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Author Victor Ponce
Title Evolutionary Bags of Space-Time Features for Human Analysis Type Book Whole
Year 2016 Publication PhD Thesis Universitat de Barcelona, UOC and CVC Abbreviated Journal
Volume Issue Pages
Keywords (down) Computer algorithms; Digital image processing; Digital video; Analysis of variance; Dynamic programming; Evolutionary computation; Gesture
Abstract The representation (or feature) learning has been an emerging concept in the last years, since it collects a set of techniques that are present in any theoretical or practical methodology referring to artificial intelligence. In computer vision, a very common representation has adopted the form of the well-known Bag of Visual Words. This representation appears implicitly in most approaches where images are described, and is also present in a huge number of areas and domains: image content retrieval, pedestrian detection, human-computer interaction, surveillance, e-health, and social computing, amongst others. The early stages of this dissertation provide an approach for learning visual representations inside evolutionary algorithms, which consists of evolving weighting schemes to improve the BoVW representations for the task of recognizing categories of videos and images. Thus, we demonstrate the applicability of the most common weighting schemes, which are often used in text mining but are less frequently found in computer vision tasks. Beyond learning these visual representations, we provide an approach based on fusion strategies for learning spatiotemporal representations, from multimodal data obtained by depth sensors. Besides, we specially aim at the evolutionary and dynamic modelling, where the temporal factor is present in the nature of the data, such as video sequences of gestures and actions. Indeed, we explore the effects of probabilistic modelling for those approaches based on dynamic programming, so as to handle the temporal deformation and variance amongst video sequences of different categories. Finally, we integrate dynamic programming and generative models into an evolutionary computation framework, with the aim of learning Bags of SubGestures (BoSG) representations and hence to improve the generalization capability of standard gesture recognition approaches. The results obtained in the experimentation demonstrate, first, that evolutionary algorithms are useful for improving the representation of BoVW approaches in several datasets for recognizing categories in still images and video sequences. On the other hand, our experimentation reveals that both, the use of dynamic programming and generative models to align video sequences, and the representations obtained from applying fusion strategies in multimodal data, entail an enhancement on the performance when recognizing some gesture categories. Furthermore, the combination of evolutionary algorithms with models based on dynamic programming and generative approaches results, when aiming at the classification of video categories on large video datasets, in a considerable improvement over standard gesture and action recognition approaches. Finally, we demonstrate the applications of these representations in several domains for human analysis: classification of images where humans may be present, action and gesture recognition for general applications, and in particular for conversational settings within the field of restorative justice
Address June 2016
Corporate Author Thesis Ph.D. thesis
Publisher Ediciones Graficas Rey Place of Publication Editor Sergio Escalera;Xavier Baro;Hugo Jair Escalante
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 Approved no
Call Number Pon2016 Serial 2814
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Author Frederic Sampedro; Sergio Escalera; Anna Domenech; Ignasi Carrio
Title A computational framework for cancer response assessment based on oncological PET-CT scans Type Journal Article
Year 2014 Publication Computers in Biology and Medicine Abbreviated Journal CBM
Volume 55 Issue Pages 92–99
Keywords (down) Computer aided diagnosis; Nuclear medicine; Machine learning; Image processing; Quantitative analysis
Abstract In this work we present a comprehensive computational framework to help in the clinical assessment of cancer response from a pair of time consecutive oncological PET-CT scans. In this scenario, the design and implementation of a supervised machine learning system to predict and quantify cancer progression or response conditions by introducing a novel feature set that models the underlying clinical context is described. Performance results in 100 clinical cases (corresponding to 200 whole body PET-CT scans) in comparing expert-based visual analysis and classifier decision making show up to 70% accuracy within a completely automatic pipeline and 90% accuracy when providing the system with expert-guided PET tumor segmentation masks.
