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Author Marta Nuñez-Garcia; Sonja Simpraga; M.Angeles Jurado; Maite Garolera; Roser Pueyo; Laura Igual
Title FADR: Functional-Anatomical Discriminative Regions for rest fMRI Characterization Type Conference Article
Year 2015 Publication Machine Learning in Medical Imaging, Proceedings of 6th International Workshop, MLMI 2015, Held in Conjunction with MICCAI 2015 Abbreviated Journal
Volume Issue Pages 61-68
Keywords
Abstract
Address Munich; Germany; October 2015
Corporate Author Thesis
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 (down) MLMI
Notes MILAB Approved no
Call Number Admin @ si @ NSJ2015 Serial 2674
Permanent link to this record
 
 
Author Aniol Lidon; Xavier Giro; Marc Bolaños; Petia Radeva; Markus Seidl; Matthias Zeppelzauer
Title UPC-UB-STP @ MediaEval 2015 diversity task: iterative reranking of relevant images Type Conference Article
Year 2015 Publication 2015 MediaEval Retrieving Diverse Images Task Abbreviated Journal
Volume Issue Pages
Keywords
Abstract This paper presents the results of the UPC-UB-STP team in the 2015 MediaEval Retrieving Diverse Images Task. The goal of the challenge is to provide a ranked list of Flickr photos for a predefined set of queries. Our approach firstly generates a ranking of images based on a query-independent estimation of its relevance. Only top results are kept and iteratively re-ranked based on their intra-similarity to introduce diversity.
Address Wurzen; Germany; September 2015
Corporate Author Thesis
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 (down) MediaEval
Notes MILAB Approved no
Call Number Admin @ si @LGB2016 Serial 2793
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Author Firat Ismailoglu; Ida G. Sprinkhuizen-Kuyper; Evgueni Smirnov; Sergio Escalera; Ralf Peeters
Title Fractional Programming Weighted Decoding for Error-Correcting Output Codes Type Conference Article
Year 2015 Publication Multiple Classifier Systems, Proceedings of 12th International Workshop , MCS 2015 Abbreviated Journal
Volume Issue Pages 38-50
Keywords
Abstract In order to increase the classification performance obtained using Error-Correcting Output Codes designs (ECOC), introducing weights in the decoding phase of the ECOC has attracted a lot of interest. In this work, we present a method for ECOC designs that focuses on increasing hypothesis margin on the data samples given a base classifier. While achieving this, we implicitly reward the base classifiers with high performance, whereas punish those with low performance. The resulting objective function is of the fractional programming type and we deal with this problem through the Dinkelbach’s Algorithm. The conducted tests over well known UCI datasets show that the presented method is superior to the unweighted decoding and that it outperforms the results of the state-of-the-art weighted decoding methods in most of the performed experiments.
Address Gunzburg; Germany; 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
Series Volume Series Issue Edition
ISSN ISBN 978-3-319-20247-1 Medium
Area Expedition Conference (down) MCS
Notes HuPBA;MILAB Approved no
Call Number Admin @ si @ ISS2015 Serial 2601
Permanent link to this record
 
