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Author Antonio Clavelli; Dimosthenis Karatzas; Josep Llados; Mario Ferraro; Giuseppe Boccignone
Title Towards Modelling an Attention-Based Text Localization Process Type Conference Article
Year 2013 Publication 6th Iberian Conference on Pattern Recognition and Image Analysis Abbreviated Journal
Volume 7887 Issue Pages 296-303
Keywords text localization; visual attention; eye guidance
Abstract This note introduces a visual attention model of text localization in real-world scenes. The core of the model built upon the proto-object concept is discussed. It is shown how such dynamic mid-level representation of the scene can be derived in the framework of an action-perception loop engaging salience, text information value computation, and eye guidance mechanisms.
Preliminary results that compare model generated scanpaths with those eye-tracked from human subjects are presented.
Address Madeira; Portugal; June 2013
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 978-3-642-38627-5 Medium
Area Expedition Conference IbPRIA
Notes DAG Approved no
Call Number Admin @ si @ CKL2013 Serial 2291
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Author Jaume Gibert; Ernest Valveny; Horst Bunke
Title Graph of Words Embedding for Molecular Structure-Activity Relationship Analysis Type Conference Article
Year 2010 Publication 15th Iberoamerican Congress on Pattern Recognition Abbreviated Journal
Volume 6419 Issue Pages 30–37
Keywords
Abstract Structure-Activity relationship analysis aims at discovering chemical activity of molecular compounds based on their structure. In this article we make use of a particular graph representation of molecules and propose a new graph embedding procedure to solve the problem of structure-activity relationship analysis. The embedding is essentially an arrangement of a molecule in the form of a vector by considering frequencies of appearing atoms and frequencies of covalent bonds between them. Results on two benchmark databases show the effectiveness of the proposed technique in terms of recognition accuracy while avoiding high operational costs in the transformation.
Address Sao Paulo, Brazil
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 978-3-642-16686-0 Medium
Area Expedition Conference CIARP
Notes DAG Approved no
Call Number DAG @ dag @ GVB2010 Serial 1462
Permanent link to this record
 
 
Author David Aldavert; Ricardo Toledo; Arnau Ramisa; Ramon Lopez de Mantaras
Title Efficient Object Pixel-Level Categorization using Bag of Features: Advances in Visual Computing Type Conference Article
Year 2009 Publication 5th International Symposium on Visual Computing Abbreviated Journal
Volume 5875 Issue Pages 44–55
Keywords
Abstract In this paper we present a pixel-level object categorization method suitable to be applied under real-time constraints. Since pixels are categorized using a bag of features scheme, the major bottleneck of such an approach would be the feature pooling in local histograms of visual words. Therefore, we propose to bypass this time-consuming step and directly obtain the score from a linear Support Vector Machine classifier. This is achieved by creating an integral image of the components of the SVM which can readily obtain the classification score for any image sub-window with only 10 additions and 2 products, regardless of its size. Besides, we evaluated the performance of two efficient feature quantization methods: the Hierarchical K-Means and the Extremely Randomized Forest. All experiments have been done in the Graz02 database, showing comparable, or even better results to related work with a lower computational cost.
