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Author Jose Antonio Rodriguez; Gemma Sanchez; Josep Llados
Title A Pen-based Interface for Real-time Document Edition Type Conference Article
Year 2007 Publication (up) 9th International Conference on Document Analysis and Recognition. Abbreviated Journal
Volume 2 Issue Pages 939–944
Keywords
Abstract
Address Curitiba (Brazil)
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 ICDAR
Notes DAG Approved no
Call Number DAG @ dag @ RSL2007b Serial 883
Permanent link to this record
 
 
Author Patricia Marquez; Debora Gil ; Aura Hernandez-Sabate
Title Error Analysis for Lucas-Kanade Based Schemes Type Conference Article
Year 2012 Publication (up) 9th International Conference on Image Analysis and Recognition Abbreviated Journal
Volume 7324 Issue I Pages 184-191
Keywords Optical flow, Confidence measure, Lucas-Kanade, Cardiac Magnetic Resonance
Abstract Optical flow is a valuable tool for motion analysis in medical imaging sequences. A reliable application requires determining the accuracy of the computed optical flow. This is a main challenge given the absence of ground truth in medical sequences. This paper presents an error analysis of Lucas-Kanade schemes in terms of intrinsic design errors and numerical stability of the algorithm. Our analysis provides a confidence measure that is naturally correlated to the accuracy of the flow field. Our experiments show the higher predictive value of our confidence measure compared to existing measures.
Address Aveiro, Portugal
Corporate Author Thesis
Publisher Springer-Verlag Berlin Heidelberg Place of Publication Editor
Language english Summary Language Original Title
Series Editor Campilho, Aurélio and Kamel, Mohamed Series Title Lecture Notes in Computer Science Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN 0302-9743 ISBN 978-3-642-31294-6 Medium
Area Expedition Conference ICIAR
Notes IAM Approved no
Call Number IAM @ iam @ MGH2012a Serial 1899
Permanent link to this record
 