 
Author Fernando Vilariño
Title Computer Vision and Performing Arts Type Conference Article
Year 2015 Publication Korean Scholars of Marketing Science Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
Address Seoul; Korea; October 2015
Corporate Author Thesis
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 (down) KAMS
Notes MV;SIAI Approved no
Call Number Admin @ si @Vil2015 Serial 2799
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Author Alejandro Gonzalez Alzate; Gabriel Villalonga; Jiaolong Xu; David Vazquez; Jaume Amores; Antonio Lopez
Title Multiview Random Forest of Local Experts Combining RGB and LIDAR data for Pedestrian Detection Type Conference Article
Year 2015 Publication IEEE Intelligent Vehicles Symposium IV2015 Abbreviated Journal
Volume Issue Pages 356-361
Keywords Pedestrian Detection
Abstract Despite recent significant advances, pedestrian detection continues to be an extremely challenging problem in real scenarios. In order to develop a detector that successfully operates under these conditions, it becomes critical to leverage upon multiple cues, multiple imaging modalities and a strong multi-view classifier that accounts for different pedestrian views and poses. In this paper we provide an extensive evaluation that gives insight into how each of these aspects (multi-cue, multimodality and strong multi-view classifier) affect performance both individually and when integrated together. In the multimodality component we explore the fusion of RGB and depth maps obtained by high-definition LIDAR, a type of modality that is only recently starting to receive attention. As our analysis reveals, although all the aforementioned aspects significantly help in improving the performance, the fusion of visible spectrum and depth information allows to boost the accuracy by a much larger margin. The resulting detector not only ranks among the top best performers in the challenging KITTI benchmark, but it is built upon very simple blocks that are easy to implement and computationally efficient. These simple blocks can be easily replaced with more sophisticated ones recently proposed, such as the use of convolutional neural networks for feature representation, to further improve the accuracy.
Address Seoul; Corea; June 2015
Corporate Author Thesis
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 ACDC Expedition Conference (down) IV
Notes ADAS; 600.076; 600.057; 600.054 Approved no
Call Number ADAS @ adas @ GVX2015 Serial 2625
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Author Aleksandr Setkov; Fabio Martinez Carillo; Michele Gouiffes; Christian Jacquemin; Maria Vanrell; Ramon Baldrich
Title DAcImPro: A Novel Database of Acquired Image Projections and Its Application to Object Recognition Type Conference Article
Year 2015 Publication Advances in Visual Computing. Proceedings of 11th International Symposium, ISVC 2015 Part II Abbreviated Journal
Volume 9475 Issue Pages 463-473
Keywords Projector-camera systems; Feature descriptors; Object recognition
Abstract Projector-camera systems are designed to improve the projection quality by comparing original images with their captured projections, which is usually complicated due to high photometric and geometric variations. Many research works address this problem using their own test data which makes it extremely difficult to compare different proposals. This paper has two main contributions. Firstly, we introduce a new database of acquired image projections (DAcImPro) that, covering photometric and geometric conditions and providing data for ground-truth computation, can serve to evaluate different algorithms in projector-camera systems. Secondly, a new object recognition scenario from acquired projections is presented, which could be of a great interest in such domains, as home video projections and public presentations. We show that the task is more challenging than the classical recognition problem and thus requires additional pre-processing, such as color compensation or projection area selection.
Address
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 978-3-319-27862-9 Medium
Area Expedition Conference (down) ISVC
Notes CIC Approved no
Call Number Admin @ si @ SMG2015 Serial 2736
Permanent link to this record
 
 
Author Marçal Rusiñol; Dimosthenis Karatzas; Josep Llados
Title Automatic Verification of Properly Signed Multi-page Document Images Type Conference Article
Year 2015 Publication Proceedings of the Eleventh International Symposium on Visual Computing Abbreviated Journal
Volume 9475 Issue Pages 327-336
Keywords Document Image; Manual Inspection; Signature Verification; Rejection Criterion; Document Flow
Abstract In this paper we present an industrial application for the automatic screening of incoming multi-page documents in a banking workflow aimed at determining whether these documents are properly signed or not. The proposed method is divided in three main steps. First individual pages are classified in order to identify the pages that should contain a signature. In a second step, we segment within those key pages the location where the signatures should appear. The last step checks whether the signatures are present or not. Our method is tested in a real large-scale environment and we report the results when checking two different types of real multi-page contracts, having in total more than 14,500 pages.
Address Las Vegas, Nevada, USA; December 2015
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume 9475 Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference (down) ISVC
Notes DAG; 600.077 Approved no
Call Number Admin @ si @ Serial 3189
Permanent link to this record
 
 
Author Fernando Vilariño; Dan Norton; Onur Ferhat
Title Memory Fields: DJs in the Library Type Conference Article
Year 2015 Publication 21 st Symposium of Electronic Arts Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
Address Vancouver; Canada; August 2015
Corporate Author Thesis
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 (down) ISEA
Notes ;SIAI Approved no
Call Number Admin @ si @VNF2015 Serial 2800
Permanent link to this record
 
