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Author |
Xavier Baro; Jordi Gonzalez; Junior Fabian; Miguel Angel Bautista; Marc Oliu; Hugo Jair Escalante; Isabelle Guyon; Sergio Escalera |
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Title |
ChaLearn Looking at People 2015 challenges: action spotting and cultural event recognition |
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Conference Article |
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2015 |
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2015 IEEE Conference on Computer Vision and Pattern Recognition Worshops (CVPRW) |
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1-9 |
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Following previous series on Looking at People (LAP) challenges [6, 5, 4], ChaLearn ran two competitions to be presented at CVPR 2015: action/interaction spotting and cultural event recognition in RGB data. We ran a second round on human activity recognition on RGB data sequences. In terms of cultural event recognition, tens of categories have to be recognized. This involves scene understanding and human analysis. This paper summarizes the two performed challenges and obtained results. Details of the ChaLearn LAP competitions can be found at http://gesture.chalearn.org/. |
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Boston; EEUU; June 2015 |
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CVPRW |
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HuPBA;MV |
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no |
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2652 |
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Andres Traumann; Sergio Escalera; Gholamreza Anbarjafari |
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Title |
A New Retexturing Method for Virtual Fitting Room Using Kinect 2 Camera |
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Conference Article |
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2015 |
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2015 IEEE Conference on Computer Vision and Pattern Recognition Worshops (CVPRW) |
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75-79 |
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Boston; EEUU; June 2015 |
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CVPRW |
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HuPBA;MILAB |
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Admin @ si @ TEA2015 |
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2653 |
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Ramin Irani; Kamal Nasrollahi; Chris Bahnsen; D.H. Lundtoft; Thomas B. Moeslund; Marc O. Simon; Ciprian Corneanu; Sergio Escalera; Tanja L. Pedersen; Maria-Louise Klitgaard; Laura Petrini |
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Title |
Spatio-temporal Analysis of RGB-D-T Facial Images for Multimodal Pain Level Recognition |
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Conference Article |
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2015 |
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2015 IEEE Conference on Computer Vision and Pattern Recognition Worshops (CVPRW) |
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88-95 |
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Pain is a vital sign of human health and its automatic detection can be of crucial importance in many different contexts, including medical scenarios. While most available computer vision techniques are based on RGB, in this paper, we investigate the effect of combining RGB, depth, and thermal
facial images for pain detection and pain intensity level recognition. For this purpose, we extract energies released by facial pixels using a spatiotemporal filter. Experiments on a group of 12 elderly people applying the multimodal approach show that the proposed method successfully detects pain and recognizes between three intensity levels in 82% of the analyzed frames improving more than 6% over RGB only analysis in similar conditions. |
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Boston; EEUU; June 2015 |
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CVPRW |
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HuPBA;MILAB |
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no |
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Admin @ si @ INB2015 |
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2654 |
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Mohammad Ali Bagheri; Qigang Gao; Sergio Escalera; Albert Clapes; Kamal Nasrollahi; Michael Holte; Thomas B. Moeslund |
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Title |
Keep it Accurate and Diverse: Enhancing Action Recognition Performance by Ensemble Learning |
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Conference Article |
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2015 |
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IEEE Conference on Computer Vision and Pattern Recognition Worshops (CVPRW) |
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22-29 |
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The performance of different action recognition techniques has recently been studied by several computer vision researchers. However, the potential improvement in classification through classifier fusion by ensemble-based methods has remained unattended. In this work, we evaluate the performance of an ensemble of action learning techniques, each performing the recognition task from a different perspective.
The underlying idea is that instead of aiming a very sophisticated and powerful representation/learning technique, we can learn action categories using a set of relatively simple and diverse classifiers, each trained with different feature set. In addition, combining the outputs of several learners can reduce the risk of an unfortunate selection of a learner on an unseen action recognition scenario.
