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Author Ignasi Rius; Javier Varona; Jordi Gonzalez; Juan J. Villanueva
Title Action Spaces for Efficient Bayesian Tracking of Human Motion Type Miscellaneous
Year 2006 Publication International Conference on Pattern Recognition (ICPR´06), 1: 472–475 Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
Address (up) Hong Kong (China)
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 Approved no
Call Number ISE @ ise @ RVG2006 Serial 770
Permanent link to this record
 
 
Author W. Liu; Josep Llados
Title Graphics Recognition. Ten Years Review and Future Perspectives Type Book Whole
Year 2006 Publication 6th International Workshop Abbreviated Journal
Volume 3926 Issue Pages
Keywords
Abstract
Address (up) Hong Kong (China)
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 GREC
Notes DAG Approved no
Call Number DAG @ dag @ LiL2006 Serial 800
Permanent link to this record
 
 
Author Alicia Fornes; Josep Llados; Gemma Sanchez
Title Primitive Segmentation in Old Handwritten Music Scores Type Miscellaneous
Year 2005 Publication 6th IAPR International Workshop on Graphics Recognition (GREC 2005) Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
Address (up) Hong Kong, Hong Kong SAR (China)
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 Approved no
Call Number DAG @ dag @ FLS2005a Serial 584
Permanent link to this record
 
 
Author Fernando Barrera; Felipe Lumbreras; Angel Sappa
Title Multimodal Template Matching based on Gradient and Mutual Information using Scale-Space Type Conference Article
Year 2010 Publication 17th IEEE International Conference on Image Processing Abbreviated Journal
Volume Issue Pages 2749–2752
Keywords
Abstract This paper presents the combined use of gradient and mutual information for infrared and intensity templates matching. We propose to joint: (i) feature matching in a multiresolution context and (ii) information propagation through scale-space representations. Our method consists in combining mutual information with a shape descriptor based on gradient, and propagate them following a coarse-to-fine strategy. The main contributions of this work are: to offer a theoretical formulation towards a multimodal stereo matching; to show that gradient and mutual information can be reinforced while they are propagated between consecutive levels; and to show that they are valid cost functions in multimodal template matchings. Comparisons are presented showing the improvements and viability of the proposed approach.
Address (up) Hong-Kong
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 1522-4880 ISBN 978-1-4244-7992-4 Medium
Area Expedition Conference ICIP
Notes ADAS Approved no
Call Number ADAS @ adas @ BLS2010 Serial 1358
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Author Mohammad Rouhani; Angel Sappa
Title A Fast accurate Implicit Polynomial Fitting Approach Type Conference Article
Year 2010 Publication 17th IEEE International Conference on Image Processing Abbreviated Journal
Volume Issue Pages 1429–1432
Keywords
Abstract This paper presents a novel hybrid approach that combines state of the art fitting algorithms: algebraic-based and geometric-based. It consists of two steps; first, the 3L algorithm is used as an initialization and then, the obtained result, is improved through a geometric approach. The adopted geometric approach is based on a distance estimation that avoids costly search for the real orthogonal distance. Experimental results are presented as well as quantitative comparisons.
Address (up) Hong-Kong
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 1522-4880 ISBN 978-1-4244-7992-4 Medium
Area Expedition Conference ICIP
Notes ADAS Approved no
Call Number ADAS @ adas @ RoS2010b Serial 1359
Permanent link to this record
 
