Abstract
Complex patterns may sometimes be efficiently represented by a small number of points extracted from the image on the basis of atypical properties of neighborhoods of the points. In this paper we describe theoretical and experimental results on the use of sequences of interpoint distances extracted from a noisy point image to match the image with one of a set of prototypes. Minimal spanning tree and nearest neighbor distances are used for these computations. The methods applied are invariant under translation and rotation of the image and could be made invariant to scale changes. Several distance measures between classes are defined. Sufficient conditions for the separability of classes are given in terms of these measures.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
