Home << 1 2 3 4 5 6 7 8 9 10 >>
List View
 | 
Citations
 | 
Details
   web
Records
Author Sounak Dey; Anguelos Nicolaou; Josep Llados; Umapada Pal
Title Local Binary Pattern for Word Spotting in Handwritten Historical Document Type Conference Article
Year 2016 Publication Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR) Abbreviated Journal
Volume Issue Pages 574-583
Keywords Local binary patterns; Spatial sampling; Learning-free; Word spotting; Handwritten; Historical document analysis; Large-scale data
Abstract Digital libraries store images which can be highly degraded and to index this kind of images we resort to word spotting as our information retrieval system. Information retrieval for handwritten document images is more challenging due to the difficulties in complex layout analysis, large variations of writing styles, and degradation or low quality of historical manuscripts. This paper presents a simple innovative learning-free method for word spotting from large scale historical documents combining Local Binary Pattern (LBP) and spatial sampling. This method offers three advantages: firstly, it operates in completely learning free paradigm which is very different from unsupervised learning methods, secondly, the computational time is significantly low because of the LBP features, which are very fast to compute, and thirdly, the method can be used in scenarios where annotations are not available. Finally, we compare the results of our proposed retrieval method with other methods in the literature and we obtain the best results in the learning free paradigm.
Address Merida; Mexico; December 2016
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference S+SSPR
Notes DAG; 600.097; 602.006; 603.053 Approved no
Call Number (up) Admin @ si @ DNL2016 Serial 2876
Permanent link to this record
 
 
Author Anjan Dutta; Umapada Pal; Josep Llados
Title Compact Correlated Features for Writer Independent Signature Verification Type Conference Article
Year 2016 Publication 23rd International Conference on Pattern Recognition Abbreviated Journal
Volume Issue Pages
Keywords
Abstract This paper considers the offline signature verification problem which is considered to be an important research line in the field of pattern recognition. In this work we propose hybrid features that consider the local features and their global statistics in the signature image. This has been done by creating a vocabulary of histogram of oriented gradients (HOGs). We impose weights on these local features based on the height information of water reservoirs obtained from the signature. Spatial information between local features are thought to play a vital role in considering the geometry of the signatures which distinguishes the originals from the forged ones. Nevertheless, learning a condensed set of higher order neighbouring features based on visual words, e.g., doublets and triplets, continues to be a challenging problem as possible combinations of visual words grow exponentially. To avoid this explosion of size, we create a code of local pairwise features which are represented as joint descriptors. Local features are paired based on the edges of a graph representation built upon the Delaunay triangulation. We reveal the advantage of combining both type of visual codebooks (order one and pairwise) for signature verification task. This is validated through an encouraging result on two benchmark datasets viz. CEDAR and GPDS300.
Address Cancun; Mexico; December 2016
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 ICPR
Notes DAG; 600.097 Approved no
Call Number (up) Admin @ si @ DPL2016 Serial 2875
Permanent link to this record
 
 
Author Thanh Ha Do; Salvatore Tabbone; Oriol Ramos Terrades
Title Sparse representation over learned dictionary for symbol recognition Type Journal Article
Year 2016 Publication Signal Processing Abbreviated Journal SP
Volume 125 Issue Pages 36-47
Keywords Symbol Recognition; Sparse Representation; Learned Dictionary; Shape Context; Interest Points
Abstract In this paper we propose an original sparse vector model for symbol retrieval task. More speci cally, we apply the K-SVD algorithm for learning a visual dictionary based on symbol descriptors locally computed around interest points. Results on benchmark datasets show that the obtained sparse representation is competitive related to state-of-the-art methods. Moreover, our sparse representation is invariant to rotation and scale transforms and also robust to degraded images and distorted symbols. Thereby, the learned visual dictionary is able to represent instances of unseen classes of symbols.
Address
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
Notes DAG; 600.061; 600.077 Approved no
Call Number (up) Admin @ si @ DTR2016 Serial 2946
Permanent link to this record
 
