DocumentCode :
1427154
Title :
Use of the two-dimensional Radon transform to generate a taxonomy of shape for the characterization of abrasive powder particles
Author :
Leavers, Violet F.
Author_Institution :
Sch. of Eng., Manchester Univ., UK
Volume :
22
Issue :
12
fYear :
2000
fDate :
12/1/2000 12:00:00 AM
Firstpage :
1411
Lastpage :
1423
Abstract :
An image processing technique for the extraction of parameters characteristic of the shape and angularity of abrasive powder particles is proposed. The image data are not analyzed directly. Information concerning angularity and shape is extracted from the parametric transformation of the 2D binarized edge map. The transformation process used, the Radon transform, is one to many, that is, each image point generates in transform space the parameters of all the possible curves on which it may lie and the resulting distribution is an accumulation of that evidence. Once the image data are segmented, the technique has the potential to deliver a comprehensive numerical description of the shape and angularity of the particles under investigation without the need for further interaction by the operator. The parameters obtained are arranged into a taxonomy according to their usefulness in categorizing the shapes under inspection. The technique is novel in that it offers an analytical definition of a corner and its apex and it automatically selects only those protrusions coincident with the convex hull of the shape and, hence, those most likely to contribute to the process of abrasion. The advantages and potential pitfalls of using the technique are illustrated and discussed using real image data
Keywords :
Radon transforms; image segmentation; 2D binarized edge map; abrasive powder particles; angularity; comprehensive numerical description; convex hull; parametric transformation; shape; two-dimensional Radon transform; Abrasives; Data analysis; Data mining; Image analysis; Image generation; Image processing; Image segmentation; Powders; Shape; Taxonomy;
fLanguage :
English
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publisher :
ieee
ISSN :
0162-8828
Type :
jour
DOI :
10.1109/34.895975
Filename :
895975
Link To Document :
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