Huffman-Clowers labelling using fuzzy logic

Date of Publication

1994

Document Type

Bachelor's Thesis

Degree Name

Bachelor of Science in Computer Science with Specialization in Computer Technology

College

College of Computer Studies

Department/Unit

Computer Technology

Abstract/Summary

Huffman-Clower labelling is one of the basic foundations in the development of robotic vision. Fuzzy logic and fuzzy set theory on the other hand has been extensively used for control purposes both in hardware and software applications. The primary objective of the study is to identify the perspective of 3-D objects from 2-D grabbed video images. The object is identified primarily through edge detection and its perspective known by identifying and classifying the individual lines making up that object according to the convention proposed by Huffman and Clowes. The Huffman-Clowes algorithm labes lines as either convex, concave or border lines. However, before these lines can be labelled properly, several image processing and analysis techniques had to be employed. This included the study of what dilation, erosion, and thinning are and how they worked. It also entailed the creation of a line detection function which could take on the rigors of sensitive line identification. Another challenging aspect of the study was incorporating fuzzy set theory and fuzzy logic into the labelling process. The Huffman-Clowes algorithm is simply not appropriate as an optimum fuzzy logic-based application. Still, the group managed to come up with a system of utilizing fuzzy set theory in an expert system environment.

Abstract Format

html

Language

English

Format

Print

Accession Number

TU09110

Shelf Location

Archives, The Learning Commons, 12F, Henry Sy Sr. Hall

Physical Description

49 numb. leaves

This document is currently not available here.

Share

COinS