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Author |
Marçal Rusiñol; Dimosthenis Karatzas; Josep Llados |
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Title |
Spotting Graphical Symbols in Camera-Acquired Documents in Real Time |
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Book Chapter |
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Year |
2014 |
Publication |
Graphics Recognition. Current Trends and Challenges |
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Volume |
8746 |
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Pages  |
3-10 |
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In this paper we present a system devoted to spot graphical symbols in camera-acquired document images. The system is based on the extraction and further matching of ORB compact local features computed over interest key-points. Then, the FLANN indexing framework based on approximate nearest neighbor search allows to efficiently match local descriptors between the captured scene and the graphical models. Finally, the RANSAC algorithm is used in order to compute the homography between the spotted symbol and its appearance in the document image. The proposed approach is efficient and is able to work in real time. |
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Springer Berlin Heidelberg |
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Bart Lamiroy; Jean-Marc Ogier |
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0302-9743 |
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978-3-662-44853-3 |
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DAG; 600.045; 600.055; 600.061; 600.077 |
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no |
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Admin @ si @ RKL2014 |
Serial |
2700 |
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Author |
Angel Sappa; Niki Aifanti; N. Grammalidis; Sotiris Malassiotis |
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Title |
Advances in Vision-Based Human Body Modeling |
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Book Chapter |
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2004 |
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3D Modeling & Animation: Systhesis and Analysis Techniques for the Human Body |
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1-26 |
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N. Sarris and M. Strintzis. |
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1-59140-299-9 |
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ADAS |
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no |
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ADAS @ adas @ SAG2004a |
Serial |
458 |
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Author |
Muhammad Muzzamil Luqman; Jean-Yves Ramel; Josep Llados |
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Title |
Multilevel Analysis of Attributed Graphs for Explicit Graph Embedding in Vector Spaces |
Type |
Book Chapter |
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Year |
2013 |
Publication |
Graph Embedding for Pattern Analysis |
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1-26 |
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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. |
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Springer New York |
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978-1-4614-4456-5 |
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DAG |
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no |
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Admin @ si @ LRL2013b |
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2271 |
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Author |
Michal Drozdzal; Santiago Segui; Petia Radeva; Carolina Malagelada; Fernando Azpiroz; Jordi Vitria |
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Title |
An Application for Efficient Error-Free Labeling of Medical Images |
Type |
Book Chapter |
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Year |
2013 |
Publication |
Multimodal Interaction in Image and Video Applications |
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Volume |
48 |
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Pages |
1-16 |
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Abstract |
In this chapter we describe an application for efficient error-free labeling of medical images. In this scenario, the compilation of a complete training set for building a realistic model of a given class of samples is not an easy task, making the process tedious and time consuming. For this reason, there is a need for interactive labeling applications that minimize the effort of the user while providing error-free labeling. We propose a new algorithm that is based on data similarity in feature space. This method actively explores data in order to find the best label-aligned clustering and exploits it to reduce the labeler effort, that is measured by the number of “clicks. Moreover, error-free labeling is guaranteed by the fact that all data and their labels proposals are visually revised by en expert. |
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Springer Berlin Heidelberg |
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1868-4394 |
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978-3-642-35931-6 |
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MILAB; OR;MV |
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no |
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Call Number |
Admin @ si @ DSR2013 |
Serial |
2235 |
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Author |
C. Alejandro Parraga |
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Title |
Color Vision, Computational Methods for |
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Book Chapter |
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Year |
2014 |
Publication |
Encyclopedia of Computational Neuroscience |
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Pages |
1-11 |
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Keywords |
Color computational vision; Computational neuroscience of color |
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Abstract |
The study of color vision has been aided by a whole battery of computational methods that attempt to describe the mechanisms that lead to our perception of colors in terms of the information-processing properties of the visual system. Their scope is highly interdisciplinary, linking apparently dissimilar disciplines such as mathematics, physics, computer science, neuroscience, cognitive science, and psychology. Since the sensation of color is a feature of our brains, computational approaches usually include biological features of neural systems in their descriptions, from retinal light-receptor interaction to subcortical color opponency, cortical signal decoding, and color categorization. They produce hypotheses that are usually tested by behavioral or psychophysical experiments. |
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Springer-Verlag Berlin Heidelberg |
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Editor |
Dieter Jaeger; Ranu Jung |
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978-1-4614-7320-6 |
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Notes |
CIC; 600.074 |
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no |