Address Las Vegas, USA
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 978-3-642-10330-8 Medium
Area Expedition Conference ISVC
Notes ADAS Approved no
Call Number Admin @ si @ ATR2009a Serial 1246
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Author Pierluigi Casale; Oriol Pujol; Petia Radeva
Title Face-to-face social activity detection using data collected with a wearable device Type Conference Article
Year 2009 Publication 4th Iberian Conference on Pattern Recognition and Image Analysis Abbreviated Journal
Volume 5524 Issue Pages 56–63
Keywords
Abstract In this work the feasibility of building a socially aware badge that learns from user activities is explored. A wearable multisensor device has been prototyped for collecting data about user movements and photos of the environment where the user acts. Using motion data, speaking and other activities have been classified. Images have been analysed in order to complement motion data and help for the detection of social behaviours. A face detector and an activity classifier are both used for detecting if users have a social activity in the time they worn the device. Good results encourage the improvement of the system at both hardware and software level
Address Póvoa de Varzim, Portugal
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 978-3-642-02171-8 Medium
Area Expedition Conference IbPRIA
Notes MILAB;HuPBA Approved no
Call Number BCNPCL @ bcnpcl @ CPR2009b Serial 1206
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Author Santiago Segui; Laura Igual; Fernando Vilariño; Petia Radeva; Carolina Malagelada; Fernando Azpiroz; Jordi Vitria
Title Diagnostic System for Intestinal Motility Disfunctions Using Video Capsule Endoscopy Type Book Chapter
Year 2008 Publication Computer Vision Systems. 6th International Abbreviated Journal
Volume 5008 Issue Pages 251–260
Keywords
Abstract Wireless Video Capsule Endoscopy is a clinical technique consisting of the analysis of images from the intestine which are pro- vided by an ingestible device with a camera attached to it. In this paper we propose an automatic system to diagnose severe intestinal motility disfunctions using the video endoscopy data. The system is based on the application of computer vision techniques within a machine learn- ing framework in order to obtain the characterization of diverse motil- ity events from video sequences. We present experimental results that demonstrate the effectiveness of the proposed system and compare them with the ground-truth provided by the gastroenterologists.
Address Santorini (Greece)
Corporate Author Thesis
Publisher Springer-Verlag Place of Publication Berlin Heidelberg Editor A. Gasteratos, M. Vincze, and J.K. Tsotsos
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN ISBN 978-3-540-79546-9 Medium
Area 800 Expedition Conference ICVS
Notes OR; MV; MILAB; SIAI Approved no
Call Number BCNPCL @ bcnpcl @ SIV2008; IAM @ iam @ SIV2008 Serial 962
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Author Patricia Suarez; Angel Sappa; Boris X. Vintimilla
Title Vegetation Index Estimation from Monospectral Images Type Conference Article
Year 2018 Publication 15th International Conference on Images Analysis and Recognition Abbreviated Journal
Volume 10882 Issue Pages 353-362
Keywords
Abstract This paper proposes a novel approach to estimate Normalized Difference Vegetation Index (NDVI) from just the red channel of a RGB image. The NDVI index is defined as the ratio of the difference of the red and infrared radiances over their sum. In other words, information from the red channel of a RGB image and the corresponding infrared spectral band are required for its computation. In the current work the NDVI index is estimated just from the red channel by training a Conditional Generative Adversarial Network (CGAN). The architecture proposed for the generative network consists of a single level structure, which combines at the final layer results from convolutional operations together with the given red channel with Gaussian noise to enhance
details, resulting in a sharp NDVI image. Then, the discriminative model
estimates the probability that the NDVI generated index came from the training dataset, rather than the index automatically generated. Experimental results with a large set of real images are provided showing that a Conditional GAN single level model represents an acceptable approach to estimate NDVI index.
Address Povoa de Varzim; Portugal; June 2018
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 ICIAR
Notes MSIAU; 600.086; 600.130; 600.122 Approved no
Call Number Admin @ si @ SSV2018c Serial 3196
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Author Vacit Oguz Yazici; Joost Van de Weijer; Arnau Ramisa
Title Color Naming for Multi-Color Fashion Items Type Conference Article
Year 2018 Publication 6th World Conference on Information Systems and Technologies Abbreviated Journal
Volume 747 Issue Pages 64-73
Keywords Deep learning; Color; Multi-label
Abstract There exists a significant amount of research on color naming of single colored objects. However in reality many fashion objects consist of multiple colors. Currently, searching in fashion datasets for multi-colored objects can be a laborious task. Therefore, in this paper we focus on color naming for images with multi-color fashion items. We collect a dataset, which consists of images which may have from one up to four colors. We annotate the images with the 11 basic colors of the English language. We experiment with several designs for deep neural networks with different losses. We show that explicitly estimating the number of colors in the fashion item leads to improved results.
