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Author | Partha Pratim Roy; Eduard Vazquez; Josep Llados; Ramon Baldrich; Umapada Pal | ||||
Title | A System to Segment Text and Symbols from Color Maps | Type | Book Chapter | ||
Year | 2008 | Publication | Graphics Recognition. Recent Advances and New Opportunities | Abbreviated Journal | |
Volume | 5046 | Issue | Pages | 245-256 | |
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Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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Notes | DAG;CIC | Approved | no | ||
Call Number | CAT @ cat @ RVL2008 | Serial | 1005 | ||
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Author | Josep Llados; Jaime Lopez-Krahe; Enric Marti | ||||
Title | A system to understand hand-drawn floor plans using subgraph isomorphism and Hough transform | Type | Book Chapter | ||
Year | 1997 | Publication | Machine Vision and Applications | Abbreviated Journal | |
Volume | 10 | Issue | 3 | Pages | 150-158 |
Keywords | Line drawings – Hough transform – Graph matching – CAD systems – Graphics recognition | ||||
Abstract | Presently, man-machine interface development is a widespread research activity. A system to understand hand drawn architectural drawings in a CAD environment is presented in this paper. To understand a document, we have to identify its building elements and their structural properties. An attributed graph structure is chosen as a symbolic representation of the input document and the patterns to recognize in it. An inexact subgraph isomorphism procedure using relaxation labeling techniques is performed. In this paper we focus on how to speed up the matching. There is a building element, the walls, characterized by a hatching pattern. Using a straight line Hough transform (SLHT)-based method, we recognize this pattern, characterized by parallel straight lines, and remove from the input graph the edges belonging to this pattern. The isomorphism is then applied to the remainder of the input graph. When all the building elements have been recognized, the document is redrawn, correcting the inaccurate strokes obtained from a hand-drawn input. | ||||
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Notes | DAG;IAM | Approved | no | ||
Call Number | IAM @ iam @ LLM1997a | Serial | 1566 | ||
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Author | Josep Llados; Ernest Valveny; Enric Marti | ||||
Title | Symbol Recognition in Document Image Analysis: Methods and Challenges | Type | Book Chapter | ||
Year | 2000 | Publication | Recent Research Developments in Pattern Recognition, Transworld Research Network, | Abbreviated Journal | |
Volume | 1 | Issue | Pages | 151–178. | |
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ISSN | ISBN | 81-86846-61-1 | Medium | ||
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Notes | DAG;IAM | Approved | no | ||
Call Number | IAM @ iam @ LVM2000 | Serial | 1575 | ||
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Author | Jun Wan; Guodong Guo; Sergio Escalera; Hugo Jair Escalante; Stan Z Li | ||||
Title | Face Presentation Attack Detection (PAD) Challenges | Type | Book Chapter | ||
Year | 2023 | Publication | Advances in Face Presentation Attack Detection | Abbreviated Journal | |
Volume | Issue | Pages | 17–35 | ||
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Abstract | In recent years, the security of face recognition systems has been increasingly threatened. Face Anti-spoofing (FAS) is essential to secure face recognition systems primarily from various attacks. In order to attract researchers and push forward the state of the art in Face Presentation Attack Detection (PAD), we organized three editions of Face Anti-spoofing Workshop and Competition at CVPR 2019, CVPR 2020, and ICCV 2021, which have attracted more than 800 teams from academia and industry, and greatly promoted the algorithms to overcome many challenging problems. In this chapter, we introduce the detailed competition process, including the challenge phases, timeline and evaluation metrics. Along with the workshop, we will introduce the corresponding dataset for each competition including data acquisition details, data processing, statistics, and evaluation protocol. Finally, we provide the available link to download the datasets used in the challenges. | ||||
