Abstract
This paper describes a method of object recognition based on aspect graph using the Dempster-Shafer theory. The method deals with several sequential images as input images, and extracts basic probability of the model objects for each input image. The basic probability represents ambiguous information for object discrimination. Our approach combines basic probability which is extracted from each image based on Dempster-Shafer's rule for combining. We have applied our approach to the discrimination of objects. The experimental results show higher reliability than conventional Baysian approach.
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 callMore From: Transactions of the Institute of Systems, Control and Information Engineers
Disclaimer: 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.