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Call Number |
Admin @ si @ Par2014 |
Serial |
2512 |
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Author |
Sergio Escalera; Vassilis Athitsos; Isabelle Guyon |
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Title |
Challenges in Multi-modal Gesture Recognition |
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Book Chapter |
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Year |
2017 |
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1-60 |
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Keywords |
Gesture recognition; Time series analysis; Multimodal data analysis; Computer vision; Pattern recognition; Wearable sensors; Infrared cameras; Kinect TMTM |
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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 |
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no |
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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 |
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Title |
Introduction to NIPS 2017 Competition Track |
Type |
Book Chapter |
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Year |
2018 |
Publication |
The NIPS ’17 Competition: Building Intelligent Systems |
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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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Springer |
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Editor |
Sergio Escalera; Markus Weimer |
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978-3-319-94042-7 |
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HUPBA; no proj |
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no |
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Call Number |
Admin @ si @ EWB2018 |
Serial |
3200 |
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Author |
Jun Wan; Guodong Guo; Sergio Escalera; Hugo Jair Escalante; Stan Z Li |
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Title |
Face Anti-spoofing Progress Driven by Academic Challenges |
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Book Chapter |
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2023 |
Publication |
Advances in Face Presentation Attack Detection |
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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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HUPBA |
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no |
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Admin @ si @ WGE2023c |
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3957 |
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Author |
Felipe Lumbreras; Ramon Baldrich; Maria Vanrell; Joan Serrat; Juan J. Villanueva |
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Title |
Multiresolution texture classification of ceramic tiles. |
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1999 |
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Recent Research developments in optical engineering, Research Signpost, 2: 213–228 |
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India |
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ADAS;CIC |
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ADAS @ adas @ LBV1999b |
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45 |
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Author |
David Masip; Jordi Vitria |
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Title |
Classifier Combination Applied to Real Time Face Detection and Classification. |
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2004 |
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Recerca Automatica, Visio i Robotica, Ed. UPC, A. Grau, V. Puig (Eds.), 345–353, ISBN 84–7653–844–8 |
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OR;MV |
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BCNPCL @ bcnpcl @ MBV2004b |
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449 |
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Author |
P. Andreeva; Maya Dimitrova; Petia Radeva |
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Title |
Data Mining Learning Models and Algorithms for Medical Applications |
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2004 |
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18 Conference Systems for Automation of Engineering and Research (SEAR 2004) |
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Varna (Bulgaria) |
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MILAB |
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BCNPCL @ bcnpcl @ ADR2004 |
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474 |
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Author |
Niki Aifanti; Angel Sappa; N. Grammalidis; Sotiris Malassiotis |
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Title |
Human Motion Tracking and Recognition |
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2005 |
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Encyclopedia of Information Science and Technology, 1(5):1355–1360 |
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ADAS @ adas @ ASG2005 |
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496 |
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Angel Sappa; Niki Aifanti; Sotiris Malassiotis; N. Grammalidis |
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Title |
Survey of 3D Human Body Representations |
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2005 |
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Encyclopedia of Information Science and Technology, 1(5):2696–2701 |
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ADAS @ adas @ SAM2005a |
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497 |
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Jordi Vitria; Petia Radeva; I. Aguilo |
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Title |
Recent Advances in Artificial Intelligence Research and Development |
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2004 |
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Frontiers in Artificial Intelligence and Applications, 113, J. Vitria, P. Radeva, I. Aguilo (Eds.), ISBN: 1–58603–466–9 |
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Amsterdam |
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OR;MILAB;MV |
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BCNPCL @ bcnpcl @ VRA2004 |
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509 |
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Author |
Ignasi Rius; Dani Rowe; Jordi Gonzalez; Xavier Roca |
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Title |
A 3D Dynamic Model of Human Actions for Probabilistic Image Tracking |
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Book Chapter |
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Year |
2005 |
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Pattern Recognition and Image Analysis (IbPRIA 2005), LNCS 3522: 529–536 |
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Estoril (Portugal) |
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ISE |
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Call Number |
ISE @ ise @ RRG2005b |
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544 |
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