Technical Committees

of the IAPR


By Larry O’Gorman—Editor, IAPR Newsletter

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The Technical Committees (TCs) of IAPR are one of its major centers of activity.  These comprise groups of people with interest in particular sub-fields of pattern recognition. The main purpose of the TCs is to promote work in their area and facilitate communication among their members. They accomplish this in various ways, such as: maintaining mailing lists of members, producing periodic mailings on topics of interest, and holding formal workshops each year or two.


All the TCs encourage membership of those participating or interested in their sub-field. Membership is easy, just email the TC chair that you'd like to be included. After this, you can choose your level of involvement, from keeping up with the field via mailings to actively participating in organization of the next workshop.


Below are descriptions of all 19 IAPR TCs. Look over the descriptions, and if you're interested in delving more into one of the subfields, send mail to join.

TC1 Statistical Pattern Recognition Techniques                Chair:  Professor Fabio Roli


TC1 aims to promote interaction and collaboration among researchers working directly in statistical pattern recognition and also among those specialized in other fields but using or developing statistical techniques. In this relation it is of particular interest to stimulate links with mathematical statisticians, theoreticians and practitioners who work at present outside the pattern recognition community.


TC1 web site:

Click here to email Professor Roli:

TC2 Structural & Syntactical Pattern Recognition            Chair: Professor James Tin-Yau Kwok


Structural and Syntactical Pattern Recognition (SSPR) is a core area of Pattern Recognition. It is based on the fundamental premise that "shape" or "patterns" in any domain (space, space-time, etc.) is encoded by the attributes of parts and their relations in the domain of reference.  IAPR's TC2 on SSPR promotes interaction among researchers working on such fundamental issues and their applications in different domains.


TC2 web site:

Click here to email Professor Kwok:

TC3 Neural Networks & Computational Intelligence                      Chair:  Dr Simone Marinai


Computational Intelligence (CI) covers all types of sub symbolic knowledge processing and machine learning techniques, particularly artificial neural networks, evolutionary algorithms and fuzzy logic. Although these research fields are very well developed, there still exists a large gap between theory and application. To close this gap, focusing on pattern recognition problems, is an important goal of TC3 on Neural Networks and Computational Intelligence. Activities of TC3 include the organization of workshops, collecting tutorials on CI-methods and benchmark data sets for the statistical evaluation of pattern recognition algorithms.


TC3 web site:

Click here to email Dr Marinai:

TC4 Biometrics          Chair:  Professor Tieniu Tan


Click here to email Professor Tan for information on TC4 Biometrics:

TC5 Benchmarking & Software        Chair:  Dr Roberto Paredes

This information was taken from the TC5 web site.  (L. O’Gorman, ed.)


"The aim of TC5 is to provide resources to assist researchers in implementing and benchmarking pattern recognition systems. While other IAPR TCs are concerned with particular areas of pattern recognition, TC5 is unusual in that its remit is to provide benchmarking and software services to all fields of pattern recognition."


TC5 web site:

Click here to email Dr. Paredes:

TC7 Remote Sensing and Mapping             Chair:  Professor Selim Aksoy


TC7 aims at promoting the use of pattern recognition methods in the analysis of data collected from satellites or airborne sensors used for Earth observation as the large volumes of remote sensing data recently available require advanced algorithms and techniques for automatic analysis.


TC7 web site:

Click here to email Professor Aksoy:

TC8 Machine Vision Applications                 Chair:  Dr Shigeru Sasaki


Please see the TC8 web site for more information,

TC9 Biomedical Image Analysis      Chair:  (the chair is currently empty)


The goal of TC9 is the application of computer vision, pattern recognition, computer graphics and robotics techniques to biomedical images acquired by microscopy, video, X-ray, computed tomography, magnetic resonance, nuclear medicine and ultrasound. These images are at different spatial scales, ranging from molecular and cellular  to tissue and organ. Typical problems are: representation of pictorial data, visualization, feature extraction, segmentation, intra- and inter-subject registration, longitudinal / temporal studies, image-guided therapy, shape and motion measurements, spectral analysis, digital anatomical atlases, statistical shape analysis, modeling of anatomy and physiology, virtual and augmented reality for therapy planning and guidance, telemedicine with medical images, telepresence in medicine, telesurgery, image-guided medical robots, etc.


