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Adaptive Image Thresholding for Real-time Particle Monitoring by Keivan Torabi; Saed Sayad; Stephen Thomas Balke is a Computer Science article available to read on EtoBox.

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## Abstract Image thresholding is critical to computer vision systems designed to detect very small numbers of contaminant particles from analysis of images acquired by in‐line process monitoring. The objective of this work was to obtain a thresholding method that would permit in‐line, “real‐time,” determination of both the number of particles in an image and their size. An additional requirement was that it automatically adapt to inevitable variations in the image quality. A new global image thresholding method, the MaxMin method (“MaxMin”), was developed. MaxMin notes the size of the smallest detected particle in an image as threshold value is progressively changed from black to white. The selected threshold value is the one providing the largest size. MaxMin was tested on thousands of images, and it was shown to readily adapt to images of different background noise levels and provided particle counts as accurate as those of a human observer in less than three seconds per image. Error in particle size measurement was a function of the particle size and the image resolution. It was about 3% for 50 μm particles, using a CCD camera with 2× lens, calibrated for each pixel to represen

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Author

Keivan Torabi; Saed Sayad; Stephen Thomas Balke

Publisher

John Wiley and Sons; Wiley (John Wiley & Sons); John Wiley & Sons Inc.; Wiley (ISSN 0899-9457)

Published

2006

Language

EN

Field

Computer Science (Physical Sciences)