Journal Information
Pattern Recognition (PR)
Impact Factor:
Call For Papers
Pattern Recognition is the official journal of the Pattern Recognition Society. The Society was formed to fill a need for information exchange among research workers in the pattern recognition field. Up to now, we ''pattern-recognitionophiles'' have been tagging along in computer science, information theory, optical processing techniques, and other miscellaneous fields. Because this work in pattern recognition presently appears in widely spread articles and as isolated lectures in conferences in many diverse areas, the purpose of the journal Pattern Recognition is to give all of us an opportunity to get together in one place to publish our work. The journal will thereby expedite communication among research scientists interested in pattern recognition.

We consider pattern recognition in the broad sense, and we assume that the journal will be read by people with a common interest in pattern recognition but from many diverse backgrounds. These include biometrics, target recognition, biological taxonomy, meteorology, space science, classification methods, character recognition, image processing, industrial applications, neural computing, and many others.

The publication policy is to publish (1) new original articles that have been appropriately reviewed by competent scientific people, (2) reviews of developments in the field, and (3) pedagogical papers covering specific areas of interest in pattern recognition. Various special issues will be organized from time to time on current topics of interest to Pattern Recognition.
Last updated by Dou Sun in 2021-03-07
Special Issues
Special Issue on Open World Robust Pattern Recognition
Submission Date: 2022-01-30

The special issue seeks original contributions which address the challenges in open world robust pattern recognition. Possible topics include but are not limited to: - Theoretical analysis of openness and robustness - Out-of-distribution detection, open-set recognition, anomaly detection - Class-incremental learning, continual learning, lifelong learning - Adversarial attack and defense methods - Learning with noisy data in open world - Model adaptation and transfer learning in changing environment - Applications in open world sensing such as video surveillance, robot vision, autonomous driving, biometrics recognition, document analysis, etc.
Last updated by Dou Sun in 2021-09-21
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