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Author |
Monica Piñol; Angel Sappa; Angeles Lopez; Ricardo Toledo |
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Title |
Feature Selection Based on Reinforcement Learning for Object Recognition |
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Conference Article |
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Year |
2012 |
Publication |
Adaptive Learning Agents Workshop |
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33-39 |
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Address |
Valencia |
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ALA |
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ADAS; RV |
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Admin @ si @ PSL2012 |
Serial |
2018 |
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Author |
Ariel Amato; Angel Sappa; Alicia Fornes; Felipe Lumbreras; Josep Llados |
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Title |
Divide and Conquer: Atomizing and Parallelizing A Task in A Mobile Crowdsourcing Platform |
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Conference Article |
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Year |
2013 |
Publication |
2nd International ACM Workshop on Crowdsourcing for Multimedia |
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Pages |
21-22 |
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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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Barcelona; October 2013 |
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978-1-4503-2396-3 |
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CrowdMM |
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ADAS; ISE; DAG; 600.054; 600.055; 600.045; 600.061; 602.006 |
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Call Number |
Admin @ si @ SLA2013 |
Serial |
2335 |
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Author |
Jose Carlos Rubio; Joan Serrat; Antonio Lopez |
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Title |
Video Co-segmentation |
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Conference Article |
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Year |
2012 |
Publication |
11th Asian Conference on Computer Vision |
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Volume |
7725 |
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Pages |
13-24 |
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Abstract |
Segmentation of a single image is in general a highly underconstrained problem. A frequent approach to solve it is to somehow provide prior knowledge or constraints on how the objects of interest look like (in terms of their shape, size, color, location or structure). Image co-segmentation trades the need for such knowledge for something much easier to obtain, namely, additional images showing the object from other viewpoints. Now the segmentation problem is posed as one of differentiating the similar object regions in all the images from the more varying background. In this paper, for the first time, we extend this approach to video segmentation: given two or more video sequences showing the same object (or objects belonging to the same class) moving in a similar manner, we aim to outline its region in all the frames. In addition, the method works in an unsupervised manner, by learning to segment at testing time. We compare favorably with two state-of-the-art methods on video segmentation and report results on benchmark videos. |
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Daejeon, Korea |
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Springer Berlin Heidelberg |
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LNCS |
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0302-9743 |
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978-3-642-37443-2 |
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ACCV |
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ADAS |
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no |
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Call Number |
Admin @ si @ RSL2012d |
Serial |
2153 |
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Author |
Arnau Ramisa; Shrihari Vasudevan; David Aldavert; Ricardo Toledo; Ramon Lopez de Mantaras |
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Title |
Evaluation of the SIFT Object Recognition Method in Mobile Robots: Frontiers in Artificial Intelligence and Applications |
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Conference Article |
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Year |
2009 |
Publication |
12th International Conference of the Catalan Association for Artificial Intelligence |
Abbreviated Journal |
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Volume |
202 |
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Pages |
9-18 |
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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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Cardona, Spain |
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0922-6389 |
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978-1-60750-061-2 |
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CCIA |
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ADAS |
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no |
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Call Number |
Admin @ si @ RVA2009 |
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1248 |
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Author |
Diego Cheda; Daniel Ponsa; Antonio Lopez |
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Title |
Pedestrian Candidates Generation using Monocular Cues |
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Conference Article |
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Year |
2012 |
Publication |
IEEE Intelligent Vehicles Symposium |
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Pages |
7-12 |
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Keywords |
pedestrian detection |
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Abstract |
Common techniques for pedestrian candidates generation (e.g., sliding window approaches) are based on an exhaustive search over the image. This implies that the number of windows produced is huge, which translates into a significant time consumption in the classification stage. In this paper, we propose a method that significantly reduces the number of windows to be considered by a classifier. Our method is a monocular one that exploits geometric and depth information available on single images. Both representations of the world are fused together to generate pedestrian candidates based on an underlying model which is focused only on objects standing vertically on the ground plane and having certain height, according with their depths on the scene. We evaluate our algorithm on a challenging dataset and demonstrate its application for pedestrian detection, where a considerable reduction in the number of candidate windows is reached. |
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IEEE Xplore |
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ISSN |
1931-0587 |
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978-1-4673-2119-8 |
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Conference |
IV |
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ADAS |
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Call Number |
Admin @ si @ CPL2012c; ADAS @ adas @ cpl2012d |
Serial |
2013 |
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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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Conference Article |
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Year |
2015 |
Publication |
Pattern Recognition and Image Analysis, Proceedings of 7th Iberian Conference , ibPRIA 2015 |
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3-12 |
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Keywords |
SSL; Pedestrian Detection |
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Abstract |
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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ACDC |
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IbPRIA |
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ADAS; 600.057; 600.054; 600.076 |
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Call Number |
GRV2015; ADAS @ adas @ GRV2015 |
Serial |
2454 |
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Author |
Alexey Dosovitskiy; German Ros; Felipe Codevilla; Antonio Lopez; Vladlen Koltun |
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Title |
CARLA: An Open Urban Driving Simulator |
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Conference Article |
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2017 |
Publication |
1st Annual Conference on Robot Learning. Proceedings of Machine Learning |
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78 |
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1-16 |
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Keywords |
Autonomous driving; sensorimotor control; simulation |
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We introduce CARLA, an open-source simulator for autonomous driving research. CARLA has been developed from the ground up to support development, training, and validation of autonomous urban driving systems. In addition to open-source code and protocols, CARLA provides open digital assets (urban layouts, buildings, vehicles) that were created for this purpose and can be used freely. The simulation platform supports flexible specification of sensor suites and environmental conditions. We use CARLA to study the performance of three approaches to autonomous driving: a classic modular pipeline, an endto-end
model trained via imitation learning, and an end-to-end model trained via
reinforcement learning. The approaches are evaluated in controlled scenarios of
increasing difficulty, and their performance is examined via metrics provided by CARLA, illustrating the platform’s utility for autonomous driving research. |
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Mountain View; CA; USA; November 2017 |
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CORL |
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ADAS; 600.085; 600.118 |
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no |
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Call Number |
Admin @ si @ DRC2017 |
Serial |
2988 |
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Author |
Joan Serrat; J. Argemi; Juan J. Villanueva |
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Title |
Automatization of TW2 method using a knowledge-based image analysis system. |
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Year |
1991 |
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VIth International Congress of Auxology. |
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Madrid |
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ADAS @ adas @ SAV1991 |
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259 |
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Author |
A. Dupuy; Joan Serrat; Jordi Vitria; J. Pladellorens |
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Title |
Analysis of gammagraphic images by mathematical morphology. |
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1991 |
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Pattern Recognition and image Analysis: IV Spanish Symposium of Pattern Recognition and image Analysis, World Scientific Pub. |
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ADAS;OR;MV |
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ADAS @ adas @ DSV1991 |
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262 |
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Joan Serrat; Jordi Vitria; J. Pladellorens |
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Title |
Morphological Segmentation of Heart Scintigraphic image Sequences. |
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1991 |
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Computer Assisted Radiology. |
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Berlin |
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ADAS;OR;MV |
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ADAS @ adas @ SVP1991 |
Serial |
263 |
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