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 HuPBA;MILAB Approved no
Call Number Admin @ si @ SED2014 Serial 2606
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Author Angel Sappa (ed)
Title ICT Applications for Smart Cities Type Book Whole
Year 2022 Publication ICT Applications for Smart Cities Abbreviated Journal
Volume 224 Issue Pages
Keywords (down) Computational Intelligence; Intelligent Systems; Smart Cities; ICT Applications; Machine Learning; Pattern Recognition; Computer Vision; Image Processing
Abstract Part of the book series: Intelligent Systems Reference Library (ISRL)

This book is the result of four-year work in the framework of the Ibero-American Research Network TICs4CI funded by the CYTED program. In the following decades, 85% of the world's population is expected to live in cities; hence, urban centers should be prepared to provide smart solutions for problems ranging from video surveillance and intelligent mobility to the solid waste recycling processes, just to mention a few. More specifically, the book describes underlying technologies and practical implementations of several successful case studies of ICTs developed in the following smart city areas:

• Urban environment monitoring
• Intelligent mobility
• Waste recycling processes
• Video surveillance
• Computer-aided diagnose in healthcare systems
• Computer vision-based approaches for efficiency in production processes

The book is intended for researchers and engineers in the field of ICTs for smart cities, as well as to anyone who wants to know about state-of-the-art approaches and challenges on this field.
Address September 2022
Corporate Author Thesis
Publisher Springer Place of Publication Editor Angel Sappa
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title ISRL
Series Volume Series Issue Edition
ISSN ISBN 978-3-031-06306-0 Medium
Area Expedition Conference
Notes MSIAU; MACO Approved no
Call Number Admin @ si @ Sap2022 Serial 3812
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Author Mariano Vazquez; Ruth Aris; Guillaume Hozeaux; R.Aubry; P.Villar;Jaume Garcia ; Debora Gil; Francesc Carreras
Title A massively parallel computational electrophysiology model of the heart Type Journal Article
Year 2011 Publication International Journal for Numerical Methods in Biomedical Engineering Abbreviated Journal IJNMBE
Volume 27 Issue Pages 1911-1929
Keywords (down) computational electrophysiology; parallelization; finite element methods
Abstract This paper presents a patient-sensitive simulation strategy capable of using the most efficient way the high-performance computational resources. The proposed strategy directly involves three different players: Computational Mechanics Scientists (CMS), Image Processing Scientists and Cardiologists, each one mastering its own expertise area within the project. This paper describes the general integrative scheme but focusing on the CMS side presents a massively parallel implementation of computational electrophysiology applied to cardiac tissue simulation. The paper covers different angles of the computational problem: equations, numerical issues, the algorithm and parallel implementation. The proposed methodology is illustrated with numerical simulations testing all the different possibilities, ranging from small domains up to very large ones. A key issue is the almost ideal scalability not only for large and complex problems but also for medium-size meshes. The explicit formulation is particularly well suited for solving this highly transient problems, with very short time-scale.
Address Swansea (UK)
Corporate Author John Wiley & Sons, Ltd. Thesis
Publisher John Wiley & Sons, Ltd. 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 IAM Approved no
Call Number IAM @ iam @ VAH2011 Serial 1198
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Author Arjan Gijsenij; Theo Gevers; Joost Van de Weijer
Title Computational Color Constancy: Survey and Experiments Type Journal Article
Year 2011 Publication IEEE Transactions on Image Processing Abbreviated Journal TIP
Volume 20 Issue 9 Pages 2475-2489
Keywords (down) computational color constancy;computer vision application;gamut-based method;learning-based method;static method;colour vision;computer vision;image colour analysis;learning (artificial intelligence);lighting
Abstract Computational color constancy is a fundamental prerequisite for many computer vision applications. This paper presents a survey of many recent developments and state-of-the- art methods. Several criteria are proposed that are used to assess the approaches. A taxonomy of existing algorithms is proposed and methods are separated in three groups: static methods, gamut-based methods and learning-based methods. Further, the experimental setup is discussed including an overview of publicly available data sets. Finally, various freely available methods, of which some are considered to be state-of-the-art, are evaluated on two data sets.
