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Author | Xavier Baro; Jordi Gonzalez; Junior Fabian; Miguel Angel Bautista; Marc Oliu; Hugo Jair Escalante; Isabelle Guyon; Sergio Escalera | ||||
Title | ChaLearn Looking at People 2015 challenges: action spotting and cultural event recognition | Type | Conference Article | ||
Year | 2015 | Publication | 2015 IEEE Conference on Computer Vision and Pattern Recognition Worshops (CVPRW) | Abbreviated Journal | |
Volume | Issue | Pages | 1-9 | ||
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Abstract | 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/. | ||||
Address | Boston; EEUU; June 2015 | ||||
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Area | Expedition | Conference | CVPRW | ||
Notes | HuPBA;MV | Approved | no | ||
Call Number | Serial | 2652 | |||
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Author | Gerard Canal; Cecilio Angulo; Sergio Escalera | ||||
Title | Gesture based Human Multi-Robot interaction | Type | Conference Article | ||
Year | 2015 | Publication | IEEE International Joint Conference on Neural Networks IJCNN2015 | Abbreviated Journal | |
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Abstract | The emergence of robot applications for nontechnical users implies designing new ways of interaction between robotic platforms and users. The main goal of this work is the development of a gestural interface to interact with robots
in a similar way as humans do, allowing the user to provide information of the task with non-verbal communication. The gesture recognition application has been implemented using the Microsoft’s KinectTM v2 sensor. Hence, a real-time algorithm based on skeletal features is described to deal with both, static gestures and dynamic ones, being the latter recognized using a weighted Dynamic Time Warping method. The gesture recognition application has been implemented in a multi-robot case. A NAO humanoid robot is in charge of interacting with the users and respond to the visual signals they produce. Moreover, a wheeled Wifibot robot carries both the sensor and the NAO robot, easing navigation when necessary. A broad set of user tests have been carried out demonstrating that the system is, indeed, a natural approach to human robot interaction, with a fast response and easy to use, showing high gesture recognition rates. |
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Address | Killarney; Ireland; July 2015 | ||||
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Area | Expedition | Conference | IJCNN | ||
Notes | HuPBA;MILAB | Approved | no | ||
Call Number | CAE2015a | Serial | 2651 | ||
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Author | Isabelle Guyon; Kristin Bennett; Gavin Cawley; Hugo Jair Escalante; Sergio Escalera | ||||
Title | The AutoML challenge on codalab | Type | Conference Article | ||
Year | 2015 | Publication | IEEE International Joint Conference on Neural Networks IJCNN2015 | Abbreviated Journal | |
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Address | Killarney; Ireland; July 2015 | ||||
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Area | Expedition | Conference | IJCNN | ||
Notes | HuPBA;MILAB | Approved | no | ||
Call Number | Admin @ si @ GBC2015b | Serial | 2650 | ||
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Author | Kamal Nasrollahi; Sergio Escalera; P. Rasti; Gholamreza Anbarjafari; Xavier Baro; Hugo Jair Escalante; Thomas B. Moeslund | ||||
Title | Deep Learning based Super-Resolution for Improved Action Recognition | Type | Conference Article | ||
Year | 2015 | Publication | 5th International Conference on Image Processing Theory, Tools and Applications IPTA2015 | Abbreviated Journal | |
Volume | Issue | Pages | 67 - 72 | ||
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Abstract | Action recognition systems mostly work with videos of proper quality and resolution. Even most challenging benchmark databases for action recognition, hardly include videos of low-resolution from, e.g., surveillance cameras. In videos recorded by such cameras, due to the distance between people and cameras, people are pictured very small and hence challenge action recognition algorithms. Simple upsampling methods, like bicubic interpolation, cannot retrieve all the detailed information that can help the recognition. To deal with this problem, in this paper we combine results of bicubic interpolation with results of a state-ofthe-art deep learning-based super-resolution algorithm, through an alpha-blending approach. The experimental results obtained on down-sampled version of a large subset of Hoolywood2 benchmark database show the importance of the proposed system in increasing the recognition rate of a state-of-the-art action recognition system for handling low-resolution videos. | ||||