 
Author Ricard Borras; Agata Lapedriza; Laura Igual
Title Depth Information in Human Gait Analysis: An Experimental Study on Gender Recognition Type Conference Article
Year 2012 Publication (up) 9th International Conference on Image Analysis and Recognition Abbreviated Journal
Volume 7325 Issue II Pages 98-105
Keywords
Abstract This work presents DGait, a new gait database acquired with a depth camera. This database contains videos from 53 subjects walking in different directions. The intent of this database is to provide a public set to explore whether the depth can be used as an additional information source for gait classification purposes. Each video is labelled according to subject, gender and age. Furthermore, for each subject and view point, we provide initial and final frames of an entire walk cycle. On the other hand, we perform gait-based gender classification experiments with DGait database, in order to illustrate the usefulness of depth information for this purpose. In our experiments, we extract 2D and 3D gait features based on shape descriptors, and compare the performance of these features for gender identification, using a Kernel SVM. The obtained results show that depth can be an information source of great relevance for gait classification problems.
Address Aveiro, Portugal
Corporate Author Thesis
Publisher Springer Berlin Heidelberg Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0302-9743 ISBN 978-3-642-31297-7 Medium
Area Expedition Conference ICIAR
Notes OR; MILAB;MV Approved no
Call Number Admin @ si @ BLI2012 Serial 2009
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Author Fernando Barrera; Felipe Lumbreras; Angel Sappa
Title Evaluation of Similarity Functions in Multimodal Stereo Type Conference Article
Year 2012 Publication (up) 9th International Conference on Image Analysis and Recognition Abbreviated Journal
Volume 7324 Issue I Pages 320-329
Keywords Aveiro, Portugal
Abstract This paper presents an evaluation framework for multimodal stereo matching, which allows to compare the performance of four similarity functions. Additionally, it presents details of a multimodal stereo head that supply thermal infrared and color images, as well as, aspects of its calibration and rectification. The pipeline includes a novel method for the disparity selection, which is suitable for evaluating the similarity functions. Finally, a benchmark for comparing different initializations of the proposed framework is presented. Similarity functions are based on mutual information, gradient orientation and scale space representations. Their evaluation is performed using two metrics: i) disparity error, and ii) number of correct matches on planar regions. In addition to the proposed evaluation, the current paper also shows that 3D sparse representations can be recovered from such a multimodal stereo head.
Address
Corporate Author Thesis
Publisher Springer Berlin Heidelberg 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 978-3-642-31294-6 Medium
Area Expedition Conference ICIAR
Notes ADAS Approved no
Call Number BLS2012a Serial 2014
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Author Miguel Oliveira; Angel Sappa; V. Santos
Title Color Correction using 3D Gaussian Mixture Models Type Conference Article
Year 2012 Publication (up) 9th International Conference on Image Analysis and Recognition Abbreviated Journal
Volume 7324 Issue I Pages 97-106
Keywords
Abstract The current paper proposes a novel color correction approach based on a probabilistic segmentation framework by using 3D Gaussian Mixture Models. Regions are used to compute local color correction functions, which are then combined to obtain the final corrected image. The proposed approach is evaluated using both a recently published metric and two large data sets composed of seventy images. The evaluation is performed by comparing our algorithm with eight well known color correction algorithms. Results show that the proposed approach is the highest scoring color correction method. Also, the proposed single step 3D color space probabilistic segmentation reduces processing time over similar approaches.
Address
Corporate Author Thesis
Publisher Springer Berlin Heidelberg 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 10.1007/978-3-642-31295-3_12 Medium
Area Expedition Conference ICIAR
Notes ADAS Approved no
Call Number Admin @ si @ OSS2012a Serial 2015
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Author Laura Igual; Joan Carles Soliva; Roger Gimeno; Sergio Escalera; Oscar Vilarroya; Petia Radeva
Title Automatic Internal Segmentation of Caudate Nucleus for Diagnosis of Attention Deficit Hyperactivity Disorder Type Conference Article
Year 2012 Publication (up) 9th International Conference on Image Analysis and Recognition Abbreviated Journal
Volume 7325 Issue II Pages 222-229
Keywords
Abstract Poster
Studies on volumetric brain Magnetic Resonance Imaging (MRI) showed neuroanatomical abnormalities in pediatric Attention-Deficit/Hyperactivity Disorder (ADHD). In particular, the diminished right caudate volume is one of the most replicated findings among ADHD samples in morphometric MRI studies. In this paper, we propose a fully-automatic method for internal caudate nucleus segmentation based on machine learning. Moreover, the ratio between right caudate body volume and the bilateral caudate body volume is applied in a ADHD diagnostic test. We separately validate the automatic internal segmentation of caudate in head and body structures and the diagnostic test using real data from ADHD and control subjects. As a result, we show accurate internal caudate segmentation and similar performance among the proposed automatic diagnostic test and the manual annotation.
Address Aveiro, Portugal
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 0302-9743 ISBN 978-3-642-31297-7 Medium
Area Expedition Conference ICIAR
Notes OR; HuPBA; MILAB Approved no
Call Number Admin @ si @ ISG2012 Serial 2059
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Author Panagiota Spyridonos; Fernando Vilariño; Jordi Vitria; Fernando Azpiroz; Petia Radeva
Title Anisotropic Feature Extraction from Endoluminal Images for Detection of Intestinal Contractions Type Book Chapter
Year 2006 Publication (up) 9th International Conference on Medical Image Computing and Computer–Assisted Intervention Abbreviated Journal
Volume 4191 Issue Pages 161–168
Keywords
Abstract Wireless endoscopy is a very recent and at the same time unique technique allowing to visualize and study the occurrence of con- tractions and to analyze the intestine motility. Feature extraction is es- sential for getting efficient patterns to detect contractions in wireless video endoscopy of small intestine. We propose a novel method based on anisotropic image filtering and efficient statistical classification of con- traction features. In particular, we apply the image gradient tensor for mining informative skeletons from the original image and a sequence of descriptors for capturing the characteristic pattern of contractions. Fea- tures extracted from the endoluminal images were evaluated in terms of their discriminatory ability in correct classifying images as either belong- ing to contractions or not. Classification was performed by means of a support vector machine classifier with a radial basis function kernel. Our classification rates gave sensitivity of the order of 90.84% and specificity of the order of 94.43% respectively. These preliminary results highlight the high efficiency of the selected descriptors and support the feasibility of the proposed method in assisting the automatic detection and analysis of contractions.
Address Copenhagen (Denmark)
Corporate Author Thesis
Publisher Springer Verlag Place of Publication Berlin Heidelberg Editor R. Larsen, M. Nielsen, and J. Sporring
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN ISBN Medium
Area 800 Expedition Conference MICCAI06
Notes MV;OR;MILAB;SIAI Approved no
Call Number BCNPCL @ bcnpcl @ SVV2006; IAM @ iam @ SVV2006 Serial 725
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Author Ellen J.L. Brunenberg; Oriol Pujol; Bart M. Ter Haar Romeny; Petia Radeva
Title Automatic IVUS Segmentation of Atherosclerotic Plaque with Stop & Go Snake Type Miscellaneous
Year 2006 Publication (up) 9th International Conference on Medical Image Computing and Computer–Assisted Intervention (MICCAI´06), 9–16 Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
Address Copenhagen (Denmark)
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 MILAB;HuPBA Approved no
Call Number BCNPCL @ bcnpcl @ BPT2006 Serial 767
Permanent link to this record
 
