Pattern Recognition Letters

About the journal

Pattern Recognition Letters aims at rapid publication of concise articles of a broad interest in pattern recognition. Subject areas include all the current fields of interest represented by the Technical Committees of the International Association of Pattern Recognition, and other developing themes involving learning and recognition. Examples include: • Statistical, structural, syntactic pattern recognition; • Neural networks, machine learning, data mining; • Discrete geometry, algebraic, graph-based techniques for pattern recognition; • Signal analysis, image coding and processing, shape and texture analysis; • Computer vision, robotics, remote sensing; • Document processing, text and graphics recognition, digital libraries; • Speech recognition, music analysis, multimedia systems; • Natural language analysis, information retrieval; • Biometrics, biomedical pattern analysis and information systems; • Scientific, engineering, social and economical applications of pattern recognition; • Special hardware architectures, software packages for pattern recognition.

Articles

• João M. Carvalho, Susana Brás, Diogo Pratas, Jacqueline Ferreira, Sandra C. Soares, Armando J. Pinho. Extended-Alphabet Finite-Context Models. Pattern Recognition Letters, no. 112, p. 49-55, September 2018
• Vahid Mokhtari, Luís Seabra Lopes, Armando J. Pinho. Learning Robot Tasks with Loops from Experiences to Enhance Robot Adaptability. Pattern Recognition Letters, vol. 99, p. 57-66, November 2017
• Hamidreza Kasaei, Ana Maria Tomé, Luís Seabra Lopes, Miguel Riem Oliveira. GOOD: A Global Orthographic Object Descriptor for 3D Object Recognition and Manipulation. Pattern Recognition Letters, vol. 83, Part 3, p. 312–320, November 2016
• R. Schachtner, G. Poeppel, Ana Maria Tomé, Elmar W. Lang. A Bayesian approach to the Lee–Seung update rules for NMF. Pattern Recognition Letters, vol. 45, p. 251-256, August 2014

Impact factor

The impact factor of Pattern Recognition Letters is as follows: