Learning Conoidal Structures in Connectionist Framework.
Date of Submission
December 2000
Date of Award
Winter 12-12-2001
Institute Name (Publisher)
Indian Statistical Institute
Document Type
Master's Dissertation
Degree Name
Master of Technology
Subject Name
Computer Science
Department
Machine Intelligence Unit (MIU-Kolkata)
Supervisor
Basak, Jayanta (MIU-Kolkata; ISI)
Abstract (Summary of the Work)
A two layer neural network model is designed which accepts image coordinates as the input and learns the parametric form of conoidal shapes (lines/circles/ellipses) adaptively. It provides an efficient representation of visual information embedded in the connection weights and the parameter of the processing elements. It not only reduces the large space requirements as in classical Hough transform, but also represents parameters with high precision, even in presence of noise. The performance of the methodology is compared with other existing algorithms and has been found to excel over those algorithms in many cases.
Control Number
ISI-DISS-2000-72
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
DOI
http://dspace.isical.ac.in:8080/jspui/handle/10263/6244
Recommended Citation
Das, Anirban, "Learning Conoidal Structures in Connectionist Framework." (2001). Master’s Dissertations. 186.
https://digitalcommons.isical.ac.in/masters-dissertations/186
Comments
ProQuest Collection ID: http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqm&rft_dat=xri:pqdiss:28843207