Primitive and point configuration texture model and primitive estimation using mathematical morphology

Authors:
Akira Asano;Takeshi Ohkubo;Mitsuji Muneyasu;Takao Hinamoto
Affiliations:
Hiroshima University, Higashi-Hiroshima, Hiroshima, Japan;Hiroshima University, Higashi-Hiroshima, Hiroshima, Japan;Kansai University, Suita, Osaka, Japan;Hiroshima University, Higashi-Hiroshima, Hiroshima, Japan
Venue:
SCIA'03 Proceedings of the 13th Scandinavian conference on Image analysis
Year:
2003

Citing 5
Cited 2

Pattern Spectrum and Multiscale Shape Representation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Texture Modeling by Optimal Gray Scale Structuring Elements Using Morphological Pattern Spectrum

ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 3
Image Analysis and Mathematical Morphology

Image Analysis and Mathematical Morphology
Morphological texture analysis using optimization of structuring elements

Proceedings of the 11th international conference on Theoretical foundations of computer vision
Modification of spatial distribution in primitive and point configuration texture model

SCIA'03 Proceedings of the 13th Scandinavian conference on Image analysis

Improving the accuracy of isotropic granulometries

Pattern Recognition Letters
Primitive and grain estimation using flexible magnification for a morphological texture model

ISMM'11 Proceedings of the 10th international conference on Mathematical morphology and its applications to image and signal processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

A model for texture description, called "Primitive and Point Configuration (PPC) texture model," and an estimation method of the primitive, which is an elementary object for configuring a texture, are proposed in this paper. The PPC texture model regards that a texture is composed by arranging grains that are derived from one or a few primitives by some modification. The primitive shape is estimated by the principle that the primitive resembling the grains best should be the optimal estimation. This estimation is achieved by finding the structuring element that minimizes the integral of the size distribution function of a target texture.