 
Author Miguel Oliveira; L. Seabra Lopes; G. Hyun Lim; S. Hamidreza Kasaei; Angel Sappa; A. Tom
Title Concurrent Learning of Visual Codebooks and Object Categories in Openended Domains Type Conference Article
Year 2015 Publication International Conference on Intelligent Robots and Systems Abbreviated Journal
Volume Issue Pages 2488 - 2495
Keywords Visual Learning; Computer Vision; Autonomous Agents
Abstract In open-ended domains, robots must continuously learn new object categories. When the training sets are created offline, it is not possible to ensure their representativeness with respect to the object categories and features the system will find when operating online. In the Bag of Words model, visual codebooks are constructed from training sets created offline. This might lead to non-discriminative visual words and, as a consequence, to poor recognition performance. This paper proposes a visual object recognition system which concurrently learns in an incremental and online fashion both the visual object category representations as well as the codebook words used to encode them. The codebook is defined using Gaussian Mixture Models which are updated using new object views. The approach contains similarities with the human visual object recognition system: evidence suggests that the development of recognition capabilities occurs on multiple levels and is sustained over large periods of time. Results show that the proposed system with concurrent learning of object categories and codebooks is capable of learning more categories, requiring less examples, and with similar accuracies, when compared to the classical Bag of Words approach using offline constructed codebooks.
Address Hamburg; Germany; October 2015
Corporate Author Thesis
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 (down) IROS
Notes ADAS; 600.076 Approved no
Call Number Admin @ si @ OSL2015 Serial 2664
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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
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
Corporate Author Thesis
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 (down) IPTA
Notes HuPBA;MV Approved no
Call Number Admin @ si @ NER2015 Serial 2648
Permanent link to this record
 
 
Author Carles Sanchez; Jorge Bernal; F. Javier Sanchez; Marta Diez-Ferrer; Antoni Rosell; Debora Gil
Title Towards On-line Quantification of Tracheal Stenosis from Videobronchoscopy Type Conference Article
Year 2015 Publication 6th International Conference on Information Processing in Computer-Assisted Interventions IPCAI2015 Abbreviated Journal
Volume 10 Issue 6 Pages 935-945
Keywords
Abstract 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.
Address Barcelona; Spain; June 2015
Corporate Author Thesis
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 (down) IPCAI
Notes IAM; MV; 600.075 Approved no
Call Number Admin @ si @ SBS2015b Serial 2613
Permanent link to this record
 
 
Author Dan Norton; Fernando Vilariño; Onur Ferhat
Title Memory Field – Creative Engagement in Digital Collections Type Conference Article
Year 2015 Publication Internet Librarian International Conference Abbreviated Journal
Volume Issue Pages
Keywords
Abstract “Memory Fields” is a trans-disciplinary project aiming at the (re)valorisation of digital collections.Its main deliverable is an interface for a dual screen installation, used to access and mix the public library digital collections. The collections being used in this case are a collection of digitised posters from the Spanish Civil War, belonging to the Arxiu General de Catalunya, and a collection of field recordings made by Dan Norton. The system generates visualisations, and the images and sounds are mixed together using narrative primitives of video dj. Users contribute to the digital collections by adding personal memories and observations. The comments and recollections appear as flowers growing in a “memory field” and memories remain public in a Twitter feed (@Memoryfields).
Address London; UK; October 2015
Corporate Author Thesis
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 (down) ILI
Notes MV;SIAI Approved no
Call Number Admin @ si @NVF2015 Serial 2796
Permanent link to this record
 