This leads to having a more robust and general-applicable framework. In order to improve the recognition performance, a powerful combination strategy is utilized based on the Dempster-Shafer theory, which can effectively make use
of diversity of base learners trained on different sources of information. The recognition results of the individual classifiers are compared with those obtained from fusing the classifiers’ output, showing enhanced performance of the proposed methodology. |
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Boston; EEUU; June 2015 |
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CVPRW |
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HuPBA;MILAB |
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no |
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Admin @ si @ BGE2015 |
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2655 |
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Author |
Onur Ferhat; Arcadi Llanza; Fernando Vilariño |
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Gaze interaction for multi-display systems using natural light eye-tracker |
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Conference Article |
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2015 |
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2nd International Workshop on Solutions for Automatic Gaze Data Analysis |
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Bielefeld; Germany; September 2015 |
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SAGA |
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MV;SIAI |
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no |
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Admin @ si @ FLV2015b |
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2676 |
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Author |
Juan Ignacio Toledo; Jordi Cucurull; Jordi Puiggali; Alicia Fornes; Josep Llados |
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Title |
Document Analysis Techniques for Automatic Electoral Document Processing: A Survey |
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Conference Article |
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2015 |
Publication |
E-Voting and Identity, Proceedings of 5th international conference, VoteID 2015 |
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139-141 |
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Document image analysis; Computer vision; Paper ballots; Paper based elections; Optical scan; Tally |
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Abstract |
In this paper, we will discuss the most common challenges in electoral document processing and study the different solutions from the document analysis community that can be applied in each case. We will cover Optical Mark Recognition techniques to detect voter selections in the Australian Ballot, handwritten number recognition for preferential elections and handwriting recognition for write-in areas. We will also propose some particular adjustments that can be made to those general techniques in the specific context of electoral documents. |
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Bern; Switzerland; September 2015 |
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VoteID |
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DAG; 600.061; 602.006; 600.077 |
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no |
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Admin @ si @ TCP2015 |
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2641 |
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Author |
Pau Riba; Josep Llados; Alicia Fornes; Anjan Dutta |
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Title |
Large-scale Graph Indexing using Binary Embeddings of Node Contexts |
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Conference Article |
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2015 |
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10th IAPR-TC15 Workshop on Graph-based Representations in Pattern Recognition |
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9069 |
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208-217 |
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Graph matching; Graph indexing; Application in document analysis; Word spotting; Binary embedding |
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Graph-based representations are experiencing a growing usage in visual recognition and retrieval due to their representational power in front of classical appearance-based representations in terms of feature vectors. Retrieving a query graph from a large dataset of graphs has the drawback of the high computational complexity required to compare the query and the target graphs. The most important property for a large-scale retrieval is the search time complexity to be sub-linear in the number of database examples. In this paper we propose a fast indexation formalism for graph retrieval. A binary embedding is defined as hashing keys for graph nodes. Given a database of labeled graphs, graph nodes are complemented with vectors of attributes representing their local context. Hence, each attribute counts the length of a walk of order k originated in a vertex with label l. Each attribute vector is converted to a binary code applying a binary-valued hash function. Therefore, graph retrieval is formulated in terms of finding target graphs in the database whose nodes have a small Hamming distance from the query nodes, easily computed with bitwise logical operators. As an application example, we validate the performance of the proposed methods in a handwritten word spotting scenario in images of historical documents. |
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Beijing; China; May 2015 |
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Springer International Publishing |
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C.-L.Liu; B.Luo; W.G.Kropatsch; J.Cheng |
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0302-9743 |
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978-3-319-18223-0 |
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GbRPR |
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DAG; 600.061; 602.006; 600.077 |
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no |
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Admin @ si @ RLF2015a |
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2618 |
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Author |
Carles Sanchez; Jorge Bernal; F. Javier Sanchez; Marta Diez-Ferrer; Antoni Rosell; Debora Gil |
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Title |
Towards On-line Quantification of Tracheal Stenosis from Videobronchoscopy |
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Conference Article |
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2015 |
Publication |
6th International Conference on Information Processing in Computer-Assisted Interventions IPCAI2015 |
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10 |
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6 |
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935-945 |
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PURPOSE:
Lack of objective measurement of tracheal obstruction degree has a negative impact on the chosen treatment prone to lead to unnecessary repeated explorations and other scanners. Accurate computation of tracheal stenosis in videobronchoscopy would constitute a breakthrough for this noninvasive technique and a reduction in operation cost for the public health service.
METHODS:
Stenosis calculation is based on the comparison of the region delimited by the lumen in an obstructed frame and the region delimited by the first visible ring in a healthy frame. We propose a parametric strategy for the extraction of lumen and tracheal ring regions based on models of their geometry and appearance that guide a deformable model. To ensure a systematic applicability, we present a statistical framework to choose optimal parametric values and a strategy to choose the frames that minimize the impact of scope optical distortion.
RESULTS:
Our method has been tested in 40 cases covering different stenosed tracheas. Experiments report a non- clinically relevant [Formula: see text] of discrepancy in the calculated stenotic area and a computational time allowing online implementation in the operating room.