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 1057-7149 ISBN Medium
Area Expedition Conference
Notes ISE;CIC Approved no
Call Number Admin @ si @ GGW2011 Serial 1717
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Author Dorota Kaminska; Kadir Aktas; Davit Rizhinashvili; Danila Kuklyanov; Abdallah Hussein Sham; Sergio Escalera; Kamal Nasrollahi; Thomas B. Moeslund; Gholamreza Anbarjafari
Title Two-stage Recognition and Beyond for Compound Facial Emotion Recognition Type Journal Article
Year 2021 Publication Electronics Abbreviated Journal ELEC
Volume 10 Issue 22 Pages 2847
Keywords (down) compound emotion recognition; facial expression recognition; dominant and complementary emotion recognition; deep learning
Abstract Facial emotion recognition is an inherently complex problem due to individual diversity in facial features and racial and cultural differences. Moreover, facial expressions typically reflect the mixture of people’s emotional statuses, which can be expressed using compound emotions. Compound facial emotion recognition makes the problem even more difficult because the discrimination between dominant and complementary emotions is usually weak. We have created a database that includes 31,250 facial images with different emotions of 115 subjects whose gender distribution is almost uniform to address compound emotion recognition. In addition, we have organized a competition based on the proposed dataset, held at FG workshop 2020. This paper analyzes the winner’s approach—a two-stage recognition method (1st stage, coarse recognition; 2nd stage, fine recognition), which enhances the classification of symmetrical emotion labels.
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 HUPBA; no proj Approved no
Call Number Admin @ si @ KAR2021 Serial 3642
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Author V.C.Kieu; Alicia Fornes; M. Visani; N.Journet ; Anjan Dutta
Title The ICDAR/GREC 2013 Music Scores Competition on Staff Removal Type Conference Article
Year 2013 Publication 10th IAPR International Workshop on Graphics Recognition Abbreviated Journal
Volume Issue Pages
Keywords (down) Competition; Music scores; Staff Removal
Abstract The first competition on music scores that was organized at ICDAR and GREC in 2011 awoke the interest of researchers, who participated both at staff removal and writer identification tasks. In this second edition, we propose a staff removal competition where we simulate old music scores. Thus, we have created a new set of images, which contain noise and 3D distortions. This paper describes the distortion methods, metrics, the participant’s methods and the obtained results.
Address Bethlehem; PA; USA; August 2013
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; 600.045; 600.061 Approved no
Call Number Admin @ si @ KFV2013 Serial 2337
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Author Alicia Fornes; V.C.Kieu; M. Visani; N.Journet; Anjan Dutta
Title The ICDAR/GREC 2013 Music Scores Competition: Staff Removal Type Book Chapter
Year 2014 Publication Graphics Recognition. Current Trends and Challenges Abbreviated Journal
Volume 8746 Issue Pages 207-220
Keywords (down) Competition; Graphics recognition; Music scores; Writer identification; Staff removal
Abstract The first competition on music scores that was organized at ICDAR and GREC in 2011 awoke the interest of researchers, who participated in both staff removal and writer identification tasks. In this second edition, we focus on the staff removal task and simulate a real case scenario concerning old and degraded music scores. For this purpose, we have generated a new set of semi-synthetic images using two degradation models that we previously introduced: local noise and 3D distortions. In this extended paper we provide an extended description of the dataset, degradation models, evaluation metrics, the participant’s methods and the obtained results that could not be presented at ICDAR and GREC proceedings due to page limitations.
Address
Corporate Author Thesis
Publisher Springer Berlin Heidelberg Place of Publication Editor B.Lamiroy; J.-M. Ogier
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN 0302-9743 ISBN 978-3-662-44853-3 Medium
Area Expedition Conference
Notes DAG; 600.077; 600.061 Approved no
Call Number Admin @ si @ FKV2014 Serial 2581
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Author Maria Vanrell; Naila Murray; Robert Benavente; C. Alejandro Parraga; Xavier Otazu; Ramon Baldrich
Title Perception Based Representations for Computational Colour Type Conference Article
Year 2011 Publication 3rd International Workshop on Computational Color Imaging Abbreviated Journal
Volume 6626 Issue Pages 16-30
Keywords (down) colour perception, induction, naming, psychophysical data, saliency, segmentation
Abstract The perceived colour of a stimulus is dependent on multiple factors stemming out either from the context of the stimulus or idiosyncrasies of the observer. The complexity involved in combining these multiple effects is the main reason for the gap between classical calibrated colour spaces from colour science and colour representations used in computer vision, where colour is just one more visual cue immersed in a digital image where surfaces, shadows and illuminants interact seemingly out of control. With the aim to advance a few steps towards bridging this gap we present some results on computational representations of colour for computer vision. They have been developed by introducing perceptual considerations derived from the interaction of the colour of a point with its context. We show some techniques to represent the colour of a point influenced by assimilation and contrast effects due to the image surround and we show some results on how colour saliency can be derived in real images. We outline a model for automatic assignment of colour names to image points directly trained on psychophysical data. We show how colour segments can be perceptually grouped in the image by imposing shading coherence in the colour space.