Address | Orleans; France; November 2015 | ||||
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Area | Expedition | Conference | IPTA | ||
Notes | HuPBA;MV | Approved | no | ||
Call Number | Admin @ si @ NER2015 | Serial | 2648 | ||
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Author | Andres Traumann; Gholamreza Anbarjafari; Sergio Escalera | ||||
Title | Accurate 3D Measurement Using Optical Depth Information | Type | Journal Article | ||
Year | 2015 | Publication | Electronic Letters | Abbreviated Journal | EL |
Volume | 51 | Issue | 18 | Pages | 1420-1422 |
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Abstract | A novel three-dimensional measurement technique is proposed. The methodology consists in mapping from the screen coordinates reported by the optical camera to the real world, and integrating distance gradients from the beginning to the end point, while also minimising the error through fitting pixel locations to a smooth curve. The results demonstrate accuracy of less than half a centimetre using Microsoft Kinect II. | ||||
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Notes | HuPBA;MILAB | Approved | no | ||
Call Number | Admin @ si @ TAE2015 | Serial | 2647 | ||
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Author | Onur Ferhat; Arcadi Llanza; Fernando Vilariño | ||||
Title | A Feature-Based Gaze Estimation Algorithm for Natural Light Scenarios | Type | Conference Article | ||
Year | 2015 | Publication | Pattern Recognition and Image Analysis, Proceedings of 7th Iberian Conference , ibPRIA 2015 | Abbreviated Journal | |
Volume | 9117 | Issue | Pages | 569-576 | |
Keywords | Eye tracking; Gaze estimation; Natural light; Webcam | ||||
Abstract | We present an eye tracking system that works with regular webcams. We base our work on open source CVC Eye Tracker [7] and we propose a number of improvements and a novel gaze estimation method. The new method uses features extracted from iris segmentation and it does not fall into the traditional categorization of appearance–based/model–based methods. Our experiments show that our approach reduces the gaze estimation errors by 34 % in the horizontal direction and by 12 % in the vertical direction compared to the baseline system. | ||||
Address | Santiago de Compostela; June 2015 | ||||
Corporate Author | Thesis | ||||
Publisher | Springer International Publishing | Place of Publication | Editor | ||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
Series Volume | Series Issue | Edition | |||
ISSN | 0302-9743 | ISBN | 978-3-319-19389-2 | Medium | |
Area | Expedition | Conference | IbPRIA | ||
Notes | MV;SIAI | Approved | no | ||
Call Number | Admin @ si @ FLV2015a | Serial | 2646 | ||
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Author | Sergio Silva; Victor Campmany; Laura Sellart; Juan Carlos Moure; Antoni Espinosa; David Vazquez; Antonio Lopez | ||||
Title | Autonomous GPU-based Driving | Type | Abstract | ||
Year | 2015 | Publication | Programming and Tunning Massive Parallel Systems | Abbreviated Journal | PUMPS |
Volume | Issue | Pages | |||
Keywords | Autonomous Driving; ADAS; CUDA | ||||
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. |
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Address | Barcelona; Spain | ||||
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ISSN | ISBN | Medium | |||
Area | Expedition | Conference | PUMPS | ||
Notes | ADAS; 600.076; 600.082; 600.085 | Approved | no | ||
Call Number | ADAS @ adas @ SCS2015 | Serial | 2645 | ||
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Author | Victor Campmany; Sergio Silva; Juan Carlos Moure; Antoni Espinosa; David Vazquez; Antonio Lopez | ||||
Title | GPU-based pedestrian detection for autonomous driving | Type | Abstract | ||
Year | 2015 | Publication | Programming and Tunning Massive Parallel Systems | Abbreviated Journal | PUMPS |
Volume | Issue | Pages | |||
Keywords | Autonomous Driving; ADAS; CUDA; Pedestrian Detection | ||||
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. | ||||
Address | Barcelona; Spain | ||||
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Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | PUMPS | ||
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ISSN | ISBN | Medium | |||
Area | Expedition | Conference | PUMPS | ||
Notes | ADAS; 600.076; 600.082; 600.085 | Approved | no | ||
Call Number | ADAS @ adas @ CSM2015 | Serial | 2644 | ||
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Author | Pau Riba; Josep Llados; Alicia Fornes | ||||
Title | Handwritten Word Spotting by Inexact Matching of Grapheme Graphs | Type | Conference Article | ||
Year | 2015 | Publication | 13th International Conference on Document Analysis and Recognition ICDAR2015 | Abbreviated Journal | |