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Series Editor | Series Title | Abbreviated Series Title | SLCV | ||
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Notes | HUPBA | Approved | no | ||
Call Number | Admin @ si @ WGE2023b | Serial | 3956 | ||
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Author | Jun Wan; Guodong Guo; Sergio Escalera; Hugo Jair Escalante; Stan Z Li | ||||
Title | Best Solutions Proposed in the Context of the Face Anti-spoofing Challenge Series | Type | Book Chapter | ||
Year | 2023 | Publication | Advances in Face Presentation Attack Detection | Abbreviated Journal | |
Volume | Issue | Pages | 37–78 | ||
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Abstract | The PAD competitions we organized attracted more than 835 teams from home and abroad, most of them from the industry, which shows that the topic of face anti-spoofing is closely related to daily life, and there is an urgent need for advanced algorithms to solve its application needs. Specifically, the Chalearn LAP multi-modal face anti-spoofing attack detection challenge attracted more than 300 teams for the development phase with a total of 13 teams qualifying for the final round; the Chalearn Face Anti-spoofing Attack Detection Challenge attracted 340 teams in the development stage, and finally, 11 and 8 teams have submitted their codes in the single-modal and multi-modal face anti-spoofing recognition challenges, respectively; the 3D High-Fidelity Mask Face Presentation Attack Detection Challenge attracted 195 teams for the development phase with a total of 18 teams qualifying for the final round. All the results were verified and re-run by the organizing team, and the results were used for the final ranking. In this chapter, we briefly the methods developed by the teams participating in each competition, and introduce the algorithm details of the top-three ranked teams in detail. | ||||
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Notes | HUPBA | Approved | no | ||
Call Number | Admin @ si @ WGE2023d | Serial | 3958 | ||
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Author | Jun Wan; Guodong Guo; Sergio Escalera; Hugo Jair Escalante; Stan Z Li | ||||
Title | Face Anti-spoofing Progress Driven by Academic Challenges | Type | Book Chapter | ||
Year | 2023 | Publication | Advances in Face Presentation Attack Detection | Abbreviated Journal | |
Volume | Issue | Pages | 1–15 | ||
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Abstract | With the ubiquity of facial authentication systems and the prevalence of security cameras around the world, the impact that facial presentation attack techniques may have is huge. However, research progress in this field has been slowed by a number of factors, including the lack of appropriate and realistic datasets, ethical and privacy issues that prevent the recording and distribution of facial images, the little attention that the community has given to potential ethnic biases among others. This chapter provides an overview of contributions derived from the organization of academic challenges in the context of face anti-spoofing detection. Specifically, we discuss the limitations of benchmarks and summarize our efforts in trying to boost research by the community via the participation in academic challenges | ||||
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Series Editor | Series Title | Abbreviated Series Title | SLCV | ||
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Notes | HUPBA | Approved | no | ||
Call Number | Admin @ si @ WGE2023c | Serial | 3957 | ||
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Author | Rain Eric Haamer; Eka Rusadze; Iiris Lusi; Tauseef Ahmed; Sergio Escalera; Gholamreza Anbarjafari | ||||
Title | Review on Emotion Recognition Databases | Type | Book Chapter | ||
Year | 2018 | Publication | Human-Robot Interaction: Theory and Application | Abbreviated Journal | |
Volume | Issue | Pages | |||
Keywords | emotion; computer vision; databases | ||||