 
Author Sergio Escalera; Vassilis Athitsos; Isabelle Guyon
Title Challenges in multimodal gesture recognition Type Journal Article
Year 2016 Publication Journal of Machine Learning Research Abbreviated Journal JMLR
Volume 17 Issue Pages 1-54
Keywords Gesture Recognition; Time Series Analysis; Multimodal Data Analysis; Computer Vision; Pattern Recognition; Wearable sensors; Infrared Cameras; KinectTM
Abstract This paper surveys the state of the art on multimodal gesture recognition and introduces the JMLR special topic on gesture recognition 2011-2015. We began right at the start of the KinectTMrevolution when inexpensive infrared cameras providing image depth recordings became available. We published papers using this technology and other more conventional methods, including regular video cameras, to record data, thus providing a good overview of uses of machine learning and computer vision using multimodal data in this area of application. Notably, we organized a series of challenges and made available several datasets we recorded for that purpose, including tens of thousands
of videos, which are available to conduct further research. We also overview recent state of the art works on gesture recognition based on a proposed taxonomy for gesture recognition, discussing challenges and future lines of research.
Address
Corporate Author Thesis
Publisher Place of Publication Editor Zhuowen Tu
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes HuPBA;MILAB; Approved no
Call Number (up) Admin @ si @ EAG2016 Serial 2764
Permanent link to this record
 
 
Author Sergio Escalera; Jordi Gonzalez; Xavier Baro; Fernando Alonso; Martha Mackay
Title Care Respite: a remote monitoring eHealth system for improving ambient assisted living Type Conference Article
Year 2016 Publication Human Motion Analysis for Healthcare Applications Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Advances in technology that capture human motion have been quite remarkable during the last five years. New sensors have been developed, such as the Microsoft Kinect, Asus Xtion Pro live, PrimeSense Carmine and Leap Motion. Their main advantages are their non-intrusive nature, low cost and widely available support for developers offered by large corporations or Open Communities. Although they were originally developed for computer games, they have inspired numerous healthcare related ideas and projects in areas such as Medical Disorder Diagnosis, Assisted Living, Rehabilitation and Surgery.

In Assisted Living, human motion analysis allows continuous monitoring of elderly and vulnerable people and their activities to potentially detect life-threatening events such as falls. Human motion analysis in rehabilitation provides the opportunity for motivating patients through gamification, evaluating prescribed programmes of exercises and assessing patients’ progress. In operating theatres, surgeons may use a gesture-based interface to access medical information or control a tele-surgery system. Human motion analysis may also be used to diagnose a range of mental and physical diseases and conditions.

This event will discuss recent advances in human motion sensing and provide an application to healthcare for networking and exploring potential synergies and collaborations.
Address Savoy Place; London; uk; May 2016
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 HMAHA
Notes HuPBA; ISE; Approved no
Call Number (up) Admin @ si @ EGB2016 Serial 2852
Permanent link to this record
 
 
Author Hugo Jair Escalante; Victor Ponce; Jun Wan; Michael A. Riegler; Baiyu Chen; Albert Clapes; Sergio Escalera; Isabelle Guyon; Xavier Baro; Pal Halvorsen; Henning Muller; Martha Larson
Title ChaLearn Joint Contest on Multimedia Challenges Beyond Visual Analysis: An Overview Type Conference Article
Year 2016 Publication 23rd International Conference on Pattern Recognition Abbreviated Journal
Volume Issue Pages
Keywords
Abstract This paper provides an overview of the Joint Contest on Multimedia Challenges Beyond Visual Analysis. We organized an academic competition that focused on four problems that require effective processing of multimodal information in order to be solved. Two tracks were devoted to gesture spotting and recognition from RGB-D video, two fundamental problems for human computer interaction. Another track was devoted to a second round of the first impressions challenge of which the goal was to develop methods to recognize personality traits from
short video clips. For this second round we adopted a novel collaborative-competitive (i.e., coopetition) setting. The fourth track was dedicated to the problem of video recommendation for improving user experience. The challenge was open for about 45 days, and received outstanding participation: almost
200 participants registered to the contest, and 20 teams sent predictions in the final stage. The main goals of the challenge were fulfilled: the state of the art was advanced considerably in the four tracks, with novel solutions to the proposed problems (mostly relying on deep learning). However, further research is still required. The data of the four tracks will be available to
allow researchers to keep making progress in the four tracks.
Address Cancun; Mexico; December 2016
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 ICPR
Notes HuPBA; 602.143;MV Approved no
Call Number (up) Admin @ si @ EPW2016 Serial 2827
Permanent link to this record
 