 
Author Lluis Pere de las Heras; Joan Mas; Gemma Sanchez; Ernest Valveny
Title Descriptor-based Svm Wall Detector Type Conference Article
Year 2011 Publication (up) 9th International Workshop on Graphic Recognition Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Architectural floorplans exhibit a large variability in notation. Therefore, segmenting and identifying the elements of any kind of plan becomes a challenging task for approaches based on grouping structural primitives obtained by vectorization. Recently, a patch-based segmentation method working at pixel level and relying on the construction of a visual vocabulary has been proposed showing its adaptability to different notations by automatically learning the visual appearance of the elements in each different notation. In this paper we describe an evolution of this new approach in two directions: firstly we evaluate different features to obtain the description of every patch. Secondly, we train an SVM classifier to obtain the category of every patch instead of constructing a visual vocabulary. These modifications of the method have been tested for wall detection on two datasets of architectural floorplans with different notations and compared with the results obtained with the original approach.
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 GREC
Notes DAG Approved no
Call Number Admin @ si @ HMS2011b Serial 1819
Permanent link to this record
 
 
Author Mohamed Ilyes Lakhal; Albert Clapes; Sergio Escalera; Oswald Lanz; Andrea Cavallaro
Title Residual Stacked RNNs for Action Recognition Type Conference Article
Year 2018 Publication (up) 9th International Workshop on Human Behavior Understanding Abbreviated Journal
Volume Issue Pages 534-548
Keywords Action recognition; Deep residual learning; Two-stream RNN
Abstract Action recognition pipelines that use Recurrent Neural Networks (RNN) are currently 5–10% less accurate than Convolutional Neural Networks (CNN). While most works that use RNNs employ a 2D CNN on each frame to extract descriptors for action recognition, we extract spatiotemporal features from a 3D CNN and then learn the temporal relationship of these descriptors through a stacked residual recurrent neural network (Res-RNN). We introduce for the first time residual learning to counter the degradation problem in multi-layer RNNs, which have been successful for temporal aggregation in two-stream action recognition pipelines. Finally, we use a late fusion strategy to combine RGB and optical flow data of the two-stream Res-RNN. Experimental results show that the proposed pipeline achieves competitive results on UCF-101 and state of-the-art results for RNN-like architectures on the challenging HMDB-51 dataset.
Address Munich; September 2018
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 ECCVW
Notes HUPBA; no proj Approved no
Call Number Admin @ si @ LCE2018b Serial 3206
Permanent link to this record
 
 
Author Santiago Segui; Laura Igual; Jordi Vitria
Title Weighted Bagging for Graph based One-Class Classifiers Type Conference Article
Year 2010 Publication (up) 9th International Workshop on Multiple Classifier Systems Abbreviated Journal
Volume 5997 Issue Pages 1-10
Keywords
Abstract Most conventional learning algorithms require both positive and negative training data for achieving accurate classification results. However, the problem of learning classifiers from only positive data arises in many applications where negative data are too costly, difficult to obtain, or not available at all. Minimum Spanning Tree Class Descriptor (MSTCD) was presented as a method that achieves better accuracies than other one-class classifiers in high dimensional data. However, the presence of outliers in the target class severely harms the performance of this classifier. In this paper we propose two bagging strategies for MSTCD that reduce the influence of outliers in training data. We show the improved performance on both real and artificially contaminated data.
Address Cairo, Egypt
Corporate Author Thesis
Publisher Springer Berlin Heidelberg 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 978-3-642-12126-5 Medium
Area Expedition Conference MCS
Notes MILAB;OR;MV Approved no
Call Number BCNPCL @ bcnpcl @ SIV2010 Serial 1284
Permanent link to this record
 
