Primitive and Point Configuration Texture Model and Primitive Estimation Using Mathematical Morphology.
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.
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Conference Proceeding: Mathematical morphology in image analysis[show abstract] [hide abstract]
ABSTRACT: Mathematical morphology provides an effective approach to the analyzing of digital images. Basic operations in mathematical morphology are erosion, dilation, opening and closing. Morphological filters are based on the theory of mathematical morphology. This filters exploit geometric rather than analytic features of signals. The advantages of the morphological over linear filtering are direct geometric interpretations, simplicity and efficiency in hardware implementation. Subband decomposition is a procedure of filtering digital image source into a desired number of nonoverlapping frequency bands. Then, each band can be decimated and coded efficiently for data transmission. In this paper morphological filters are used for image decomposition. The image is represented by subband and Laplacian pyramid. The original image can be reconstructed from the subbands. Some image examples are presented to show the effectiveness of this approach.Conference of applied mathematics, PRIM; 06/1996
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ABSTRACT: Proposes a texture modelling method based on the pattern spectrum. The pattern spectrum is a mathematical morphological method to describe the size distribution of objects contained in an image. Our method is based on the idea of obtaining a model of the elementary particles that form a texture by optimizing a gray scale structuring element to fit the shape of elementary particles. The optimization method is applied in two stages: the first stage optimizes the extent of the structuring element and the second optimizes the pixel values in the extentPattern Recognition, 2000. Proceedings. 15th International Conference on; 02/2000
Conference Proceeding: Morphological Texture Analysis Using Optimization of Structuring Elements.[show abstract] [hide abstract]
ABSTRACT: This paper proposes a method of texture analysis using morphological size distribution. Our framework is based on the concept that a texture is described by estimation of primitive, size distribution of grains derived from the primitive, and spatial distribution of the grains. We concentrate on estimation of primitive using an assumption on grain size distribution. We assume a model that grains are derived from one primitive, and a uniform size distribution since we consider target textures containing grains of various sizes. Thus the structuring element used for the measurement of size distribution is optimized to obtain the most uniform size density function. The optimized structuring element is an estimate of the primitive under the assumption. Simulated annealing algorithm is employed for the optimization.Geometry, Morphology, and Computational Imaging, 11th International Workshop on Theoretical Foundations of Computer Vision Dagstuhl Castle, Germany, April 7-12, 2002, Revised Papers; 01/2002