 
Author Onur Ferhat; Fernando Vilariño
Title Low Cost Eye Tracking: The Current Panorama Type Journal Article
Year 2016 Publication Computational Intelligence and Neuroscience Abbreviated Journal CIN
Volume Issue Pages Article ID 8680541
Keywords
Abstract Despite the availability of accurate, commercial gaze tracker devices working with infrared (IR) technology, visible light gaze tracking constitutes an interesting alternative by allowing scalability and removing hardware requirements. Over the last years, this field has seen examples of research showing performance comparable to the IR alternatives. In this work, we survey the previous work on remote, visible light gaze trackers and analyze the explored techniques from various perspectives such as calibration strategies, head pose invariance, and gaze estimation techniques. We also provide information on related aspects of research such as public datasets to test against, open source projects to build upon, and gaze tracking services to directly use in applications. With all this information, we aim to provide the contemporary and future researchers with a map detailing previously explored ideas and the required tools.
Address
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
Notes MV; 605.103; 600.047; 600.097;SIAI Approved no
Call Number (up) Admin @ si @ FeV2016 Serial 2744
Permanent link to this record
 
 
Author Alicia Fornes; Josep Llados; Oriol Ramos Terrades; Marçal Rusiñol
Title La Visió per Computador com a Eina per a la Interpretació Automàtica de Fonts Documentals Type Journal
Year 2016 Publication Lligall, Revista Catalana d'Arxivística Abbreviated Journal
Volume 39 Issue Pages 20-46
Keywords
Abstract
Address
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
Notes DAG; 600.097 Approved no
Call Number (up) Admin @ si @ FLR2016 Serial 2897
Permanent link to this record
 
 
Author Isabelle Guyon; Imad Chaabane; Hugo Jair Escalante; Sergio Escalera; Damir Jajetic; James Robert Lloyd; Nuria Macia; Bisakha Ray; Lukasz Romaszko; Michele Sebag; Alexander Statnikov; Sebastien Treguer; Evelyne Viegas
Title A brief Review of the ChaLearn AutoML Challenge: Any-time Any-dataset Learning without Human Intervention Type Conference Article
Year 2016 Publication AutoML Workshop Abbreviated Journal
Volume Issue 1 Pages 1-8
Keywords AutoML Challenge; machine learning; model selection; meta-learning; repre- sentation learning; active learning
Abstract The ChaLearn AutoML Challenge team conducted a large scale evaluation of fully automatic, black-box learning machines for feature-based classification and regression problems. The test bed was composed of 30 data sets from a wide variety of application domains and ranged across different types of complexity. Over six rounds, participants succeeded in delivering AutoML software capable of being trained and tested without human intervention. Although improvements can still be made to close the gap between human-tweaked and AutoML models, this competition contributes to the development of fully automated environments by challenging practitioners to solve problems under specific constraints and sharing their approaches; the platform will remain available for post-challenge submissions at http://codalab.org/AutoML.
Address New York; USA; June 2016
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 ICML
Notes HuPBA;MILAB Approved no
Call Number (up) Admin @ si @ GCE2016 Serial 2769
Permanent link to this record
 
 
Author Antoni Gurgui; Debora Gil; Enric Marti; Vicente Grau
Title Left-Ventricle Basal Region Constrained Parametric Mapping to Unitary Domain Type Conference Article
Year 2016 Publication 7th International Workshop on Statistical Atlases & Computational Modelling of the Heart Abbreviated Journal
Volume 10124 Issue Pages 163-171
Keywords Laplacian; Constrained maps; Parameterization; Basal ring
Abstract Due to its complex geometry, the basal ring is often omitted when putting different heart geometries into correspondence. In this paper, we present the first results on a new mapping of the left ventricle basal rings onto a normalized coordinate system using a fold-over free approach to the solution to the Laplacian. To guarantee correspondences between different basal rings, we imposed some internal constrained positions at anatomical landmarks in the normalized coordinate system. To prevent internal fold-overs, constraints are handled by cutting the volume into regions defined by anatomical features and mapping each piece of the volume separately. Initial results presented in this paper indicate that our method is able to handle internal constrains without introducing fold-overs and thus guarantees one-to-one mappings between different basal ring geometries.
Address Athens; October 2016
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference STACOM
Notes IAM; Approved no
Call Number (up) Admin @ si @ GGM2016 Serial 2884
Permanent link to this record
 