Address Naples; March 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 WORLDCIST
Notes LAMP; 600.109; 601.309; 600.120 Approved no
Call Number Admin @ si @ YWR2018 Serial 3161
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Author Patricia Suarez; Angel Sappa; Boris X. Vintimilla
Title Learning to Colorize Infrared Images Type Conference Article
Year 2017 Publication 15th International Conference on Practical Applications of Agents and Multi-Agent System Abbreviated Journal
Volume Issue Pages
Keywords CNN in multispectral imaging; Image colorization
Abstract This paper focuses on near infrared (NIR) image colorization by using a Generative Adversarial Network (GAN) architecture model. The proposed architecture consists of two stages. Firstly, it learns to colorize the given input, resulting in a RGB image. Then, in the second stage, a discriminative model is used to estimate the probability that the generated image came from the training dataset, rather than the image automatically generated. The proposed model starts the learning process from scratch, because our set of images is very di erent from the dataset used in existing pre-trained models, so transfer learning strategies cannot be used. Infrared image colorization is an important problem when human perception need to be considered, e.g, in remote sensing applications. Experimental results with a large set of real images are provided showing the validity of the proposed approach.
Address Porto; Portugal; June 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 PAAMS
Notes ADAS; MSIAU; 600.086; 600.122; 600.118 Approved no
Call Number Admin @ si @ Serial 2919
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Author Hana Jarraya; Oriol Ramos Terrades; Josep Llados
Title Graph Embedding through Probabilistic Graphical Model applied to Symbolic Graphs Type Conference Article
Year 2017 Publication 8th Iberian Conference on Pattern Recognition and Image Analysis Abbreviated Journal
Volume Issue Pages
Keywords Attributed Graph; Probabilistic Graphical Model; Graph Embedding; Structured Support Vector Machines
Abstract We propose a new Graph Embedding (GEM) method that takes advantages of structural pattern representation. It models an Attributed Graph (AG) as a Probabilistic Graphical Model (PGM). Then, it learns the parameters of this PGM presented by a vector. This vector is a signature of AG in a lower dimensional vectorial space. We apply Structured Support Vector Machines (SSVM) to process classification task. As first tentative, results on the GREC dataset are encouraging enough to go further on this direction.
Address Faro; Portugal; June 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 IbPRIA
Notes DAG; 600.097; 600.121 Approved no
Call Number Admin @ si @ JRL2017a Serial 2953
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Author German Ros; Laura Sellart; Gabriel Villalonga; Elias Maidanik; Francisco Molero; Marc Garcia; Adriana Cedeño; Francisco Perez; Didier Ramirez; Eduardo Escobar; Jose Luis Gomez; David Vazquez; Antonio Lopez
Title Semantic Segmentation of Urban Scenes via Domain Adaptation of SYNTHIA Type Book Chapter
Year 2017 Publication Domain Adaptation in Computer Vision Applications Abbreviated Journal
Volume 12 Issue Pages 227-241
Keywords SYNTHIA; Virtual worlds; Autonomous Driving
Abstract Vision-based semantic segmentation in urban scenarios is a key functionality for autonomous driving. Recent revolutionary results of deep convolutional neural networks (DCNNs) foreshadow the advent of reliable classifiers to perform such visual tasks. However, DCNNs require learning of many parameters from raw images; thus, having a sufficient amount of diverse images with class annotations is needed. These annotations are obtained via cumbersome, human labour which is particularly challenging for semantic segmentation since pixel-level annotations are required. In this chapter, we propose to use a combination of a virtual world to automatically generate realistic synthetic images with pixel-level annotations, and domain adaptation to transfer the models learnt to correctly operate in real scenarios. We address the question of how useful synthetic data can be for semantic segmentation – in particular, when using a DCNN paradigm. In order to answer this question we have generated a synthetic collection of diverse urban images, named SYNTHIA, with automatically generated class annotations and object identifiers. We use SYNTHIA in combination with publicly available real-world urban images with manually provided annotations. Then, we conduct experiments with DCNNs that show that combining SYNTHIA with simple domain adaptation techniques in the training stage significantly improves performance on semantic segmentation.