Volume | Issue | Pages | 781 - 785 | ||
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Abstract | This paper presents a graph-based word spotting for handwritten documents. Contrary to most word spotting techniques, which use statistical representations, we propose a structural representation suitable to be robust to the inherent deformations of handwriting. Attributed graphs are constructed using a part-based approach. Graphemes extracted from shape convexities are used as stable units of handwriting, and are associated to graph nodes. Then, spatial relations between them determine graph edges. Spotting is defined in terms of an error-tolerant graph matching using bipartite-graph matching algorithm. To make the method usable in large datasets, a graph indexing approach that makes use of binary embeddings is used as preprocessing. Historical documents are used as experimental framework. The approach is comparable to statistical ones in terms of time and memory requirements, especially when dealing with large document collections. | ||||
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Area | Expedition | Conference | ICDAR | ||
Notes | DAG; 600.077; 600.061; 602.006 | Approved | no | ||
Call Number | Admin @ si @ RLF2015b | Serial | 2642 | ||
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Author | Juan Ignacio Toledo; Jordi Cucurull; Jordi Puiggali; Alicia Fornes; Josep Llados | ||||
Title | Document Analysis Techniques for Automatic Electoral Document Processing: A Survey | Type | Conference Article | ||
Year | 2015 | Publication | E-Voting and Identity, Proceedings of 5th international conference, VoteID 2015 | Abbreviated Journal | |
Volume | Issue | Pages | 139-141 | ||
Keywords | Document image analysis; Computer vision; Paper ballots; Paper based elections; Optical scan; Tally | ||||
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. | ||||
Address | Bern; Switzerland; September 2015 | ||||
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Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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ISSN | ISBN | Medium | |||
Area | Expedition | Conference | VoteID | ||
Notes | DAG; 600.061; 602.006; 600.077 | Approved | no | ||
Call Number | Admin @ si @ TCP2015 | Serial | 2641 | ||
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Author | Tadashi Araki; Nobutaka Ikeda; Nilanjan Dey; Sayan Chakraborty; Luca Saba; Dinesh Kumar; Elisa Cuadrado Godia; Xiaoyi Jiang; Ajay Gupta; Petia Radeva; John R. Laird; Andrew Nicolaides; Jasjit S. Suri | ||||
Title | A comparative approach of four different image registration techniques for quantitative assessment of coronary artery calcium lesions using intravascular ultrasound | Type | Journal Article | ||
Year | 2015 | Publication | Computer Methods and Programs in Biomedicine | Abbreviated Journal | CMPB |
Volume | 118 | Issue | 2 | Pages | 158-172 |
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Notes | MILAB | Approved | no | ||
Call Number | Admin @ si @ AID2015 | Serial | 2640 | ||
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Author | Nuria Cirera; Alicia Fornes; Josep Llados | ||||
Title | Hidden Markov model topology optimization for handwriting recognition | Type | Conference Article | ||
Year | 2015 | Publication | 13th International Conference on Document Analysis and Recognition ICDAR2015 | Abbreviated Journal | |
Volume | Issue | Pages | 626-630 | ||
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Abstract | In this paper we present a method to optimize the topology of linear left-to-right hidden Markov models. These models are very popular for sequential signals modeling on tasks such as handwriting recognition. Many topology definition methods select the number of states for a character model based
on character length. This can be a drawback when characters are shorter than the minimum allowed by the model, since they can not be properly trained nor recognized. The proposed method optimizes the number of states per model by automatically including convenient skip-state transitions and therefore it avoids the aforementioned problem.We discuss and compare our method with other character length-based methods such the Fixed, Bakis and Quantile methods. Our proposal performs well on off-line handwriting recognition task. |
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Address | Nancy; France; August 2015 | ||||
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Area | Expedition | Conference | ICDAR | ||
Notes | DAG; 600.061; 602.006; 600.077 | Approved | no | ||
Call Number | Admin @ si @ CFL2015 | Serial | 2639 | ||
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Author | Marc Bolaños; R. Mestre; Estefania Talavera; Xavier Giro; Petia Radeva | ||||
