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Author David Masip; Agata Lapedriza; Jordi Vitria
Title Measuring External Face Appearance for Face Classification. Type Book Chapter
Year 2007 Publication (up) Face Recognition, Ed. Kresimir Delac and Mislav Grgic, pp. 287–307, ISBN 978–3–902613–03–5, I–Tech Education and Publishing Abbreviated Journal
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Abstract
Address Vienna (Austria)
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 @ MLV2007b Serial 940
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Author Fadi Dornaika; Bogdan Raducanu
Title Analysis and Recognition of Facial Expressions in Videos Using Facial Shape Deformation Type Book Chapter
Year 2012 Publication (up) Facial Expressions: Dynamic Patterns, Impairments and Social Perceptions Abbreviated Journal
Volume Issue Pages 157-178
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Address
Corporate Author Thesis
Publisher NOVA Publishers Place of Publication Editor S.E. Carter
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 Admin @ si @ DoR2012 Serial 2183
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Author Estefania Talavera; Alexandre Cola; Nicolai Petkov; Petia Radeva
Title Towards Egocentric Person Re-identification and Social Pattern Analysis. Type Book Chapter
Year 2019 Publication (up) Frontiers in Artificial Intelligence and Applications Abbreviated Journal
Volume 310 Issue Pages 203 - 211
Keywords
Abstract CoRR abs/1905.04073
Wearable cameras capture a first-person view of the daily activities of the camera wearer, offering a visual diary of the user behaviour. Detection of the appearance of people the camera user interacts with for social interactions analysis is of high interest. Generally speaking, social events, lifestyle and health are highly correlated, but there is a lack of tools to monitor and analyse them. We consider that egocentric vision provides a tool to obtain information and understand users social interactions. We propose a model that enables us to evaluate and visualize social traits obtained by analysing social interactions appearance within egocentric photostreams. Given sets of egocentric images, we detect the appearance of faces within the days of the camera wearer, and rely on clustering algorithms to group their feature descriptors in order to re-identify persons. Recurrence of detected faces within photostreams allows us to shape an idea of the social pattern of behaviour of the user. We validated our model over several weeks recorded by different camera wearers. Our findings indicate that social profiles are potentially useful for social behaviour interpretation.
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Publisher Place of Publication Editor
Language Summary Language Original Title
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ISSN ISBN Medium
Area Expedition Conference
Notes MILAB; no proj Approved no
Call Number Admin @ si @ TCP2019 Serial 3377
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Author Xavier Baro; Jordi Vitria
Title Feature Selection with Non-Parametric Mutual Information for Adaboost Learning Type Book Chapter
Year 2005 Publication (up) Frontiers in Artificial Intelligence and Applications / Artificial intelligence Research and Development, 131:131–138, Eds: B. Lopez, J. Melendez, P. Radeva, J. Vitria, IOS Press, ISBN: 1–58603–560–6 Abbreviated Journal
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Address
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Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
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ISSN ISBN Medium
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Notes OR;HuPBA;MV Approved no
Call Number BCNPCL @ bcnpcl @ BaV2005b Serial 583
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Author Jordi Vitria; Petia Radeva; I. Aguilo
Title Recent Advances in Artificial Intelligence Research and Development Type Book Chapter
Year 2004 Publication (up) Frontiers in Artificial Intelligence and Applications, 113, J. Vitria, P. Radeva, I. Aguilo (Eds.), ISBN: 1–58603–466–9 Abbreviated Journal
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Abstract
Address Amsterdam
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;MILAB;MV Approved no
Call Number BCNPCL @ bcnpcl @ VRA2004 Serial 509
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Author Susana Alvarez; Xavier Otazu; Maria Vanrell
Title Image Segmentation Based on Inter-Feature Distance Maps Type Book Chapter
Year 2005 Publication (up) Frontiers in Artificial Intelligence and Applications, IOS Press, 131: 75–82 Abbreviated Journal
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Abstract
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
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Notes CIC Approved no
Call Number CAT @ cat @ AOV2005 Serial 569
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Author Misael Rosales; Petia Radeva; Oriol Rodriguez; Debora Gil
Title Suppression of IVUS Image Rotation. A Kinematic Approach Type Book Chapter
Year 2005 Publication (up) Functional Imaging and Modeling of the Heart Abbreviated Journal LNCS
Volume 3504 Issue Pages 889-892
Keywords
Abstract IntraVascular Ultrasound (IVUS) is an exploratory technique used in interventional procedures that shows cross section images of arteries and provides qualitative information about the causes and severity of the arterial lumen narrowing. Cross section analysis as well as visualization of plaque extension in a vessel segment during the catheter imaging pullback are the technique main advantages. However, IVUS sequence exhibits a periodic rotation artifact that makes difficult the longitudinal lesion inspection and hinders any segmentation algorithm. In this paper we propose a new kinematic method to estimate and remove the image rotation of IVUS images sequences. Results on several IVUS sequences show good results and prompt some of the clinical applications to vessel dynamics study, and relation to vessel pathology.
