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Image Processing Techniques for Ash Line Detection

Dahl, Ola (1985) In MSc Theses
Department of Automatic Control
Abstract
For efficient firing in sloping grate bark ovens, it is important to control the ash line position on the grate. Here, the problem of detecting the ash line with an image processing system is treated. The presentation of trend curves to the operator is of special interest. The trend curves shall indicate how the ash line position changes with time, and in that way improve the manual control of the process. Future development can result in a closed loop feedback system. The goal has been to obtain algorithms suitable for a small computer e.g. IBM-PC. <br><br> Three algorithms for detection of the ash line are presented. One is an optimization method where the position of the ash line is computed by maximizing a criterion. The criterion is... (More)
For efficient firing in sloping grate bark ovens, it is important to control the ash line position on the grate. Here, the problem of detecting the ash line with an image processing system is treated. The presentation of trend curves to the operator is of special interest. The trend curves shall indicate how the ash line position changes with time, and in that way improve the manual control of the process. Future development can result in a closed loop feedback system. The goal has been to obtain algorithms suitable for a small computer e.g. IBM-PC. <br><br> Three algorithms for detection of the ash line are presented. One is an optimization method where the position of the ash line is computed by maximizing a criterion. The criterion is designed especially for the application, and maximized by dynamic programming. The other two algorithms are based on thresholding techniques. One is based on preprocessing before a global thresholding, and the other is based on thresholding in subimages. A threshold selection algorithm has been developed and used in the implementation of the algorithms. <br><br> The algorithms have been tested on images taken from a video tape, showing the interior of an industrial bark oven. Experimental results show that the algorithms can be used for the generation of trend curves. (Less)
Please use this url to cite or link to this publication:
author
Dahl, Ola
supervisor
organization
year
type
H3 - Professional qualifications (4 Years - )
subject
keywords
Image processing, Ash line, Dynamic programming, Thresholding, Trend curves
publication/series
MSc Theses
report number
TFRT-5328
ISSN
0280-5316
language
English
id
8849630
date added to LUP
2016-03-27 17:29:51
date last changed
2016-03-27 17:29:51
@misc{8849630,
  abstract     = {For efficient firing in sloping grate bark ovens, it is important to control the ash line position on the grate. Here, the problem of detecting the ash line with an image processing system is treated. The presentation of trend curves to the operator is of special interest. The trend curves shall indicate how the ash line position changes with time, and in that way improve the manual control of the process. Future development can result in a closed loop feedback system. The goal has been to obtain algorithms suitable for a small computer e.g. IBM-PC. <br><br> Three algorithms for detection of the ash line are presented. One is an optimization method where the position of the ash line is computed by maximizing a criterion. The criterion is designed especially for the application, and maximized by dynamic programming. The other two algorithms are based on thresholding techniques. One is based on preprocessing before a global thresholding, and the other is based on thresholding in subimages. A threshold selection algorithm has been developed and used in the implementation of the algorithms. <br><br> The algorithms have been tested on images taken from a video tape, showing the interior of an industrial bark oven. Experimental results show that the algorithms can be used for the generation of trend curves.},
  author       = {Dahl, Ola},
  issn         = {0280-5316},
  keyword      = {Image processing,Ash line,Dynamic programming,Thresholding,Trend curves},
  language     = {eng},
  note         = {Student Paper},
  series       = {MSc Theses},
  title        = {Image Processing Techniques for Ash Line Detection},
  year         = {1985},
}