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Ariel Amato, Angel Sappa, Alicia Fornes, Felipe Lumbreras and Josep Llados. 2013. Divide and Conquer: Atomizing and Parallelizing A Task in A Mobile Crowdsourcing Platform. 2nd International ACM Workshop on Crowdsourcing for Multimedia.21–22.
Abstract: In this paper we present some conclusions about the advantages of having an efficient task formulation when a crowdsourcing platform is used. In particular we show how the task atomization and distribution can help to obtain results in an efficient way. Our proposal is based on a recursive splitting of the original task into a set of smaller and simpler tasks. As a result both more accurate and faster solutions are obtained. Our evaluation is performed on a set of ancient documents that need to be digitized.
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Ariel Amato, Felipe Lumbreras and Angel Sappa. 2014. A General-purpose Crowdsourcing Platform for Mobile Devices. 9th International Conference on Computer Vision Theory and Applications.211–215.
Abstract: This paper presents details of a general purpose micro-task on-demand platform based on the crowdsourcing philosophy. This platform was specifically developed for mobile devices in order to exploit the strengths of such devices; namely: i) massivity, ii) ubiquity and iii) embedded sensors. The combined use of mobile platforms and the crowdsourcing model allows to tackle from the simplest to the most complex tasks. Users experience is the highlighted feature of this platform (this fact is extended to both task-proposer and tasksolver). Proper tools according with a specific task are provided to a task-solver in order to perform his/her job in a simpler, faster and appealing way. Moreover, a task can be easily submitted by just selecting predefined templates, which cover a wide range of possible applications. Examples of its usage in computer vision and computer games are provided illustrating the potentiality of the platform.
Keywords: Crowdsourcing Platform; Mobile Crowdsourcing
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Arnau Ramisa, Adriana Tapus, Ramon Lopez de Mantaras and Ricardo Toledo. 2008. Mobile Robot Localization using Panoramic Vision and Combination of Feature Region Detectors. IEEE International Conference on Robotics and Automation,.538–543.
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Arnau Ramisa, David Aldavert, Shrihari Vasudevan, Ricardo Toledo and Ramon Lopez de Mantaras. 2011. The IIIA30 MObile Robot Object Recognition Datset. 11th Portuguese Robotics Open.
Abstract: 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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Arnau Ramisa, Ramon Lopez de Mantaras and Ricardo Toledo. 2007. Comparing Combinations of Feature Regions for Panoramic VSLAM. 4th International Conference on Informatics in Control, Automation and Robotics.292–297.
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Arnau Ramisa, Shrihari Vasudevan, David Aldavert, Ricardo Toledo and Ramon Lopez de Mantaras. 2009. Evaluation of the SIFT Object Recognition Method in Mobile Robots: Frontiers in Artificial Intelligence and Applications. 12th International Conference of the Catalan Association for Artificial Intelligence.9–18.
Abstract: General object recognition in mobile robots is of primary importance in order to enhance the representation of the environment that robots will use for their reasoning processes. Therefore, we contribute reduce this gap by evaluating the SIFT Object Recognition method in a challenging dataset, focusing on issues relevant to mobile robotics. Resistance of the method to the robotics working conditions was found, but it was limited mainly to well-textured objects.
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Aura Hernandez-Sabate, Debora Gil, David Roche, Monica M. S. Matsumoto and Sergio S. Furuie. 2011. Inferring the Performance of Medical Imaging Algorithms. In Pedro Real, Daniel Diaz-Pernil, Helena Molina-Abril, Ainhoa Berciano and Walter Kropatsch, eds. 14th International Conference on Computer Analysis of Images and Patterns. Berlin, Springer-Verlag Berlin Heidelberg, 520–528. (LNCS.)
Abstract: 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.
Keywords: Validation, Statistical Inference, Medical Imaging Algorithms.
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Aura Hernandez-Sabate, Lluis Albarracin, Daniel Calvo and Nuria Gorgorio. 2016. EyeMath: Identifying Mathematics Problem Solving Processes in a RTS Video Game. 5th International Conference Games and Learning Alliance.50–59. (LNCS.)
Abstract: Photorealistic virtual environments are crucial for developing and testing automated driving systems in a safe way during trials. As commercially available simulators are expensive and bulky, this paper presents a low-cost, extendable, and easy-to-use (LEE) virtual environment with the aim to highlight its utility for level 3 driving automation. In particular, an experiment is performed using the presented simulator to explore the influence of different variables regarding control transfer of the car after the system was driving autonomously in a highway scenario. The results show that the speed of the car at the time when the system needs to transfer the control to the human driver is critical.
Keywords: Simulation environment; Automated Driving; Driver-Vehicle interaction
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Carme Julia, Angel Sappa, Felipe Lumbreras and Antonio Lopez. 2008. Recovery of Surface Normals and Reflectance from Different Lighting Conditions. 5th International Conference on Image Analysis and Recognition.315–325. (LNCS.)
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Carme Julia, Angel Sappa, Felipe Lumbreras and Joan Serrat. 2008. Photometric Stereo through and Adapted Alternation Approach. IEEE International Conference on Image Processing,.1500–1503.
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