CONCLUSIONS:
Our methodology allows reliable measurements of airway narrowing in the operating room. To fully assess its clinical impact, a prospective clinical trial should be done. |
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Barcelona; Spain; June 2015 |
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IPCAI |
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IAM; MV; 600.075 |
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Admin @ si @ SBS2015b |
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2613 |
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Author |
Sergio Vera; Miguel Angel Gonzalez Ballester; Debora Gil |
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A Novel Cochlear Reference Frame Based On The Laplace Equation |
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2015 |
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29th international Congress and Exhibition on Computer Assisted Radiology and Surgery |
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10 |
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1 |
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1-312 |
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Poster |
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Barcelona; Spain; June 2015 |
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CARS |
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IAM; 600.075 |
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no |
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Admin @ si @ VGG2015 |
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2615 |
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Carles Sanchez; Debora Gil; R. Tazi; Jorge Bernal; Y. Ruiz; L. Planas; F. Javier Sanchez; Antoni Rosell |
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Quasi-real time digital assessment of Central Airway Obstruction |
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Conference Article |
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2015 |
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3rd European congress for bronchology and interventional pulmonology ECBIP2015 |
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Barcelona; Spain; April 2015 |
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ECBIP |
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IAM; MV; 600.075 |
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SGT2015 |
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2612 |
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Victor Campmany; Sergio Silva; Juan Carlos Moure; Antoni Espinosa; David Vazquez; Antonio Lopez |
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Title |
GPU-based pedestrian detection for autonomous driving |
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2015 |
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Programming and Tunning Massive Parallel Systems |
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PUMPS |
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Autonomous Driving; ADAS; CUDA; Pedestrian Detection |
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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. |
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Barcelona; Spain |
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ADAS; 600.076; 600.082; 600.085 |
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ADAS @ adas @ CSM2015 |
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2644 |
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Sergio Silva; Victor Campmany; Laura Sellart; Juan Carlos Moure; Antoni Espinosa; David Vazquez; Antonio Lopez |
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Autonomous GPU-based Driving |
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2015 |
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Programming and Tunning Massive Parallel Systems |
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PUMPS |
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Autonomous Driving; ADAS; CUDA |
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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. |
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Barcelona; Spain |
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ADAS; 600.076; 600.082; 600.085 |
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ADAS @ adas @ SCS2015 |
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2645 |
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Author |
Xavier Otazu; Olivier Penacchio; Xim Cerda-Company |
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An excitatory-inhibitory firing rate model accounts for brightness induction, colour induction and visual discomfort |
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Conference Article |
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2015 |
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Barcelona Computational, Cognitive and Systems Neuroscience |
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Barcelona; June 2015 |
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BARCCSYN |
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NEUROBIT; |
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Admin @ si @ OPC2015b |
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2634 |
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M. Cruz; Cristhian A. Aguilera-Carrasco; Boris X. Vintimilla; Ricardo Toledo; Angel Sappa |
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Cross-spectral image registration and fusion: an evaluation study |
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Conference Article |
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2015 |
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2nd International Conference on Machine Vision and Machine Learning |
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multispectral imaging; image registration; data fusion; infrared and visible spectra |
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This paper presents a preliminary study on the registration and fusion of cross-spectral imaging. The objective is to evaluate the validity of widely used computer vision approaches when they are applied at different
spectral bands. In particular, we are interested in merging images from the infrared (both long wave infrared: LWIR and near infrared: NIR) and visible spectrum (VS). Experimental results with different data sets are presented. |
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Barcelona; July 2015 |
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MVML |
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ADAS; 600.076 |
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Admin @ si @ CAV2015 |
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2629 |
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Jiaolong Xu |
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Domain Adaptation of Deformable Part-based Models |
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2015 |
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PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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On-board pedestrian detection is crucial for Advanced Driver Assistance Systems
(ADAS). An accurate classication is fundamental for vision-based pedestrian detection.
The underlying assumption for learning classiers is that the training set and the deployment environment (testing) follow the same probability distribution regarding the features used by the classiers. However, in practice, there are dierent reasons that can break this constancy assumption. Accordingly, reusing existing classiers by adapting them from the previous training environment (source domain) to the new testing one (target domain) is an approach with increasing acceptance in the computer vision community. In this thesis we focus on the domain adaptation of deformable part-based models (DPMs) for pedestrian detection. As a prof of concept, we use a computer graphic based synthetic dataset, i.e. a virtual world, as the source domain, and adapt the virtual-world trained DPM detector to various real-world dataset.
We start by exploiting the maximum detection accuracy of the virtual-world
trained DPM. Even though, when operating in various real-world datasets, the virtualworld trained detector still suer from accuracy degradation due to the domain gap of virtual and real worlds. We then focus on domain adaptation of DPM. At the rst step, we consider single source and single target domain adaptation and propose two batch learning methods, namely A-SSVM and SA-SSVM. Later, we further consider leveraging multiple target (sub-)domains for progressive domain adaptation and propose a hierarchical adaptive structured SVM (HA-SSVM) for optimization. Finally, we extend HA-SSVM for the challenging online domain adaptation problem, aiming at making the detector to automatically adapt to the target domain online, without any human intervention. All of the proposed methods in this thesis do not require
revisiting source domain data. The evaluations are done on the Caltech pedestrian detection benchmark. Results show that SA-SSVM slightly outperforms A-SSVM and avoids accuracy drops as high as 15 points when comparing with a non-adapted detector. The hierarchical model learned by HA-SSVM further boosts the domain adaptation performance. Finally, the online domain adaptation method has demonstrated that it can achieve comparable accuracy to the batch learned models while not requiring manually label target domain examples. Domain adaptation for pedestrian detection is of paramount importance and a relatively unexplored area. We humbly hope the work in this thesis could provide foundations for future work in this area. |
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April 2015 |
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Ph.D. thesis |
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Antonio Lopez |
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978-84-943427-1-4 |
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ADAS; 600.076 |
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Admin @ si @ Xu2015 |
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2631 |
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