Title: Window-size determination for granulometrical structural texture classification
Authors: Jan, SR
Hsueh, YC
資訊工程學系
Department of Computer Science
Keywords: granulometry;local granulometry size distribution;window size;structural texture;texture periodicity;covariance;co-occurrence matrix
Issue Date: 1-Apr-1998
Abstract: In this paper we present a method to predict the window size when determining the local granulometry for a structural texture image set. The proposed method is based on the concept of periodicity property of structural texture images. It suggests that one may choose the minimum odd number not less than the maximum periods of texture images as a window size. (C) 1998 Elsevier Science B.V. All rights reserved.
URI: http://hdl.handle.net/11536/32716
ISSN: 0167-8655
Journal: PATTERN RECOGNITION LETTERS
Volume: 19
Issue: 5-6
Begin Page: 439
End Page: 446
Appears in Collections:Articles


Files in This Item:

  1. 000075054100007.pdf

If it is a zip file, please download the file and unzip it, then open index.html in a browser to view the full text content.