Abstract | Over the past few decades human-computer interaction has become more important in our daily lives and research has developed in many directions: memory research, depression detection, and behavioural deficiency detection, lie detection, (hidden) emotion recognition etc. Because of that, the number of generic emotion and face databases or those tailored to specific needs have grown immensely large. Thus, a comprehensive yet compact guide is needed to help researchers find the most suitable database and understand what types of databases already exist. In this paper, different elicitation methods are discussed and the databases are primarily organized into neat and informative tables based on the format. | ||||
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ISSN | ISBN | 978-1-78923-316-2 | Medium | ||
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Notes | HUPBA; 602.133 | Approved | no | ||
Call Number | Admin @ si @ HRL2018 | Serial | 3212 | ||
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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 | 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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Notes | HUPBA; no proj | Approved | no | ||
Call Number | Admin @ si @ ACB2017a | Serial | 2981 | ||
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Author | Sergio Escalera; Vassilis Athitsos; Isabelle Guyon | ||||
Title | Challenges in Multi-modal Gesture Recognition | Type | Book Chapter | ||
Year | 2017 | Publication | Abbreviated Journal | ||
Volume | Issue | Pages | 1-60 | ||
Keywords | Gesture recognition; Time series analysis; Multimodal data analysis; Computer vision; Pattern recognition; Wearable sensors; Infrared cameras; Kinect TMTM | ||||
Abstract | This paper surveys the state of the art on multimodal gesture recognition and introduces the JMLR special topic on gesture recognition 2011–2015. We began right at the start of the Kinect TMTM revolution when inexpensive infrared cameras providing image depth recordings became available. We published papers using this technology and other more conventional methods, including regular video cameras, to record data, thus providing a good overview of uses of machine learning and computer vision using multimodal data in this area of application. Notably, we organized a series of challenges and made available several datasets we recorded for that purpose, including tens of thousands of videos, which are available to conduct further research. We also overview recent state of the art works on gesture recognition based on a proposed taxonomy for gesture recognition, discussing challenges and future lines of research. | ||||
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Notes | HuPBA; no proj | Approved | no | ||
Call Number | Admin @ si @ EAG2017 | Serial | 3008 | ||
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Author | Sergio Escalera; Markus Weimer; Mikhail Burtsev; Valentin Malykh; Varvara Logacheva; Ryan Lowe; Iulian Vlad Serban; Yoshua Bengio; Alexander Rudnicky; Alan W. Black; Shrimai Prabhumoye; Łukasz Kidzinski; Mohanty Sharada; Carmichael Ong; Jennifer Hicks; Sergey Levine; Marcel Salathe; Scott Delp; Iker Huerga; Alexander Grigorenko; Leifur Thorbergsson; Anasuya Das; Kyla Nemitz; Jenna Sandker; Stephen King; Alexander S. Ecker; Leon A. Gatys; Matthias Bethge; Jordan Boyd Graber; Shi Feng; Pedro Rodriguez; Mohit Iyyer; He He; Hal Daume III; Sean McGregor; Amir Banifatemi; Alexey Kurakin; Ian Goodfellow; Samy Bengio | ||||
Title | Introduction to NIPS 2017 Competition Track | Type | Book Chapter | ||
Year | 2018 | Publication | The NIPS ’17 Competition: Building Intelligent Systems | Abbreviated Journal | |
Volume | Issue | Pages | 1-23 | ||
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Abstract | Competitions have become a popular tool in the data science community to solve hard problems, assess the state of the art and spur new research directions. Companies like Kaggle and open source platforms like Codalab connect people with data and a data science problem to those with the skills and means to solve it. Hence, the question arises: What, if anything, could NIPS add to this rich ecosystem?