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Corporate Author Thesis
Publisher Springer Berlin / Heidelberg Place of Publication Editor Frangi, Alejandro and Radeva, Petia and Santos, Andres and Hernandez, Monica
Language Summary Language Original Title
Series Editor Series Title Lecture Notes in Computer Science Abbreviated Series Title LNCS
Series Volume 3504 Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes IAM;MILAB Approved no
Call Number IAM @ iam @ RRR2005 Serial 1645
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Author David Roche; Debora Gil; Jesus Giraldo
Title Mathematical modeling of G protein-coupled receptor function: What can we learn from empirical and mechanistic models? Type Book Chapter
Year 2014 Publication (up) G Protein-Coupled Receptors – Modeling and Simulation Advances in Experimental Medicine and Biology Abbreviated Journal
Volume 796 Issue 3 Pages 159-181
Keywords β-arrestin; biased agonism; curve fitting; empirical modeling; evolutionary algorithm; functional selectivity; G protein; GPCR; Hill coefficient; intrinsic efficacy; inverse agonism; mathematical modeling; mechanistic modeling; operational model; parameter optimization; receptor dimer; receptor oligomerization; receptor constitutive activity; signal transduction; two-state model
Abstract Empirical and mechanistic models differ in their approaches to the analysis of pharmacological effect. Whereas the parameters of the former are not physical constants those of the latter embody the nature, often complex, of biology. Empirical models are exclusively used for curve fitting, merely to characterize the shape of the E/[A] curves. Mechanistic models, on the contrary, enable the examination of mechanistic hypotheses by parameter simulation. Regretfully, the many parameters that mechanistic models may include can represent a great difficulty for curve fitting, representing, thus, a challenge for computational method development. In the present study some empirical and mechanistic models are shown and the connections, which may appear in a number of cases between them, are analyzed from the curves they yield. It may be concluded that systematic and careful curve shape analysis can be extremely useful for the understanding of receptor function, ligand classification and drug discovery, thus providing a common language for the communication between pharmacologists and medicinal chemists.