TC9 web site:

Click here for the General Info page of the TC9 web site:

TC10 Graphics Recognition             Chair:  Professor Liu Wenyin 


TC10 promotes interaction among researchers working in document image analysis in general, and graphics recognition in particular. GREC is its primary workshop series organized by TC10, which also hosts the international contest series in graphics recognition. TC10 co-sponsors ICDAR with TC11.


Topics of primary interest are:  raster-to-vector techniques; recognition of graphical primitives, shapes and symbols; analysis and interpretation of engineering drawings, logic diagrams, maps, diagrams, charts, etc.; analysis of line drawings, tables, forms etc.; 3-D models from multiple 2-D views (line drawing); graphics-based information retrieval; performance evaluation in graphics recognition; and systems for graphics recognition.


TC10 web site:

Click here to email Professor Liu Wenyin:

TC11 Reading Systems         Chair:  Dr Jianying Hu          


The goal of TC11is to foster research in the understanding and development of systems that are able to analyze any media containing character symbols and return an encoded representation of the text content and structure. It covers a wide range of applications including digital libraries, pen-based computing, check and mail reading, signature verification, web mining and content repurposing, web security using human interactive proofs, textual content analysis in videos, and historical document preservation and archiving. Key research areas include image processing, feature extraction and selection, classification methods, statistical and syntactical pattern recognition and machine learning.


TC11 web site:

Click here to email Dr Hu:

TC12 Multimedia and Visual Information Systems            Chair:  Dr Marcel Worring


TC12 promotes interaction among researchers working in the modeling, design, and development of systems for the analysis, processing, description, and retrieval of multimedia and visual information as well as the applications of these systems in challenging domains. Emphasis is on the role that pattern recognition can play in supporting the various tasks.


TC12 web site:

Click here to email Dr Worring:

TC13 Pattern Recognition in Astronomy & Astrophysics            Chair:  Dr Tin Kam Ho


TC13 aims to promote interaction and collaboration among researchers working in computer science as well as in astronomy and astrophysics, to

facilitate integration of methodologies in pattern recognition, information retrieval, and data analysis into modern computational astronomy.


TC13 web site:

Click here to email Dr. Ho:

TC14 Signal Analysis for Machine Intelligence                  Chair:  Professor Sergios Theodoridis


The goal of TC14 is to focus on the Signal Analysis and Signal Processing aspects in Pattern Recognition tasks and promote a cross-fertilization  between these disciplines. Typical areas of interest are: speech, audio and music applications; image, video and intelligent multimedia tasks; kernel methods; learning theory and modeling; Bayesian learning; sequential learning; neural networks learning; feature generation and selection for signals and images (e.g., PCA, ICA)


TC14 web site:

Click here to email Professor Theodoridis:

TC15 Graph Based Representations                        Chair:  Professor Luc Brun


The goal of the TC15 is to federate and to encourage research works in Pattern Recognition and Image Analysis within the graph theory framework. Our topics include graph based clustering and matching, graph based image segmentation, irregular (graph) pyramids, graph representation of shape, graph transformations and graph navigation. Some of the topics of the TC15 are related to other domains, like obviously graph theory, automata theory, machine learning, finite state machines, robotics,  Petri nets…


TC15 web site:

Click here to email Professor Brun:

TC16 Algebraic and Discrete Mathematical Techniques in Pattern Recognition & Image Analysis

Chair:  Dr Igor Gurevich


“The main goals of TC16 are discussion of actual and prospective lines of research and exchange of the results in Algebraic and Discrete Mathematical Problems and Techniques inspired by Pattern Recognition and Image Analysis. The means which TC 16 uses to achieve the goals are more or less standard for IAPR TCs: the organization of workshops and conferences, the preparation of publications (survey articles, tutorials, etc.), the design of databases including information on scientists and specialists in the field, bibliographical databases, benchmarking data, support of communication between members, exchange by results and others.”

TC16 web site:

Click here to email Dr Gurevich

TC17 Machine Learning and Data Mining       Chair: Prof Atsushi Imiya          


“Data Mining, which is also referred to as knowledge discovery in data bases, means a process of nontrivial extraction of implicit, previously unknown and potentially useful information (such as knowledge rules, constraints, regularities) from data in data bases.