Address
Corporate Author Thesis
Publisher Springer Place of Publication Editor Gabriela Csurka
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; 600.085; 600.082; 600.076; 600.118 Approved no
Call Number ADAS @ adas @ RSV2017 Serial 2882
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Author Mohamed Ramzy Ibrahim; Robert Benavente; Daniel Ponsa; Felipe Lumbreras
Title Unveiling the Influence of Image Super-Resolution on Aerial Scene Classification Type Conference Article
Year 2023 Publication Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications Abbreviated Journal
Volume 14469 Issue Pages 214–228
Keywords
Abstract Deep learning has made significant advances in recent years, and as a result, it is now in a stage where it can achieve outstanding results in tasks requiring visual understanding of scenes. However, its performance tends to decline when dealing with low-quality images. The advent of super-resolution (SR) techniques has started to have an impact on the field of remote sensing by enabling the restoration of fine details and enhancing image quality, which could help to increase performance in other vision tasks. However, in previous works, contradictory results for scene visual understanding were achieved when SR techniques were applied. In this paper, we present an experimental study on the impact of SR on enhancing aerial scene classification. Through the analysis of different state-of-the-art SR algorithms, including traditional methods and deep learning-based approaches, we unveil the transformative potential of SR in overcoming the limitations of low-resolution (LR) aerial imagery. By enhancing spatial resolution, more fine details are captured, opening the door for an improvement in scene understanding. We also discuss the effect of different image scales on the quality of SR and its effect on aerial scene classification. Our experimental work demonstrates the significant impact of SR on enhancing aerial scene classification compared to LR images, opening new avenues for improved remote sensing applications.
Address
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 CIARP
Notes MSIAU Approved no
Call Number Admin @ si @ IBP2023 Serial 4008
Permanent link to this record
 
 
Author Yael Tudela; Ana Garcia Rodriguez; Gloria Fernandez Esparrach; Jorge Bernal
Title Towards Fine-Grained Polyp Segmentation and Classification Type Conference Article
Year 2023 Publication Workshop on Clinical Image-Based Procedures Abbreviated Journal
Volume 14242 Issue Pages 32-42
Keywords Medical image segmentation; Colorectal Cancer; Vision Transformer; Classification
Abstract Colorectal cancer is one of the main causes of cancer death worldwide. Colonoscopy is the gold standard screening tool as it allows lesion detection and removal during the same procedure. During the last decades, several efforts have been made to develop CAD systems to assist clinicians in lesion detection and classification. Regarding the latter, and in order to be used in the exploration room as part of resect and discard or leave-in-situ strategies, these systems must identify correctly all different lesion types. This is a challenging task, as the data used to train these systems presents great inter-class similarity, high class imbalance, and low representation of clinically relevant histology classes such as serrated sessile adenomas.

In this paper, a new polyp segmentation and classification method, Swin-Expand, is introduced. Based on Swin-Transformer, it uses a simple and lightweight decoder. The performance of this method has been assessed on a novel dataset, comprising 1126 high-definition images representing the three main histological classes. Results show a clear improvement in both segmentation and classification performance, also achieving competitive results when tested in public datasets. These results confirm that both the method and the data are important to obtain more accurate polyp representations.
Address Vancouver; October 2023
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 MICCAIW
Notes ISE Approved no
Call Number Admin @ si @ TGF2023 Serial 3837
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Author Albert Tatjer; Bhalaji Nagarajan; Ricardo Marques; Petia Radeva
Title CCLM: Class-Conditional Label Noise Modelling Type Conference Article
Year 2023 Publication 11th Iberian Conference on Pattern Recognition and Image Analysis Abbreviated Journal
Volume 14062 Issue Pages 3-14
Keywords
Abstract The performance of deep neural networks highly depends on the quality and volume of the training data. However, cost-effective labelling processes such as crowdsourcing and web crawling often lead to data with noisy (i.e., wrong) labels. Making models robust to this label noise is thus of prime importance. A common approach is using loss distributions to model the label noise. However, the robustness of these methods highly depends on the accuracy of the division of training set into clean and noisy samples. In this work, we dive in this research direction highlighting the existing problem of treating this distribution globally and propose a class-conditional approach to split the clean and noisy samples. We apply our approach to the popular DivideMix algorithm and show how the local treatment fares better with respect to the global treatment of loss distribution. We validate our hypothesis on two popular benchmark datasets and show substantial improvements over the baseline experiments. We further analyze the effectiveness of the proposal using two different metrics – Noise Division Accuracy and Classiness.
Address Alicante; Spain; June 2023
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 IbPRIA
Notes MILAB Approved no
Call Number Admin @ si @ TNM2023 Serial 3925
Permanent link to this record
 
 
Author Henry Velesaca; Patricia Suarez; Dario Carpio; Angel Sappa
Title Synthesized Image Datasets: Towards an Annotation-Free Instance Segmentation Strategy Type Conference Article
Year 2021 Publication 16th International Symposium on Visual Computing Abbreviated Journal
Volume 13017 Issue Pages 131–143
Keywords
Abstract This paper presents a complete pipeline to perform deep learning-based instance segmentation of different types of grains (e.g., corn, sunflower, soybeans, lentils, chickpeas, mote, and beans). The proposed approach consists of using synthesized image datasets for the training process, which are easily generated according to the category of the instance to be segmented. The synthesized imaging process allows generating a large set of well-annotated grain samples with high variability—as large and high as the user requires. Instance segmentation is performed through a popular deep learning based approach, the Mask R-CNN architecture, but any learning-based instance segmentation approach can be considered. Results obtained by the proposed pipeline show that the strategy of using synthesized image datasets for training instance segmentation helps to avoid the time-consuming image annotation stage, as well as to achieve higher intersection over union and average precision performances. Results obtained with different varieties of grains are shown, as well as comparisons with manually annotated images, showing both the simplicity of the process and the improvements in the performance.
Address Virtual; October 2021
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 ISVC
Notes MSIAU Approved no
Call Number Admin @ si @ VSC2021 Serial 3667
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Author Martin Menchon; Estefania Talavera; Jose M. Massa; Petia Radeva
Title Behavioural Pattern Discovery from Collections of Egocentric Photo-Streams Type Conference Article
Year 2020 Publication ECCV Workshops Abbreviated Journal
Volume 12538 Issue Pages 469-484
Keywords
Abstract The automatic discovery of behaviour is of high importance when aiming to assess and improve the quality of life of people. Egocentric images offer a rich and objective description of the daily life of the camera wearer. This work proposes a new method to identify a person’s patterns of behaviour from collected egocentric photo-streams. Our model characterizes time-frames based on the context (place, activities and environment objects) that define the images composition. Based on the similarity among the time-frames that describe the collected days for a user, we propose a new unsupervised greedy method to discover the behavioural pattern set based on a novel semantic clustering approach. Moreover, we present a new score metric to evaluate the performance of the proposed algorithm. We validate our method on 104 days and more than 100k images extracted from 7 users. Results show that behavioural patterns can be discovered to characterize the routine of individuals and consequently their lifestyle.
Address Virtual; August 2020
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 ECCVW
Notes MILAB; no proj Approved no
Call Number Admin @ si @ MTM2020 Serial 3528
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