 
Author Jose A. Garcia; David Masip; Valerio Sbragaglia; Jacopo Aguzzi
Title Automated Identification and Tracking of Nephrops norvegicus (L.) Using Infrared and Monochromatic Blue Light Type Conference Article
Year 2016 Publication 19th International Conference of the Catalan Association for Artificial Intelligence Abbreviated Journal
Volume Issue Pages
Keywords computer vision; video analysis; object recognition; tracking; behaviour; social; decapod; Nephrops norvegicus
Abstract Automated video and image analysis can be a very efficient tool to analyze
animal behavior based on sociality, especially in hard access environments
for researchers. The understanding of this social behavior can play a key role in the sustainable design of capture policies of many species. This paper proposes the use of computer vision algorithms to identify and track a specific specie, the Norway lobster, Nephrops norvegicus, a burrowing decapod with relevant commercial value which is captured by trawling. These animals can only be captured when are engaged in seabed excursions, which are strongly related with their social behavior.
This emergent behavior is modulated by the day-night cycle, but their social
interactions remain unknown to the scientific community. The paper introduces an identification scheme made of four distinguishable black and white tags (geometric shapes). The project has recorded 15-day experiments in laboratory pools, under monochromatic blue light (472 nm.) and darkness conditions (recorded using Infra Red light). Using this massive image set, we propose a comparative of state-ofthe-art computer vision algorithms to distinguish and track the different animals’ movements. We evaluate the robustness to the high noise presence in the infrared video signals and free out-of-plane rotations due to animal movement. The experiments show promising accuracies under a cross-validation protocol, being adaptable to the automation and analysis of large scale data. In a second contribution, we created an extensive dataset of shapes (46027 different shapes) from four daily experimental video recordings, which will be available to the community.
Address Barcelona; Spain; October 2016
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 CCIA
Notes OR;MV; Approved no
Call Number (up) Admin @ si @ GMS2016 Serial 2816
Permanent link to this record
 
 
Author Jose A. Garcia; David Masip; Valerio Sbragaglia; Jacopo Aguzzi
Title Using ORB, BoW and SVM to identificate and track tagged Norway lobster Nephrops Norvegicus (L.) Type Conference Article
Year 2016 Publication 3rd International Conference on Maritime Technology and Engineering Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Sustainable capture policies of many species strongly depend on the understanding of their social behaviour. Nevertheless, the analysis of emergent behaviour in marine species poses several challenges. Usually animals are captured and observed in tanks, and their behaviour is inferred from their dynamics and interactions. Therefore, researchers must deal with thousands of hours of video data. Without loss of generality, this paper proposes a computer
vision approach to identify and track specific species, the Norway lobster, Nephrops norvegicus. We propose an identification scheme were animals are marked using black and white tags with a geometric shape in the center (holed
triangle, filled triangle, holed circle and filled circle). Using a massive labelled dataset; we extract local features based on the ORB descriptor. These features are a posteriori clustered, and we construct a Bag of Visual Words feature vector per animal. This approximation yields us invariance to rotation
and translation. A SVM classifier achieves generalization results above 99%. In a second contribution, we will make the code and training data publically available.
Address Lisboa; Portugal; July 2016
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 MARTECH
Notes OR;MV; Approved no
Call Number (up) Admin @ si @ GMS2016b Serial 2817
Permanent link to this record
 
 
Author Lluis Gomez; Dimosthenis Karatzas
Title A fast hierarchical method for multi‐script and arbitrary oriented scene text extraction Type Journal Article
Year 2016 Publication International Journal on Document Analysis and Recognition Abbreviated Journal IJDAR
Volume 19 Issue 4 Pages 335-349
Keywords scene text; segmentation; detection; hierarchical grouping; perceptual organisation
Abstract Typography and layout lead to the hierarchical organisation of text in words, text lines, paragraphs. This inherent structure is a key property of text in any script and language, which has nonetheless been minimally leveraged by existing text detection methods. This paper addresses the problem of text
segmentation in natural scenes from a hierarchical perspective.
Contrary to existing methods, we make explicit use of text structure, aiming directly to the detection of region groupings corresponding to text within a hierarchy produced by an agglomerative similarity clustering process over individual regions. We propose an optimal way to construct such an hierarchy introducing a feature space designed to produce text group hypotheses with
high recall and a novel stopping rule combining a discriminative classifier and a probabilistic measure of group meaningfulness based in perceptual organization. Results obtained over four standard datasets, covering text in variable orientations and different languages, demonstrate that our algorithm, while being trained in a single mixed dataset, outperforms state of the art
methods in unconstrained scenarios.
Address
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
Notes DAG; 600.056; 601.197 Approved no
Call Number (up) Admin @ si @ GoK2016a Serial 2862
Permanent link to this record
 