The interest in Data Mining and Machine Learning has increased over the last decade. Whereas in former times it was only a research topic, industry recently has got more and more attracted by that topic, since there is a specific need to mine the large data bases for higher-quality information. This leads to a quick transfer of research results into practice and brings industry and researchers closer together.

The problem of mining multimedia data has not been solved in the data mining community yet, since it requires special understanding of the information type. Here is a special challenge for the pattern-recognition community.  There are a lot of challenging multimedia applications around that have special requirements and therefore need new methods for solving these applications. Some of them are e.g. internet-based image mining, mining picture archiving system, mining user preferences, and text mining.

Although pattern recognition has much in common with data mining, the latter requires methods that can work on large collections of data and mixed data types such as images, video and text. Therefore new methods have to be developed that satisfy these needs. Besides that methods that can handle symbolical data and mixed data types are required. This opens new challenges to the pattern-recognition community.”


The TC17 web site is accessible from the home page of Dr. Perner:

Click here to email Professor Imiya:

TC18 Discrete Geometry       Chair:  Dr David Coeurjolly


Discrete geometry plays an essential role in the field of image analysis, computer graphics, pattern recognition, shape modelling, computer vision, and document analysis. The main reason is, of course, that all data in the computer is unavoidably discrete. Discrete geometry provides both a theoretical and a computational framework for digital images. The aim of TC18 is to promote interactions and collaboration between researchers working in the field of discrete geometry.


TC18 web site:

Click here to email Professor Coeurjolly:

TC19 Computer Vision for Cultural Heritage Applications          Chair:  Dr Robert Sablatnig


The goal of TC19 is to promote Computer Vision Applications in Cultural Heritage and their integration spanning all aspects of IAPR activities. It aims at stimulating the development of components (both hardware and software) that can be used by the target audience: Researchers in Cultural heritage like archaeologists, art historians, curators and institutions like universities, museums and research organizations.


The purpose of TC19 is to provide a forum to discuss how computer vision has been applied to cultural heritage problems and in turn define new interesting problems to work on. Therefore, we strongly encourage the development, building, and thorough evaluation of individual components and the demonstration of their usage in building complete systems for Cultural Heritage Applications. TC19 is particularly interested in topics related to the following problems:  design methods for Cultural Heritage documentation systems and components; 3D reconstruction of cultural heritage objects and fragments; reassembly of artifacts from fragments; 3D architectural site reconstruction or representation from imagery and other data; shape representation for free-form modelling (statues, bones, etc.); automated trench recordings from video; shape matching/indexing in large databases (for a single site and across multiple sites); surface modelling from various sensing modalities, to represent 3D texture, BRDF, etc. of walls, sculpture, etc.; texture modelling from imagery, remote sensing, models, etc. (to model large surfaces, backgrounds); excavation's historical documentation from multimedia data; vision-based Augmented Reality for site exploration (educational, scientific, tourism); colour vision for visualization and/or preservation and/or recovery; shape-based completion for preservation and/or recovery; archaeography (Analysis of historical documents).


TC19 web site:

Click here to email Dr. Sablatnig:

TC20 Pattern Recognition for Bioinformatics        Chair:  Professor Jagath C Rajapakse


We have seen an explosion of life sciences data over the past decade. TC20 is interested in using pattern recognition techniques to discover knowledge from life sciences data.  The goal of TC20 is to bring together pattern recognition scientists and life scientists to find solutions to problems in bioinformatics and to foster multidisciplinary research in the pattern recognition community.


Topics in research in bioinformatics include:  bio-sequence analysis; gene and protein expression analysis; protein structure and interactions prediction; signal and motif detection; systems biology, pathway analysis; ontologies and taxonomies; molecular evolution and phylogeny; immunoinformatics; biological databases; bio-imaging.


Pattern recognition techniques of investigation include statistical, syntactic and structural approaches, neural networks, fuzzy techniques, genetic algorithms, Bayesian models and networks, data mining techniques, and their hybrids.


TC20 web site:

Click here to email Profesor Rajapakse: