Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/19661
Title: Optimising connectionist models and attributed relational graph matching for object recognition
Authors: Suganthan P. N.
Keywords: DRNTU::Engineering
Issue Date: 1996
Abstract: This research work describes in depth investigation into optimising connectionist models and their applications in rigid object and pattern recognition by attributed relational graph (ARG) matching. The ARG representation is chosen because it encodes relational semantic information in itself and performs well under clutter and partial occlusion. The matching of model and scene ARGs is performed using optimising con-nectionist models. Since the connectionist models offer parallel and distributed process-ing, and cost effective hardware implementation, optimising connectionist model-based recognition systems can be employed to solve practical recognition problems.
URI: http://hdl.handle.net/10356/19661
Rights: NANYANG TECHNOLOGICAL UNIVERSITY
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Theses

Files in This Item:
File Description SizeFormat 
EEE_THESES_200.pdf
  Restricted Access
32.06 MBAdobe PDFView/Open

Google ScholarTM

Check

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.