Title | Visual Summary of Egocentric Photostreams by Representative Keyframes | Type | Conference Article | ||
Year | 2015 | Publication | IEEE International Conference on Multimedia and Expo ICMEW2015 | Abbreviated Journal | |
Volume | Issue | Pages | 1-6 | ||
Keywords | egocentric; lifelogging; summarization; keyframes | ||||
Abstract | Building a visual summary from an egocentric photostream captured by a lifelogging wearable camera is of high interest for different applications (e.g. memory reinforcement). In this paper, we propose a new summarization method based on keyframes selection that uses visual features extracted bymeans of a convolutional neural network. Our method applies an unsupervised clustering for dividing the photostreams into events, and finally extracts the most relevant keyframe for each event. We assess the results by applying a blind-taste test on a group of 20 people who assessed the quality of the
summaries. |
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Address | Torino; italy; July 2015 | ||||
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Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | 978-1-4799-7079-7 | Edition | ||
ISSN | ISBN | 978-1-4799-7079-7 | Medium | ||
Area | Expedition | Conference | ICME | ||
Notes | MILAB | Approved | no | ||
Call Number | Admin @ si @ BMT2015 | Serial | 2638 | ||
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Author | Santiago Segui; Oriol Pujol; Jordi Vitria | ||||
Title | Learning to count with deep object features | Type | Conference Article | ||
Year | 2015 | Publication | Deep Vision: Deep Learning in Computer Vision, CVPR 2015 Workshop | Abbreviated Journal | |
Volume | Issue | Pages | 90-96 | ||
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Abstract | Learning to count is a learning strategy that has been recently proposed in the literature for dealing with problems where estimating the number of object instances in a scene is the final objective. In this framework, the task of learning to detect and localize individual object instances is seen as a harder task that can be evaded by casting the problem as that of computing a regression value from hand-crafted image features. In this paper we explore the features that are learned when training a counting convolutional neural
network in order to understand their underlying representation. To this end we define a counting problem for MNIST data and show that the internal representation of the network is able to classify digits in spite of the fact that no direct supervision was provided for them during training. We also present preliminary results about a deep network that is able to count the number of pedestrians in a scene. |
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Address | Boston; USA; June 2015 | ||||
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Area | Expedition | Conference | CVPRW | ||
Notes | MILAB; HuPBA; OR;MV | Approved | no | ||
Call Number | Admin @ si @ SPV2015 | Serial | 2636 | ||
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Author | Michal Drozdzal; Santiago Segui; Petia Radeva; Carolina Malagelada; Fernando Azpiroz; Jordi Vitria | ||||
Title | Motility bar: a new tool for motility analysis of endoluminal videos | Type | Journal Article | ||
Year | 2015 | Publication | Computers in Biology and Medicine | Abbreviated Journal | CBM |
Volume | 65 | Issue | Pages | 320-330 | |
Keywords | Small intestine; Motility; WCE; Computer vision; Image classification | ||||
Abstract | Wireless Capsule Endoscopy (WCE) provides a new perspective of the small intestine, since it enables, for the first time, visualization of the entire organ. However, the long visual video analysis time, due to the large number of data in a single WCE study, was an important factor impeding the widespread use of the capsule as a tool for intestinal abnormalities detection. Therefore, the introduction of WCE triggered a new field for the application of computational methods, and in particular, of computer vision. In this paper, we follow the computational approach and come up with a new perspective on the small intestine motility problem. Our approach consists of three steps: first, we review a tool for the visualization of the motility information contained in WCE video; second, we propose algorithms for the characterization of two motility building-blocks: contraction detector and lumen size estimation; finally, we introduce an approach to detect segments of stable motility behavior. Our claims are supported by an evaluation performed with 10 WCE videos, suggesting that our methods ably capture the intestinal motility information. | ||||
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Notes | MILAB;MV | Approved | no | ||
Call Number | Admin @ si @ DSR2015 | Serial | 2635 | ||
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