Abstract
This paper discusses a class of Discrete-Time Recurrent Neural Networks with LT neurons based on Competitive Layer Model (CLM-DT-LT-RNNs). It first addresses the boundedness and complete stability of the networks, then a theorem is given to let the networks have CLM phenomena. Such networks are applied to medical image segmentation by using the global gray-level information and the contextual information of pixels. In order to alleviate time and storage consuming, a technique of divide-and-merge (DAM) is used. Simulation results are used to illustrate the application in image segmentation.
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.
