Pattern recognition in interdisciplinary perception and intelligence

This special issue came to our mind to celebrate the 50th anniversary of the field of Artificial Intelligence (AI) (Casals and Fernández-Caballero, 2007; Fernández-Caballero et al., 2008), with the aim of showing the interdisciplinarity of the fields of Pattern Recognition (PR), Intelligence and Perception, being them artificial or natural. Pattern Recognition is a field with a strong relation to Artificial Intelligence, although basically oriented to solve engineering problems in a large number of applications, and other systems like perception where the processing of sensory data is required for its interpretation. The name of Artificial Intelligence was coined by J. McCarthy (Dartmouth College, New Hampshire), M.L. Minsky (Harvard University), N. Rochester (I.B.M. Corporation) and C.E. Shannon (Bell Telephone Laboratories) in 1955, when they proposed to hold the ‘‘Dartmouth summer research project on ARTIFICIAL INTELLIGENCE”, known now as the Dartmouth Conference. The Dartmouth Conference was held during the summer of 1956, to discuss various aspects of learning and intelligence that could be simulated on machines. Pattern Recognition had been one of the important fields in AI (Minsky, 1961) until the early 1970’s when the first IJCPR (International Joint Conference on Pattern Recognition) was organised. The name of Pattern Recognition appeared later on with the objective of developing techniques in the area of classification oriented to solve engineering problems. Both areas share objectives and applications that can be solved in different ways. Pattern Recognition is the research area which studies the operation and design of systems that recognise patterns in data. It embraces sub-disciplines like discriminant analysis, feature extraction, error estimation, cluster analysis (together sometimes called statistical pattern recognition); grammatical inference, parsing and matching (sometimes called syntactical and structural pattern recognition). Pattern Recognition is largely related to other techniques such as Computer Vision, Fuzzy Sets, Neural Networks and Kernel Classifiers and to fields like Speech Recognition and Biological Perception, among others.