Morphological granulometric shape recognition in noise

Ying-Chong Cheng; Edward R. Dougherty

doi:10.1117/12.150162

20 August 1993 Morphological granulometric shape recognition in noise

Ying-Chong Cheng, Edward R. Dougherty

Proceedings Volume 2055, Intelligent Robots and Computer Vision XII: Algorithms and Techniques; (1993) https://doi.org/10.1117/12.150162
Event: Optical Tools for Manufacturing and Advanced Automation, 1993, Boston, MA, United States

Abstract

Shape classification via linear granulometric moments is examined for patterns suffering varying degrees of edge noise. It is seen that recognition is quite poor even for modest amounts of noise and remains poor even when the patterns are first filtered by a close-open filter. Recognition accuracy is greatly improved, for both unfiltered and filtered images, by employing exterior granulometries. These are constructed by applying the various linear structuring-element sequences to the corresponding linear convex hulls of the noisy patterns. The resulting granulometric distributions are then not corrupted by noise-induced probability mass at the left of the pattern spectrum, thereby greatly diminishing the detrimental effects on the pattern spectrum moments.

Citation Download Citation

Ying-Chong Cheng and Edward R. Dougherty "Morphological granulometric shape recognition in noise", Proc. SPIE 2055, Intelligent Robots and Computer Vision XII: Algorithms and Techniques, (20 August 1993); https://doi.org/10.1117/12.150162

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