Neurocomputing for Vision Research Neurocomputing techniques become more and more important in vision research. A great deal of problems in vision research are mitigated through the neurocomputing techniques, such as Bayesian inference, density modeling and clustering, latent variable models, manifold learning, neural networks, kernel machines, sampling techniques, semi-supervised learning, and subspace methods. The successes of the neurocomputing for vision research have been witnessed by the last five years. For example, the Markov chain Monte Carlo has been well applied for video tracking; the support vector machines combined with sampling technique and active learning have been demonstrated to improve the performance of relevance feedback in content based visual information retrieval significantly; the linear discriminant analysis and its variants have shown as the light to bright a way for face recognition; the graph cuts have been successfully employed in image segmentation; the supervised tensor learning has been utilized for image classification and biometric application; and the semi-supervised learning has boomed in image and video editing. There are just example evident parts of the combination of the two fields, neurocomputing and vision research. Elsevier Neurocomputing hunts for original research results for a *Special Issue on Neurocomputing for Vision Research*. The goals of this special issue are: 1) developing novel techniques in neurocomputing to target specific problems in vision research, 2) defining new vision research problems, which can be cleared up by techniques in neurocomputing, and 3) investigating new techniques in neurocomputing to enhance the performances of problems in vision research. Manuscripts are solicited to address a wide range of topics in neurocomputing for vision research, but not limit to the following: - Biometrics - Classification and clustering in vision - Emerging techniques for vision research - Motion analysis and recognition - New techniques in neurocomputing, such as subspace methods, kernel machines, semi supervised learning, manifold learning, etc. - Visual cognition - Visual information management - Visual surveillance - Industrial applications Manuscripts (8-30 pages in the Neurocomputing publishing format) should be submitted via the Electronic Editorial System, Elsevier: http://ees.elsevier.com/neucom/ Guide for authors can be found: http://authors.elsevier.com/GuideForAuthors.html?PubID=505628&dc=GFA Important: when submitting, please indicate: *Special Issue on* *Neurocomputing for Vision Research* Important Dates Manuscript submission: 10 April2007 Preliminary results: 10 July 2007 Revised version: 10 August 2007 Notification: 10 November 2007 Final manuscripts due: 10 December 2007 Anticipated publication: Spring 2008 Guest editors: Dacheng Tao University of London dacheng.tao at gmail.com Xuelong Li University of London xuelong_li at ieee.org