In 2017, we embarked to find out. We attracted 23 potential competitions, of which we selected five to be NIPS 2017 competitions. Our final selection features competitions advancing the state of the art in other sciences such as “Classifying Clinically Actionable Genetic Mutations” and “Learning to Run”. Others, like “The Conversational Intelligence Challenge” and “Adversarial Attacks and Defences” generated new data sets that we expect to impact the progress in their respective communities for years to come. And “Human-Computer Question Answering Competition” showed us just how far we as a field have come in ability and efficiency since the break-through performance of Watson in Jeopardy. Two additional competitions, DeepArt and AI XPRIZE Milestions, were also associated to the NIPS 2017 competition track, whose results are also presented within this chapter. |
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Publisher | Springer | Place of Publication | Editor | Sergio Escalera; Markus Weimer | |
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ISSN | ISBN | 978-3-319-94042-7 | Medium | ||
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Notes | HUPBA; no proj | Approved | no | ||
Call Number | Admin @ si @ EWB2018 | Serial | 3200 | ||
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Author | Sergio Escalera; Marti Soler; Stephane Ayache; Umut Guçlu; Jun Wan; Meysam Madadi; Xavier Baro; Hugo Jair Escalante; Isabelle Guyon | ||||
Title | ChaLearn Looking at People: Inpainting and Denoising Challenges | Type | Book Chapter | ||
Year | 2019 | Publication | The Springer Series on Challenges in Machine Learning | Abbreviated Journal | |
Volume | Issue | Pages | 23-44 | ||
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Abstract | Dealing with incomplete information is a well studied problem in the context of machine learning and computational intelligence. However, in the context of computer vision, the problem has only been studied in specific scenarios (e.g., certain types of occlusions in specific types of images), although it is common to have incomplete information in visual data. This chapter describes the design of an academic competition focusing on inpainting of images and video sequences that was part of the competition program of WCCI2018 and had a satellite event collocated with ECCV2018. The ChaLearn Looking at People Inpainting Challenge aimed at advancing the state of the art on visual inpainting by promoting the development of methods for recovering missing and occluded information from images and video. Three tracks were proposed in which visual inpainting might be helpful but still challenging: human body pose estimation, text overlays removal and fingerprint denoising. This chapter describes the design of the challenge, which includes the release of three novel datasets, and the description of evaluation metrics, baselines and evaluation protocol. The results of the challenge are analyzed and discussed in detail and conclusions derived from this event are outlined. | ||||
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Notes | HuPBA; no proj | Approved | no | ||
Call Number | Admin @ si @ ESA2019 | Serial | 3327 | ||
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Author | Isabelle Guyon; Lisheng Sun Hosoya; Marc Boulle; Hugo Jair Escalante; Sergio Escalera; Zhengying Liu; Damir Jajetic; Bisakha Ray; Mehreen Saeed; Michele Sebag; Alexander R.Statnikov; Wei-Wei Tu; Evelyne Viegas | ||||
Title | Analysis of the AutoML Challenge Series 2015-2018. | Type | Book Chapter | ||
Year | 2019 | Publication | Automated Machine Learning | Abbreviated Journal | |
Volume | Issue | Pages | 177-219 | ||
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Abstract | The ChaLearn AutoML Challenge (The authors are in alphabetical order of last name, except the first author who did most of the writing and the second author who produced most of the numerical analyses and plots.) (NIPS 2015 – ICML 2016) consisted of six rounds of a machine learning competition of progressive difficulty, subject to limited computational resources. It was followed bya one-round AutoML challenge (PAKDD 2018). The AutoML setting differs from former model selection/hyper-parameter selection challenges, such as the one we previously organized for NIPS 2006: the participants aim to develop fully automated and computationally efficient systems, capable of being trained and tested without human intervention, with code submission. This chapter analyzes the results of these competitions and provides details about the datasets, which were not revealed to the participants. The solutions of the winners are systematically benchmarked over all datasets of all rounds and compared with canonical machine learning algorithms available in scikit-learn. All materials discussed in this chapter (data and code) have been made publicly available at http://automl.chalearn.org/. | ||||
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Publisher | Springer | Place of Publication | Editor | ||
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Series Editor | Series Title | Abbreviated Series Title | SSCML | ||
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Notes | HuPBA; no proj | Approved | no | ||
Call Number | Admin @ si @ GHB2019 | Serial | 3330 | ||
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Author | Jaume Garcia; Debora Gil; Aura Hernandez-Sabate | ||||
Title | Endowing Canonical Geometries to Cardiac Structures | Type | Book Chapter | ||
Year | 2010 | Publication | Statistical Atlases And Computational Models Of The Heart | Abbreviated Journal | |
Volume | 6364 | Issue | Pages | 124-133 | |
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Abstract | International conference on Cardiac electrophysiological simulation challenge
In this paper, we show that canonical (shape-based) geometries can be endowed to cardiac structures using tubular coordinates defined over their medial axis. We give an analytic formulation of these geometries by means of B-Splines. Since B-Splines present vector space structure PCA can be applied to their control points and statistical models relating boundaries and the interior of the anatomical structures can be derived. We demonstrate the applicability in two cardiac structures, the 3D Left Ventricular volume, and the 2D Left-Right ventricle set in 2D Short Axis view. |
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Publisher | Springer Berlin / Heidelberg | Place of Publication | Editor | Camara, O.; Pop, M.; Rhode, K.; Sermesant, M.; Smith, N.; Young, A. | |
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Series Editor | Series Title | Lecture Notes in Computer Science | Abbreviated Series Title | LNCS | |
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Notes | IAM | Approved | no | ||
Call Number | IAM @ iam @ GGH2010b | Serial | 1515 | ||
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Author | F.Guirado; Ana Ripoll; C.Roig; Aura Hernandez-Sabate; Emilio Luque | ||||
Title | Exploiting Throughput for Pipeline Execution in Streaming Image Processing Applications | Type | Book Chapter | ||
Year | 2006 | Publication | Euro-Par 2006 Parallel Processing | Abbreviated Journal | LNCS |
Volume | 4128 | Issue | Pages | 1095-1105 | |
Keywords | 12th International Euro–Par Conference | ||||
Abstract | There is a large range of image processing applications that act on an input sequence of image frames that are continuously received. Throughput is a key performance measure to be optimized when execu- ting them. In this paper we propose a new task replication methodology for optimizing throughput for an image processing application in the field of medicine. The results show that by applying the proposed methodo- logy we are able to achieve the desired throughput in all cases, in such a way that the input frames can be processed at any given rate. | ||||
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Publisher | Springer-Verlag Berlin Heidelberg | Place of Publication | Dresden, Germany (European Union) | Editor | UAB; W, E.N.; et al. |
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Series Editor | Series Title | Lecture Notes In Computer Science | Abbreviated Series Title | ||
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Area | Expedition | Conference | Euro–Par | ||
Notes | IAM | Approved | no | ||
Call Number | IAM @ iam @ GRR2006a | Serial | 1542 | ||
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Author | Sergio Vera; Miguel Angel Gonzalez Ballester; Debora Gil | ||||
Title | Optimal Medial Surface Generation for Anatomical Volume Representations | Type | Book Chapter | ||
Year | 2012 | Publication | Abdominal Imaging. Computational and Clinical Applications | Abbreviated Journal | LNCS |
Volume | 7601 | Issue | Pages | 265-273 | |
Keywords | Medial surface representation; volume reconstruction | ||||
Abstract | Medial representations are a widely used technique in abdominal organ shape representation and parametrization. Those methods require good medial manifolds as a starting point. Any medial
surface used to parametrize a volume should be simple enough to allow an easy manipulation and complete enough to allow an accurate reconstruction of the volume. Obtaining good quality medial surfaces is still a problem with current iterative thinning methods. This forces the usage of generic, pre-calculated medial templates that are adapted to the final shape at the cost of a drop in volume reconstruction. This paper describes an operator for generation of medial structures that generates clean and complete manifolds well suited for their further use in medial representations of abdominal organ volumes. While being simpler than thinning surfaces, experiments show its high performance in volume reconstruction and preservation of medial surface main branching topology. |
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Address | Nice, France | ||||
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Publisher | Springer Berlin Heidelberg | Place of Publication | Editor | Yoshida, Hiroyuki and Hawkes, David and Vannier, MichaelW. | |
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Series Editor | Series Title | Lecture Notes in Computer Science | Abbreviated Series Title | ||
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ISSN | 0302-9743 | ISBN | 978-3-642-33611-9 | Medium | |
Area | Expedition | Conference | STACOM | ||
Notes | IAM | Approved | no | ||
Call Number | IAM @ iam @ VGG2012b | Serial | 1988 | ||
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