 
Author Jaume Amores; David Geronimo; Antonio Lopez
Title Multiple instance and active learning for weakly-supervised object-class segmentation Type Conference Article
Year 2010 Publication 3rd IEEE International Conference on Machine Vision Abbreviated Journal
Volume Issue Pages
Keywords Multiple Instance Learning; Active Learning; Object-class segmentation.
Abstract In object-class segmentation, one of the most tedious tasks is to manually segment many object examples in order to learn a model of the object category. Yet, there has been little research on reducing the degree of manual annotation for
object-class segmentation. In this work we explore alternative strategies which do not require full manual segmentation of the object in the training set. In particular, we study the use of bounding boxes as a coarser and much cheaper form of segmentation and we perform a comparative study of several Multiple-Instance Learning techniques that allow to obtain a model with this type of weak annotation. We show that some of these methods can be competitive, when used with coarse
segmentations, with methods that require full manual segmentation of the objects. Furthermore, we show how to use active learning combined with this weakly supervised strategy.
As we see, this strategy permits to reduce the amount of annotation and optimize the number of examples that require full manual segmentation in the training set.
Address (up) Hong-Kong
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 ICMV
Notes ADAS Approved no
Call Number ADAS @ adas @ AGL2010b Serial 1429
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Author Lluis Gomez; Y. Patel; Marçal Rusiñol; C.V. Jawahar; Dimosthenis Karatzas
Title Self‐supervised learning of visual features through embedding images into text topic spaces Type Conference Article
Year 2017 Publication 30th IEEE Conference on Computer Vision and Pattern Recognition Abbreviated Journal
Volume Issue Pages
Keywords
Abstract End-to-end training from scratch of current deep architectures for new computer vision problems would require Imagenet-scale datasets, and this is not always possible. In this paper we present a method that is able to take advantage of freely available multi-modal content to train computer vision algorithms without human supervision. We put forward the idea of performing self-supervised learning of visual features by mining a large scale corpus of multi-modal (text and image) documents. We show that discriminative visual features can be learnt efficiently by training a CNN to predict the semantic context in which a particular image is more probable to appear as an illustration. For this we leverage the hidden semantic structures discovered in the text corpus with a well-known topic modeling technique. Our experiments demonstrate state of the art performance in image classification, object detection, and multi-modal retrieval compared to recent self-supervised or natural-supervised approaches.
Address (up) Honolulu; Hawaii; July 2017
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 CVPR
Notes DAG; 600.084; 600.121 Approved no
Call Number Admin @ si @ GPR2017 Serial 2889
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Author Cesar de Souza; Adrien Gaidon; Yohann Cabon; Antonio Lopez
Title Procedural Generation of Videos to Train Deep Action Recognition Networks Type Conference Article
Year 2017 Publication 30th IEEE Conference on Computer Vision and Pattern Recognition Abbreviated Journal
Volume Issue Pages 2594-2604
Keywords
Abstract Deep learning for human action recognition in videos is making significant progress, but is slowed down by its dependency on expensive manual labeling of large video collections. In this work, we investigate the generation of synthetic training data for action recognition, as it has recently shown promising results for a variety of other computer vision tasks. We propose an interpretable parametric generative model of human action videos that relies on procedural generation and other computer graphics techniques of modern game engines. We generate a diverse, realistic, and physically plausible dataset of human action videos, called PHAV for ”Procedural Human Action Videos”. It contains a total of 39, 982 videos, with more than 1, 000 examples for each action of 35 categories. Our approach is not limited to existing motion capture sequences, and we procedurally define 14 synthetic actions. We introduce a deep multi-task representation learning architecture to mix synthetic and real videos, even if the action categories differ. Our experiments on the UCF101 and HMDB51 benchmarks suggest that combining our large set of synthetic videos with small real-world datasets can boost recognition performance, significantly
outperforming fine-tuning state-of-the-art unsupervised generative models of videos.
Address (up) Honolulu; Hawaii; July 2017
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 CVPR
Notes ADAS; 600.076; 600.085; 600.118 Approved no
Call Number Admin @ si @ SGC2017 Serial 3051
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Author Patricia Suarez; Angel Sappa; Boris X. Vintimilla
Title Infrared Image Colorization based on a Triplet DCGAN Architecture Type Conference Article
Year 2017 Publication IEEE Conference on Computer Vision and Pattern Recognition Workshops Abbreviated Journal
Volume Issue Pages
Keywords
Abstract This paper proposes a novel approach for colorizing near infrared (NIR) images using Deep Convolutional Generative Adversarial Network (GAN) architectures. The proposed approach is based on the usage of a triplet model for learning each color channel independently, in a more homogeneous way. It allows a fast convergence during the training, obtaining a greater similarity between the given NIR image and the corresponding ground truth. The proposed approach has been evaluated with a large data set of NIR images and compared with a recent approach, which is also based on a GAN architecture but in this case all the
color channels are obtained at the same time.
Address (up) Honolulu; Hawaii; USA; July 2017
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 CVPRW
Notes ADAS; 600.086; 600.118 Approved no
Call Number Admin @ si @ SSV2017b Serial 2920
Permanent link to this record
 
 
Author Simon Jégou; Michal Drozdzal; David Vazquez; Adriana Romero; Yoshua Bengio
Title The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation Type Conference Article
Year 2017 Publication IEEE Conference on Computer Vision and Pattern Recognition Workshops Abbreviated Journal
Volume Issue Pages
Keywords Semantic Segmentation
Abstract State-of-the-art approaches for semantic image segmentation are built on Convolutional Neural Networks (CNNs). The typical segmentation architecture is composed of (a) a downsampling path responsible for extracting coarse semantic features, followed by (b) an upsampling path trained to recover the input image resolution at the output of the model and, optionally, (c) a post-processing module (e.g. Conditional Random Fields) to refine the model predictions.

Recently, a new CNN architecture, Densely Connected Convolutional Networks (DenseNets), has shown excellent results on image classification tasks. The idea of DenseNets is based on the observation that if each layer is directly connected to every other layer in a feed-forward fashion then the network will be more accurate and easier to train.