 
Author Sergio Escalera; Jordi Gonzalez; Xavier Baro; Pablo Pardo; Junior Fabian; Marc Oliu; Hugo Jair Escalante; Ivan Huerta; Isabelle Guyon
Title ChaLearn Looking at People 2015 new competitions: Age Estimation and Cultural Event Recognition Type Conference Article
Year 2015 Publication IEEE International Joint Conference on Neural Networks IJCNN2015 Abbreviated Journal
Volume Issue Pages 1-8
Keywords
Abstract Following previous series on Looking at People (LAP) challenges [1], [2], [3], in 2015 ChaLearn runs two new competitions within the field of Looking at People: age and cultural event recognition in still images. We propose thefirst crowdsourcing application to collect and label data about apparent
age of people instead of the real age. In terms of cultural event recognition, tens of categories have to be recognized. This involves scene understanding and human analysis. This paper summarizes both challenges and data, providing some initial baselines. The results of the first round of the competition were presented at ChaLearn LAP 2015 IJCNN special session on computer vision and robotics http://www.dtic.ua.es/∼jgarcia/IJCNN2015.
Details of the ChaLearn LAP competitions can be found at http://gesture.chalearn.org/.
Address Killarney; Ireland; July 2015
Corporate Author Thesis
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 (down) IJCNN
Notes HuPBA; ISE; 600.063; 600.078;MV Approved no
Call Number Admin @ si @ EGB2015 Serial 2591
Permanent link to this record
 
 
Author Hugo Jair Escalante; Jose Martinez; Sergio Escalera; Victor Ponce; Xavier Baro
Title Improving Bag of Visual Words Representations with Genetic Programming Type Conference Article
Year 2015 Publication IEEE International Joint Conference on Neural Networks IJCNN2015 Abbreviated Journal
Volume Issue Pages
Keywords
Abstract The bag of visual words is a well established representation in diverse computer vision problems. Taking inspiration from the fields of text mining and retrieval, this representation has proved to be very effective in a large number of domains.
In most cases, a standard term-frequency weighting scheme is considered for representing images and videos in computer vision. This is somewhat surprising, as there are many alternative ways of generating bag of words representations within the text processing community. This paper explores the use of alternative weighting schemes for landmark tasks in computer vision: image
categorization and gesture recognition. We study the suitability of using well-known supervised and unsupervised weighting schemes for such tasks. More importantly, we devise a genetic program that learns new ways of representing images and videos under the bag of visual words representation. The proposed method learns to combine term-weighting primitives trying to maximize the classification performance. Experimental results are reported in standard image and video data sets showing the effectiveness of the proposed evolutionary algorithm.
Address Killarney; Ireland; July 2015
Corporate Author Thesis
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 (down) IJCNN
Notes HuPBA;MV Approved no
Call Number Admin @ si @ EME2015 Serial 2603
Permanent link to this record
 
 
Author Isabelle Guyon; Kristin Bennett; Gavin Cawley; Hugo Jair Escalante; Sergio Escalera; Tin Kam Ho; Nuria Macia; Bisakha Ray; Alexander Statnikov; Evelyne Viegas
Title Design of the 2015 ChaLearn AutoML Challenge Type Conference Article
Year 2015 Publication IEEE International Joint Conference on Neural Networks IJCNN2015 Abbreviated Journal
Volume Issue Pages
Keywords
Abstract ChaLearn is organizing for IJCNN 2015 an Automatic Machine Learning challenge (AutoML) to solve classification and regression problems from given feature representations, without any human intervention. This is a challenge with code
submission: the code submitted can be executed automatically on the challenge servers to train and test learning machines on new datasets. However, there is no obligation to submit code. Half of the prizes can be won by just submitting prediction results.
There are six rounds (Prep, Novice, Intermediate, Advanced, Expert, and Master) in which datasets of progressive difficulty are introduced (5 per round). There is no requirement to participate in previous rounds to enter a new round. The rounds alternate AutoML phases in which submitted code is “blind tested” on
datasets the participants have never seen before, and Tweakathon phases giving time (' 1 month) to the participants to improve their methods by tweaking their code on those datasets. This challenge will push the state-of-the-art in fully automatic machine learning on a wide range of problems taken from real world
applications. The platform will remain available beyond the termination of the challenge: http://codalab.org/AutoML
Address Killarney; Ireland; July 2015
Corporate Author Thesis
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 (down) IJCNN
Notes HuPBA;MILAB Approved no
Call Number Admin @ si @ GBC2015a Serial 2604
Permanent link to this record