM 2015, Held in Conjunction with MICCAI 2015 (Vol. 9534, pp. 69–79). LNCS. Springer International Publishing.
Abstract: Cardiac deformation and changes therein have been linked to pathologies. Both can be extracted in detail from tagged Magnetic Resonance Imaging (tMRI) using harmonic phase (HARP) images. Although point tracking algorithms have shown to have high accuracies on HARP images, these vary with position. Detecting and discarding areas with unreliable results is crucial for use in clinical support systems. This paper assesses the capability of two confidence measures (CMs), based on energy and image structure, for detecting locations with reduced accuracy in motion tracking results. These CMs were tested on a database of simulated tMRI images containing the most common artifacts that may affect tracking accuracy. CM performance is assessed based on its capability for HARP tracking error bounding and compared in terms of significant differences detected using a multi comparison analysis of variance that takes into account the most influential factors on HARP tracking performance. Results showed that the CM based on image structure was better suited to detect unreliable optical flow vectors. In addition, it was shown that CMs can be used to detect optical flow vectors with large errors in order to improve the optical flow obtained with the HARP tracking algorithm.
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Fernando Vilariño, Dimosthenis Karatzas, Marcos Catalan, & Alberto Valcarcel. (2015). An horizon for the Public Library as a place for innovation and creativity. The Library Living Lab in Volpelleres. In The White Book on Public Library Network from Diputació de Barcelona.
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G. Zahnd, Simone Balocco, A. Serusclat, P. Moulin, M. Orkisz, & D. Vray. (2015). Progressive attenuation of the longitudinal kinetics in the common carotid artery: preliminary in vivo assessment Ultrasound in Medicine and Biology. UMB - Ultrasound in Medicine and Biology, 41(1), 339–345.
Abstract: Longitudinal kinetics (LOKI) of the arterial wall consists of the shearing motion of the intima-media complex over the adventitia layer in the direction parallel to the blood flow during the cardiac cycle. The aim of this study was to investigate the local variability of LOKI amplitude along the length of the vessel. By use of a previously validated motion-estimation framework, 35 in vivo longitudinal B-mode ultrasound cine loops of healthy common carotid arteries were analyzed. Results indicated that LOKI amplitude is progressively attenuated along the length of the artery, as it is larger in regions located on the proximal side of the image (i.e., toward the heart) and smaller in regions located on the distal side of the image (i.e., toward the head), with an average attenuation coefficient of -2.5 ± 2.0%/mm. Reported for the first time in this study, this phenomenon is likely to be of great importance in improving understanding of atherosclerosis mechanisms, and has the potential to be a novel index of arterial stiffness.
Keywords: Arterial stiffness; Atherosclerosis; Common carotid artery; Longitudinal kinetics; Motion tracking; Ultrasound imaging
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J.Kuhn, A.Nussbaumer, J.Pirker, Dimosthenis Karatzas, A. Pagani, O.Conlan, et al. (2015). Advancing Physics Learning Through Traversing a Multi-Modal Experimentation Space. In Workshop Proceedings on the 11th International Conference on Intelligent Environments (Vol. 19, pp. 373–380).
Abstract: Translating conceptual knowledge into real world experiences presents a significant educational challenge. This position paper presents an approach that supports learners in moving seamlessly between conceptual learning and their application in the real world by bringing physical and virtual experiments into everyday settings. Learners are empowered in conducting these situated experiments in a variety of physical settings by leveraging state of the art mobile, augmented reality, and virtual reality technology. A blend of mobile-based multi-sensory physical experiments, augmented reality and enabling virtual environments can allow learners to bridge their conceptual learning with tangible experiences in a completely novel manner. This approach focuses on the learner by applying self-regulated personalised learning techniques, underpinned by innovative pedagogical approaches and adaptation techniques, to ensure that the needs and preferences of each learner are catered for individually.
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