Address Milan, Italy
Corporate Author Thesis
Publisher Springer-Verlag Place of Publication Editor Raimondo Schettini, Shoji Tominaga, Alain Trémeau
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN ISBN 978-3-642-20403-6 Medium
Area Expedition Conference CCIW
Notes CIC Approved no
Call Number Admin @ si @ VMB2011 Serial 1733
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Author Marcel P. Lucassen; Theo Gevers; Arjan Gijsenij
Title Texture Affects Color Emotion Type Journal Article
Year 2011 Publication Color Research & Applications Abbreviated Journal CRA
Volume 36 Issue 6 Pages 426–436
Keywords (down) color;texture;color emotion;observer variability;ranking
Abstract Several studies have recorded color emotions in subjects viewing uniform color (UC) samples. We conduct an experiment to measure and model how these color emotions change when texture is added to the color samples. Using a computer monitor, our subjects arrange samples along four scales: warm–cool, masculine–feminine, hard–soft, and heavy–light. Three sample types of increasing visual complexity are used: UC, grayscale textures, and color textures (CTs). To assess the intraobserver variability, the experiment is repeated after 1 week. Our results show that texture fully determines the responses on the Hard-Soft scale, and plays a role of decreasing weight for the masculine–feminine, heavy–light, and warm–cool scales. Using some 25,000 observer responses, we derive color emotion functions that predict the group-averaged scale responses from the samples' color and texture parameters. For UC samples, the accuracy of our functions is significantly higher (average R2 = 0.88) than that of previously reported functions applied to our data. The functions derived for CT samples have an accuracy of R2 = 0.80. We conclude that when textured samples are used in color emotion studies, the psychological responses may be strongly affected by texture. © 2010 Wiley Periodicals, Inc. Col Res Appl, 2010
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 ALTRES;ISE Approved no
Call Number Admin @ si @ LGG2011 Serial 1844
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Author Fahad Shahbaz Khan; Joost Van de Weijer; Sadiq Ali; Michael Felsberg
Title Evaluating the impact of color on texture recognition Type Conference Article
Year 2013 Publication 15th International Conference on Computer Analysis of Images and Patterns Abbreviated Journal
Volume 8047 Issue Pages 154-162
Keywords (down) Color; Texture; image representation
Abstract State-of-the-art texture descriptors typically operate on grey scale images while ignoring color information. A common way to obtain a joint color-texture representation is to combine the two visual cues at the pixel level. However, such an approach provides sub-optimal results for texture categorisation task.
In this paper we investigate how to optimally exploit color information for texture recognition. We evaluate a variety of color descriptors, popular in image classification, for texture categorisation. In addition we analyze different fusion approaches to combine color and texture cues. Experiments are conducted on the challenging scenes and 10 class texture datasets. Our experiments clearly suggest that in all cases color names provide the best performance. Late fusion is the best strategy to combine color and texture. By selecting the best color descriptor with optimal fusion strategy provides a gain of 5% to 8% compared to texture alone on scenes and texture datasets.
Address York; UK; August 2013
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 978-3-642-40260-9 Medium
Area Expedition Conference CAIP
Notes CIC; 600.048 Approved no
Call Number Admin @ si @ KWA2013 Serial 2263
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Author Xavier Soria; Angel Sappa; Arash Akbarinia
Title Multispectral Single-Sensor RGB-NIR Imaging: New Challenges and Opportunities Type Conference Article
Year 2017 Publication 7th International Conference on Image Processing Theory, Tools & Applications Abbreviated Journal
Volume Issue Pages
Keywords (down) Color restoration; Neural networks; Singlesensor cameras; Multispectral images; RGB-NIR dataset
Abstract Multispectral images captured with a single sensor camera have become an attractive alternative for numerous computer vision applications. However, in order to fully exploit their potentials, the color restoration problem (RGB representation) should be addressed. This problem is more evident in outdoor scenarios containing vegetation, living beings, or specular materials. The problem of color distortion emerges from the sensitivity of sensors due to the overlap of visible and near infrared spectral bands. This paper empirically evaluates the variability of the near infrared (NIR) information with respect to the changes of light throughout the day. A tiny neural network is proposed to restore the RGB color representation from the given RGBN (Red, Green, Blue, NIR) images. In order to evaluate the proposed algorithm, different experiments on a RGBN outdoor dataset are conducted, which include various challenging cases. The obtained result shows the challenge and the importance of addressing color restoration in single sensor multispectral images.