 
Author V. Valev; Petia Radeva
Title Constructing Quantitative Non-Reducible Descriptors. Type Miscellaneous
Year 1995 Publication (up) 9th Scandinavian Conference on Artificial Intelligence Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
Address Sweden
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 MILAB Approved no
Call Number BCNPCL @ bcnpcl @ VaR1995b Serial 140
Permanent link to this record
 
 
Author Sergio Vera; Miguel Angel Gonzalez Ballester; Debora Gil
Title Optimal Medial Surface Generation for Anatomical Volume Representations Type Book Chapter
Year 2012 Publication (up) Abdominal Imaging. Computational and Clinical Applications Abbreviated Journal LNCS
Volume 7601 Issue Pages 265-273
Keywords Medial surface representation; volume reconstruction
Abstract Medial representations are a widely used technique in abdominal organ shape representation and parametrization. Those methods require good medial manifolds as a starting point. Any medial
surface used to parametrize a volume should be simple enough to allow an easy manipulation and complete enough to allow an accurate reconstruction of the volume. Obtaining good quality medial
surfaces is still a problem with current iterative thinning methods. This forces the usage of generic, pre-calculated medial templates that are adapted to the final shape at the cost of a drop in volume reconstruction.
This paper describes an operator for generation of medial structures that generates clean and complete manifolds well suited for their further use in medial representations of abdominal organ volumes. While being simpler than thinning surfaces, experiments show its high performance in volume reconstruction and preservation of medial surface main branching topology.
Address Nice, France
Corporate Author Thesis
Publisher Springer Berlin Heidelberg Place of Publication Editor Yoshida, Hiroyuki and Hawkes, David and Vannier, MichaelW.
Language Summary Language Original Title
Series Editor Series Title Lecture Notes in Computer Science Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0302-9743 ISBN 978-3-642-33611-9 Medium
Area Expedition Conference STACOM
Notes IAM Approved no
Call Number IAM @ iam @ VGG2012b Serial 1988
Permanent link to this record
 
 
Author David Guillamet; Jordi Vitria
Title Non-negative Matrix Factorization to Extract Part-Based Representations. Type Miscellaneous
Year 2001 Publication (up) Accepted for 4th Catalan Conference for Artificial Intelligence. Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
Address Barcelona
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 OR;MV Approved no
Call Number BCNPCL @ bcnpcl @ GVi2001a Serial 105
Permanent link to this record
 
 
Author Y. Patel; Lluis Gomez; Marçal Rusiñol; Dimosthenis Karatzas; C.V. Jawahar
Title Self-Supervised Visual Representations for Cross-Modal Retrieval Type Conference Article
Year 2019 Publication (up) ACM International Conference on Multimedia Retrieval Abbreviated Journal
Volume Issue Pages 182–186
Keywords
Abstract Cross-modal retrieval methods have been significantly improved in last years with the use of deep neural networks and large-scale annotated datasets such as ImageNet and Places. However, collecting and annotating such datasets requires a tremendous amount of human effort and, besides, their annotations are limited to discrete sets of popular visual classes that may not be representative of the richer semantics found on large-scale cross-modal retrieval datasets. In this paper, we present a self-supervised cross-modal retrieval framework that leverages as training data the correlations between images and text on the entire set of Wikipedia articles. Our method consists in training a CNN to predict: (1) the semantic context of the article in which an image is more probable to appear as an illustration, and (2) the semantic context of its caption. Our experiments demonstrate that the proposed method is not only capable of learning discriminative visual representations for solving vision tasks like classification, but that the learned representations are better for cross-modal retrieval when compared to supervised pre-training of the network on the ImageNet dataset.
Address Otawa; Canada; june 2019
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 ICMR
Notes DAG; 600.121; 600.129 Approved no
Call Number Admin @ si @ PGR2019 Serial 3288
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