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Publisher Springer Netherlands Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0065-2598 ISBN 978-94-007-7422-3 Medium
Area Expedition Conference
Notes IAM; 600.075 Approved no
Call Number IAM @ iam @ RGG2014 Serial 2197
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Author Patricia Suarez; Angel Sappa; Boris X. Vintimilla
Title Deep learning-based vegetation index estimation Type Book Chapter
Year 2021 Publication (up) Generative Adversarial Networks for Image-to-Image Translation Abbreviated Journal
Volume Issue Pages 205-234
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Abstract Chapter 9
Address
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Publisher Elsevier Place of Publication Editor A.Solanki; A.Nayyar; M.Naved
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
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ISSN ISBN Medium
Area Expedition Conference
Notes MSIAU; 600.122 Approved no
Call Number Admin @ si @ SSV2021a Serial 3578
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Author Maryam Asadi-Aghbolaghi; Albert Clapes; Marco Bellantonio; Hugo Jair Escalante; Victor Ponce; Xavier Baro; Isabelle Guyon; Shohreh Kasaei; Sergio Escalera
Title Deep Learning for Action and Gesture Recognition in Image Sequences: A Survey Type Book Chapter
Year 2017 Publication (up) Gesture Recognition Abbreviated Journal
Volume Issue Pages 539-578
Keywords Action recognition; Gesture recognition; Deep learning architectures; Fusion strategies
Abstract Interest in automatic action and gesture recognition has grown considerably in the last few years. This is due in part to the large number of application domains for this type of technology. As in many other computer vision areas, deep learning based methods have quickly become a reference methodology for obtaining state-of-the-art performance in both tasks. This chapter is a survey of current deep learning based methodologies for action and gesture recognition in sequences of images. The survey reviews both fundamental and cutting edge methodologies reported in the last few years. We introduce a taxonomy that summarizes important aspects of deep learning for approaching both tasks. Details of the proposed architectures, fusion strategies, main datasets, and competitions are reviewed. Also, we summarize and discuss the main works proposed so far with particular interest on how they treat the temporal dimension of data, their highlighting features, and opportunities and challenges for future research. To the best of our knowledge this is the first survey in the topic. We foresee this survey will become a reference in this ever dynamic field of research.
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Publisher Place of Publication Editor
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Notes HUPBA; no proj Approved no
Call Number Admin @ si @ ACB2017a Serial 2981
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Author Muhammad Muzzamil Luqman; Jean-Yves Ramel; Josep Llados
Title Multilevel Analysis of Attributed Graphs for Explicit Graph Embedding in Vector Spaces Type Book Chapter
Year 2013 Publication (up) Graph Embedding for Pattern Analysis Abbreviated Journal
Volume Issue Pages 1-26
Keywords
Abstract Ability to recognize patterns is among the most crucial capabilities of human beings for their survival, which enables them to employ their sophisticated neural and cognitive systems [1], for processing complex audio, visual, smell, touch, and taste signals. Man is the most complex and the best existing system of pattern recognition. Without any explicit thinking, we continuously compare, classify, and identify huge amount of signal data everyday [2], starting from the time we get up in the morning till the last second we fall asleep. This includes recognizing the face of a friend in a crowd, a spoken word embedded in noise, the proper key to lock the door, smell of coffee, the voice of a favorite singer, the recognition of alphabetic characters, and millions of more tasks that we perform on regular basis.
Address
Corporate Author Thesis
Publisher Springer New York Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN 978-1-4614-4456-5 Medium
Area Expedition Conference
Notes DAG Approved no
Call Number Admin @ si @ LRL2013b Serial 2271
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Author Miquel Ferrer; I. Bardaji; Ernest Valveny; Dimosthenis Karatzas; Horst Bunke
Title Median Graph Computation by Means of Graph Embedding into Vector Spaces Type Book Chapter
Year 2013 Publication (up) Graph Embedding for Pattern Analysis Abbreviated Journal
Volume Issue Pages 45-72
Keywords
Abstract In pattern recognition [8, 14], a key issue to be addressed when designing a system is how to represent input patterns. Feature vectors is a common option. That is, a set of numerical features describing relevant properties of the pattern are computed and arranged in a vector form. The main advantages of this kind of representation are computational simplicity and a well sound mathematical foundation. Thus, a large number of operations are available to work with vectors and a large repository of algorithms for pattern analysis and classification exist. However, the simple structure of feature vectors might not be the best option for complex patterns where nonnumerical features or relations between different parts of the pattern become relevant.
