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Title: High-speed pattern cutting using real-time computer vision techniques
Author: Tao, Li G.
ISNI:       0000 0004 2721 413X
Awarding Body: Loughborough University
Current Institution: Loughborough University
Date of Award: 1994
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This thesis presents a study of computer vision for guiding cutting tools to perform high-speed pattern cutting on deformable materials. Several new concepts on establishing a computer vision system to guide a C02 laser beam to separate lace are presented. The aim of this study is to determine a cutting path on lace in real-time by using computer vision techniques, which is part of an automatic lace separation project. The purpose of this project is to replace the current lace separation process which uses a mechanical knife or scissors. The research on computer vision has concentrated on the following aspects: 1. A weighted incremental tracking algorithm based on a reference map is proposed, examined and implemented. This is essential for tracking an arbitrarily defined path across the surface of a patterned deformable material such as lace. Two methods, a weighting function and infinite impulse response filter, are used to cope with lateral distortions of the input image. Three consecutive map lines matching with one image line is introduced to cope with longitudinal distortion. A software and hardware hybrid approach boosts the tracking speed to hnls that is 2-4 times faster than the current mechanical method. 2. A modified Hough transform and the weighted incremental tracking algorithm to find the start point for tracking are proposed and investigated to enable the tracking to start from the correct position on the map. 3. In order to maintain consistent working conditions for the vision system, the light source, camera threshold and camera scan rate synchronisation with lace movement are studied. Two test rigs combining the vision and cutting system have been built and used to cut lace successfully.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available