In this paper, we extend DenseNets to deal with the problem of semantic segmentation. We achieve state-of-the-art results on urban scene benchmark datasets such as CamVid and Gatech, without any further post-processing module nor pretraining. Moreover, due to smart construction of the model, our approach has much less parameters than currently published best entries for these datasets.
Address (up) Honolulu; USA; July 2017
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 CVPRW
Notes MILAB; ADAS; 600.076; 600.085; 601.281 Approved no
Call Number ADAS @ adas @ JDV2016 Serial 2866
Permanent link to this record
 
 
Author Utkarsh Porwal; Alicia Fornes; Faisal Shafait (eds)
Title Frontiers in Handwriting Recognition. International Conference on Frontiers in Handwriting Recognition. 18th International Conference, ICFHR 2022 Type Book Whole
Year 2022 Publication Frontiers in Handwriting Recognition. Abbreviated Journal
Volume 13639 Issue Pages
Keywords
Abstract
Address (up) ICFHR 2022, Hyderabad, India, December 4–7, 2022
Corporate Author Thesis
Publisher Springer Place of Publication Editor Utkarsh Porwal; Alicia Fornes; Faisal Shafait
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN ISBN 978-3-031-21648-0 Medium
Area Expedition Conference ICFHR
Notes DAG Approved no
Call Number Admin @ si @ PFS2022 Serial 3809
Permanent link to this record
 
 
Author Md. Mostafa Kamal Sarker; Syeda Furruka Banu; Hatem A. Rashwan; Mohamed Abdel-Nasser; Vivek Kumar Singh; Sylvie Chambon; Petia Radeva; Domenec Puig
Title Food Places Classification in Egocentric Images Using Siamese Neural Networks Type Conference Article
Year 2019 Publication 22nd International Conference of the Catalan Association of Artificial Intelligence Abbreviated Journal
Volume Issue Pages 145-151
Keywords
Abstract Wearable cameras are become more popular in recent years for capturing the unscripted moments of the first-person that help to analyze the users lifestyle. In this work, we aim to recognize the places related to food in egocentric images during a day to identify the daily food patterns of the first-person. Thus, this system can assist to improve their eating behavior to protect users against food-related diseases. In this paper, we use Siamese Neural Networks to learn the similarity between images from corresponding inputs for one-shot food places classification. We tested our proposed method with ‘MiniEgoFoodPlaces’ with 15 food related places. The proposed Siamese Neural Networks model with MobileNet achieved an overall classification accuracy of 76.74% and 77.53% on the validation and test sets of the “MiniEgoFoodPlaces” dataset, respectively outperforming with the base models, such as ResNet50, InceptionV3, and InceptionResNetV2.
Address (up) Illes Balears; October 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 CCIA
Notes MILAB; no proj Approved no
Call Number Admin @ si @ SBR2019 Serial 3368
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Author Felipe Lumbreras; Ramon Baldrich; Maria Vanrell; Joan Serrat; Juan J. Villanueva
Title Multiresolution texture classification of ceramic tiles. Type Book Chapter
Year 1999 Publication Recent Research developments in optical engineering, Research Signpost, 2: 213–228 Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
Address (up) India
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 ADAS;CIC Approved no
Call Number ADAS @ adas @ LBV1999b Serial 45
Permanent link to this record
 
 
Author Marc Oliu; Sarah Adel Bargal; Stan Sclaroff; Xavier Baro; Sergio Escalera
Title Multi-varied Cumulative Alignment for Domain Adaptation Type Conference Article
Year 2022 Publication 6th International Conference on Image Analysis and Processing Abbreviated Journal
Volume 13232 Issue Pages 324–334
Keywords Domain Adaptation; Computer vision; Neural networks
Abstract Domain Adaptation methods can be classified into two basic families of approaches: non-parametric and parametric. Non-parametric approaches depend on statistical indicators such as feature covariances to minimize the domain shift. Non-parametric approaches tend to be fast to compute and require no additional parameters, but they are unable to leverage probability density functions with complex internal structures. Parametric approaches, on the other hand, use models of the probability distributions as surrogates in minimizing the domain shift, but they require additional trainable parameters to model these distributions. In this work, we propose a new statistical approach to minimizing the domain shift based on stochastically projecting and evaluating the cumulative density function in both domains. As with non-parametric approaches, there are no additional trainable parameters. As with parametric approaches, the internal structure of both domains’ probability distributions is considered, thus leveraging a higher amount of information when reducing the domain shift. Evaluation on standard datasets used for Domain Adaptation shows better performance of the proposed model compared to non-parametric approaches while being competitive with parametric ones. (Code available at: https://github.com/moliusimon/mca).
Address (up) Indonesia; October 2022
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 ICIAP
Notes HuPBA; no menciona Approved no
Call Number Admin @ si @ OAS2022 Serial 3777
Permanent link to this record
 
 
Author Dimosthenis Karatzas;Ch. Lioutas
Title Software Package Development for Electron Diffraction Image Analysis Type Conference Article
Year 1998 Publication Proceedings of the XIV Solid State Physics National Conference Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
Address (up) Ioannina, Greece
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 Approved no
Call Number IAM @ iam @ KaL1998 Serial 2045
Permanent link to this record