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Author Xavier Otazu; Olivier Penacchio; Xim Cerda-Company
Title Brightness and colour induction through contextual influences in V1 Type Conference Article
Year 2015 Publication Scottish Vision Group 2015 SGV2015 Abbreviated Journal
Volume 12 Issue 9 Pages 1208-2012
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Abstract
Address Carnoustie; Scotland; March 2015
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Area Expedition Conference SGV
Notes NEUROBIT; Approved no
Call Number Admin @ si @ OPC2015a Serial 2632
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Author Olivier Penacchio; Xavier Otazu; A. wilkins; J. Harris
Title Uncomfortable images prevent lateral interactions in the cortex from providing a sparse code Type Conference Article
Year 2015 Publication European Conference on Visual Perception ECVP2015 Abbreviated Journal
Volume Issue Pages
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Abstract
Address Liverpool; uk; August 2015
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Area Expedition Conference ECVP
Notes NEUROBIT; Approved no
Call Number Admin @ si @ POW2015 Serial 2633
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Author Xavier Otazu; Olivier Penacchio; Xim Cerda-Company
Title An excitatory-inhibitory firing rate model accounts for brightness induction, colour induction and visual discomfort Type Conference Article
Year 2015 Publication Barcelona Computational, Cognitive and Systems Neuroscience Abbreviated Journal
Volume Issue Pages
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Abstract
Address Barcelona; June 2015
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Area Expedition Conference BARCCSYN
Notes NEUROBIT; Approved no
Call Number Admin @ si @ OPC2015b Serial 2634
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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
Keywords (up)
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.
Address Boston; USA; June 2015
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Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
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ISSN ISBN Medium
Area Expedition Conference CVPRW
Notes MILAB; HuPBA; OR;MV Approved no
Call Number Admin @ si @ SPV2015 Serial 2636
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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
Keywords (up)
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.
Address Nancy; France; August 2015
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Series Editor Series Title Abbreviated Series Title
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ISSN ISBN Medium
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 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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Address
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Notes MILAB Approved no
Call Number Admin @ si @ AID2015 Serial 2640
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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
Keywords (up)
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.
Address
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Series Editor Series Title Abbreviated Series Title
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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 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
Keywords (up)
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.
Address
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Notes HuPBA;MILAB Approved no
Call Number Admin @ si @ TAE2015 Serial 2647
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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
Keywords (up)
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 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
Volume Issue Pages
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Abstract
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 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
Volume Issue Pages
Keywords (up)
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.
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 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 Andres Traumann; Sergio Escalera; Gholamreza Anbarjafari
Title A New Retexturing Method for Virtual Fitting Room Using Kinect 2 Camera Type Conference Article
Year 2015 Publication 2015 IEEE Conference on Computer Vision and Pattern Recognition Worshops (CVPRW) Abbreviated Journal
Volume Issue Pages 75-79
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Abstract
Address Boston; EEUU; June 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 Edition
ISSN ISBN Medium
Area Expedition Conference CVPRW
Notes HuPBA;MILAB Approved no
Call Number Admin @ si @ TEA2015 Serial 2653
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Author 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
Title Spatio-temporal Analysis of RGB-D-T Facial Images for Multimodal Pain Level Recognition Type Conference Article
Year 2015 Publication 2015 IEEE Conference on Computer Vision and Pattern Recognition Worshops (CVPRW) Abbreviated Journal
Volume Issue Pages 88-95
Keywords (up)
Abstract 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.
Address Boston; EEUU; June 2015
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Publisher Place of Publication Editor
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Series Editor Series Title Abbreviated Series Title
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Area Expedition Conference CVPRW
Notes HuPBA;MILAB Approved no
Call Number Admin @ si @ INB2015 Serial 2654
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Author Mohammad Ali Bagheri; Qigang Gao; Sergio Escalera; Albert Clapes; Kamal Nasrollahi; Michael Holte; Thomas B. Moeslund
Title Keep it Accurate and Diverse: Enhancing Action Recognition Performance by Ensemble Learning Type Conference Article
Year 2015 Publication IEEE Conference on Computer Vision and Pattern Recognition Worshops (CVPRW) Abbreviated Journal
Volume Issue Pages 22-29
Keywords (up)
Abstract 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.
Address Boston; EEUU; June 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 Edition
ISSN ISBN Medium
Area Expedition Conference CVPRW
Notes HuPBA;MILAB Approved no
Call Number Admin @ si @ BGE2015 Serial 2655
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