IAPR



The International Association for Pattern Recognition or IAPR is an international non-profit, professional organization concerned with pattern recognition, computer vision, and image processing in a broad sense.

History
IAPR came into official existence in January 1978. Following the Second International Conference on Pattern Recognition in 1974 in Copenhagen, the Standing Committee approved the proposal to establish a permanent international professional organization; two years later, the Constitution was approved in Coronado and Executive Officers were elected. Today, the responsibility for the day-to-day running of IAPR is delegated to an Executive Committee, assisted by Standing and ad hoc committees. The authority of the Association is vested in the Governing Board, composed of representatives of the member organizations, who decide all important matters such as general policy, the programme of activities, admissions, elections, and budget.

Purpose
The aims of IAPR are to promote pattern recognition and the allied branches of engineering together with the related arts and sciences, to advance international co-operation in the field of interest to stimulate research, development, and the application of pattern recognition in science and human activity, to further the dissemination and exchange of information on pattern recognition in the broad sense, and to encourage education in all aspects of the field of interest. In achieving these aims, IAPR fulfills the need for better world-wide communication and increases understanding among practitioners of all nations in the role that machine intelligence can play in accelerating technical and scientific progress.

Technical committees
Areas of pattern recognition currently represented by technical committees are: Statistical Pattern Recognition, Structural and Syntactical Pattern Recognition, Neural Networks and Computational Intelligence, Benchmarking and Software, Special Hardware and Software Environments, Remote Sensing and Mapping, Machine Vision Applications, Biomedical Applications, Graphics Recognition, Reading Systems, Multimedia and Visual Information Systems, Pattern Recognition in Astronomy and Astrophysics, Signal Analysis for Machine Intelligence, Graph Based Representations, Algebraic and Discrete Mathematical Techniques, Machine Learning and Data Mining, Discrete Geometry, Cultural Heritage Applications, and Bioinformatics.