Address Montreal; Canada; November 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 IPTA
Notes NEUROBIT; MSIAU; 600.122 Approved no
Call Number Admin @ si @ SSA2017 Serial 3074
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Author Lu Yu; Lichao Zhang; Joost Van de Weijer; Fahad Shahbaz Khan; Yongmei Cheng; C. Alejandro Parraga
Title Beyond Eleven Color Names for Image Understanding Type Journal Article
Year 2018 Publication Machine Vision and Applications Abbreviated Journal MVAP
Volume 29 Issue 2 Pages 361-373
Keywords (down) Color name; Discriminative descriptors; Image classification; Re-identification; Tracking
Abstract Color description is one of the fundamental problems of image understanding. One of the popular ways to represent colors is by means of color names. Most existing work on color names focuses on only the eleven basic color terms of the English language. This could be limiting the discriminative power of these representations, and representations based on more color names are expected to perform better. However, there exists no clear strategy to choose additional color names. We collect a dataset of 28 additional color names. To ensure that the resulting color representation has high discriminative power we propose a method to order the additional color names according to their complementary nature with the basic color names. This allows us to compute color name representations with high discriminative power of arbitrary length. In the experiments we show that these new color name descriptors outperform the existing color name descriptor on the task of visual tracking, person re-identification and image classification.
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 LAMP; NEUROBIT; 600.068; 600.109; 600.120 Approved no
Call Number Admin @ si @ YYW2018 Serial 3087
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Author Joost Van de Weijer; Fahad Shahbaz Khan
Title An Overview of Color Name Applications in Computer Vision Type Conference Article
Year 2015 Publication Computational Color Imaging Workshop Abbreviated Journal
Volume Issue Pages
Keywords (down) color features; color names; object recognition
Abstract In this article we provide an overview of color name applications in computer vision. Color names are linguistic labels which humans use to communicate color. Computational color naming learns a mapping from pixels values to color names. In recent years color names have been applied to a wide variety of computer vision applications, including image classification, object recognition, texture classification, visual tracking and action recognition. Here we provide an overview of these results which show that in general color names outperform photometric invariants as a color representation.
Address Saint Etienne; France; March 2015
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference CCIW
Notes LAMP; 600.079; 600.068 Approved no
Call Number Admin @ si @ WeK2015 Serial 2586
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Author Miguel Oliveira; Angel Sappa; Victor Santos
Title A probabilistic approach for color correction in image mosaicking applications Type Journal Article
Year 2015 Publication IEEE Transactions on Image Processing Abbreviated Journal TIP
Volume 14 Issue 2 Pages 508 - 523
Keywords (down) Color correction; image mosaicking; color transfer; color palette mapping functions
Abstract Image mosaicking applications require both geometrical and photometrical registrations between the images that compose the mosaic. This paper proposes a probabilistic color correction algorithm for correcting the photometrical disparities. First, the image to be color corrected is segmented into several regions using mean shift. Then, connected regions are extracted using a region fusion algorithm. Local joint image histograms of each region are modeled as collections of truncated Gaussians using a maximum likelihood estimation procedure. Then, local color palette mapping functions are computed using these sets of Gaussians. The color correction is performed by applying those functions to all the regions of the image. An extensive comparison with ten other state of the art color correction algorithms is presented, using two different image pair data sets. Results show that the proposed approach obtains the best average scores in both data sets and evaluation metrics and is also the most robust to failures.
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 1057-7149 ISBN Medium
Area Expedition Conference
Notes ADAS; 600.076 Approved no
Call Number Admin @ si @ OSS2015b Serial 2554
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