Window-size determination for granulometrical structural texture classification

Authors:
Sen-Ren Jan;Yuang-Cheh Hsueh
Affiliations:
-;-
Venue:
Pattern Recognition Letters
Year:
1998

Citing 3
Cited 3

Morphological methods in image and signal processing

Morphological methods in image and signal processing
Detecting texture periodicity from the co-occurrence matrix

Pattern Recognition Letters
Image Analysis and Mathematical Morphology

Image Analysis and Mathematical Morphology

Homogeneity cues for texel size estimation of periodic and near-periodic textures

MCPR'11 Proceedings of the Third Mexican conference on Pattern recognition
Fast texel size estimation in visual texture using homogeneity cues

Pattern Recognition Letters
Scale detection via keypoint density maps in regular or near-regular textures

Pattern Recognition Letters

Quantified Score

Hi-index	0.10

Visualization

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.