 
Author Lluis Gomez; Dimosthenis Karatzas
Title A fine-grained approach to scene text script identification Type Conference Article
Year 2016 Publication 12th IAPR Workshop on Document Analysis Systems Abbreviated Journal
Volume Issue Pages 192-197
Keywords
Abstract This paper focuses on the problem of script identification in unconstrained scenarios. Script identification is an important prerequisite to recognition, and an indispensable condition for automatic text understanding systems designed for multi-language environments. Although widely studied for document images and handwritten documents, it remains an almost unexplored territory for scene text images. We detail a novel method for script identification in natural images that combines convolutional features and the Naive-Bayes Nearest Neighbor classifier. The proposed framework efficiently exploits the discriminative power of small stroke-parts, in a fine-grained classification framework. In addition, we propose a new public benchmark dataset for the evaluation of joint text detection and script identification in natural scenes. Experiments done in this new dataset demonstrate that the proposed method yields state of the art results, while it generalizes well to different datasets and variable number of scripts. The evidence provided shows that multi-lingual scene text recognition in the wild is a viable proposition. Source code of the proposed method is made available online.
Address Santorini; Grecia; April 2016
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 DAS
Notes DAG; 601.197; 600.084 Approved no
Call Number (up) Admin @ si @ GoK2016b Serial 2863
Permanent link to this record
 
 
Author Lluis Gomez
Title Exploiting Similarity Hierarchies for Multi-script Scene Text Understanding Type Book Whole
Year 2016 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal
Volume Issue Pages
Keywords
Abstract This thesis addresses the problem of automatic scene text understanding in unconstrained conditions. In particular, we tackle the tasks of multi-language and arbitrary-oriented text detection, tracking, and script identification in natural scenes.
For this we have developed a set of generic methods that build on top of the basic observation that text has always certain key visual and structural characteristics that are independent of the language or script in which it is written. Text instances in any
language or script are always formed as groups of similar atomic parts, being them either individual characters, small stroke parts, or even whole words in the case of cursive text. This holistic (sumof-parts) and recursive perspective has lead us to explore different variants of the “segmentation and grouping” paradigm of computer vision.
Scene text detection methodologies are usually based in classification of individual regions or patches, using a priory knowledge for a given script or language. Human perception of text, on the other hand, is based on perceptual organization through which
text emerges as a perceptually significant group of atomic objects.
In this thesis, we argue that the text detection problem must be posed as the detection of meaningful groups of regions. We address the problem of text detection in natural scenes from a hierarchical perspective, making explicit use of the recursive nature of text, aiming directly to the detection of region groupings corresponding to text within a hierarchy produced by an agglomerative similarity clustering process over individual regions. We propose an optimal way to construct such an hierarchy introducing a feature space designed to produce text group hypothese with high recall and a novel stopping rule combining a discriminative classifier and a probabilistic measure of group meaningfulness based in perceptual organization. Within this generic framework, we design a text-specific object proposals algorithm that, contrary to existing generic object proposals methods, aims directly to the detection of text regions groupings. For this, we abandon the rigid definition of “what is text” of traditional specialized text detectors, and move towards more fuzzy perspective of grouping-based object proposals methods.
Then, we present a hybrid algorithm for detection and tracking of scene text where the notion of region groupings plays also a central role. By leveraging the structural arrangement of text group components between consecutive frames we can improve
the overall tracking performance of the system.
Finally, since our generic detection framework is inherently designed for multi-language environments, we focus on the problem of script identification in order to build a multi-language end-toend reading system. Facing this problem with state of the art CNN classifiers is not straightforward, as they fail to address a key
characteristic of scene text instances: their extremely variable aspect ratio. Instead of resizing input images to a fixed size as in the typical use of holistic CNN classifiers, we propose a patch-based classification framework in order to preserve discriminative parts of the image that are characteristic of its class. We describe a novel method based on the use of ensembles of conjoined networks to jointly learn discriminative stroke-parts representations and their relative importance in a patch-based classification scheme.
Address
Corporate Author Thesis Ph.D. thesis
Publisher Place of Publication Editor Dimosthenis Karatzas
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes DAG Approved no
Call Number (up) Admin @ si @ Gom2016 Serial 2891
Permanent link to this record