Address
Corporate Author Thesis
Publisher Springer New York Place of Publication Editor Yun Fu; Yungian Ma
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN 978-1-4614-4456-5 Medium
Area Expedition Conference
Notes DAG Approved no
Call Number Admin @ si @ FBV2013 Serial 2421
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Author Josep Llados; Ernest Valveny; Gemma Sanchez; Enric Marti
Title Symbol recognition: current advances and perspectives Type Book Chapter
Year 2002 Publication (up) Graphics Recognition Algorithms And Applications Abbreviated Journal LNCS
Volume 2390 Issue Pages 104-128
Keywords
Abstract The recognition of symbols in graphic documents is an intensive research activity in the community of pattern recognition and document analysis. A key issue in the interpretation of maps, engineering drawings, diagrams, etc. is the recognition of domain dependent symbols according to a symbol database. In this work we first review the most outstanding symbol recognition methods from two different points of view: application domains and pattern recognition methods. In the second part of the paper, open and unaddressed problems involved in symbol recognition are described, analyzing their current state of art and discussing future research challenges. Thus, issues such as symbol representation, matching, segmentation, learning, scalability of recognition methods and performance evaluation are addressed in this work. Finally, we discuss the perspectives of symbol recognition concerning to new paradigms such as user interfaces in handheld computers or document database and WWW indexing by graphical content.
Address London, UK
Corporate Author Thesis
Publisher Springer-Verlag Place of Publication Editor Dorothea Blostein and Young- Bin Kwon
Language Summary Language Original Title
Series Editor Series Title Lecture Notes in Computer Science Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN ISBN 3-540-44066-6 Medium
Area Expedition Conference GREC
Notes DAG; IAM; Approved no
Call Number IAM @ iam @ LVS2002 Serial 1572
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Author Josep Llados; Gemma Sanchez; Enric Marti
Title A string based method to recognize symbols and structural textures in architectural plans Type Book Chapter
Year 1998 Publication (up) Graphics Recognition Algorithms and Systems Second International Workshop, GREC' 97 Nancy, France, August 22–23, 1997 Selected Papers Abbreviated Journal LNCS
Volume 1389 Issue 1998 Pages 91-103
Keywords
Abstract This paper deals with the recognition of symbols and structural textures in architectural plans using string matching techniques. A plan is represented by an attributed graph whose nodes represent characteristic points and whose edges represent segments. Symbols and textures can be seen as a set of regions, i.e. closed loops in the graph, with a particular arrangement. The search for a symbol involves a graph matching between the regions of a model graph and the regions of the graph representing the document. Discriminating a texture means a clustering of neighbouring regions of this graph. Both procedures involve a similarity measure between graph regions. A string codification is used to represent the sequence of outlining edges of a region. Thus, the similarity between two regions is defined in terms of the string edit distance between their boundary strings. The use of string matching allows the recognition method to work also under presence of distortion.
Address
Corporate Author Thesis
Publisher Springer Link Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title LNCS Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes DAG; IAM Approved no
Call Number IAM @ iam @ SLE1998 Serial 1573
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Author Salim Jouili; Salvatore Tabbone; Ernest Valveny
Title Comparing Graph Similarity Measures for Graphical Recognition Type Book Chapter
Year 2010 Publication (up) Graphics Recognition. Achievements, Challenges, and Evolution. 8th International Workshop, GREC 2009. Selected Papers Abbreviated Journal
Volume 6020 Issue Pages 37-48
Keywords
Abstract In this paper we evaluate four graph distance measures. The analysis is performed for document retrieval tasks. For this aim, different kind of documents are used including line drawings (symbols), ancient documents (ornamental letters), shapes and trademark-logos. The experimental results show that the performance of each graph distance measure depends on the kind of data and the graph representation technique.
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 978-3-642-13727-3 Medium
Area Expedition Conference GREC
Notes DAG Approved no
Call Number Admin @ si @ JTV2010 Serial 2404
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