Structuring element

About: Structuring element is a research topic. Over the lifetime, 997 publications have been published within this topic receiving 26839 citations.

Implementing Mathematical Morphology Techniques to Detect Cracks in Underground Pipeline Images Automatically

Abstract: An oil pipe buried under needs to be checked for their current quality before oil transportation takes place through it. The method involved for all these days were manual using “PIGS”. The manual work done by a human operator was hectic, to overcome this situation, a computerized automated image processing technique is introduced through this paper, where the image analysis or pattern analysis is evaluated using the Mathematical Morphology. Mathematical Morphology accomplishes to detect the cracks using Set Theory and also Curvature evaluation for segment images with respect to a precise geometric model to define crack like patterns. This paper describes the method, background of the theory discussed and evaluation of the theory used to identify the defects. Based on the Mathematical Theories of sets and topological notations, its principle lies in studying the Morphological properties (Shape, Size, Orientation and other forms) of the object(Patterns) through non-linear transformations associated with a reference object(SE-Structuring Element).At the end of this paper, image processing to detect the cracks is achieved.

Skew detection in document images

Abstract: of EP1394725Skew angle in a document image (A) is estimated using operators known from mathematical morphology. Skew angle in a document image (A) is estimated by run-length smoothing the image and then producing a plurality of eroded run-length-smoothed images. The run-length-smoothed image (RLSA(A)) is eroded using a linear structuring element (k2L alpha ) oriented at each of a plurality of different angles ( alpha ). The angle of the linear structuring element which produces an eroded image having the greatest surface area is designated as the skew angle. A plurality of run-length-smoothed images (RLSA alpha (A)) may be produced, each generated by smoothing the document image using a linear structuring element (k1L alpha ) oriented at a respective different angle ( alpha i). Then each run-length smoothed image (RLSA alpha (A)) is eroded using a linear structuring element oriented at the corresponding angle ( alpha i).

Fast implementation of large erosions and dilations in Mamba

Abstract: This document explains how to implement fast erosions and dilations when large structuring elements are needed. These structuring elements can be squares, hexagons, octogons or dodecagons. This implementation, realized in the Mamba library brings a dramatic increase of the computation speed. This increase is all the more important as the size of the structuring element is large.

Zonohedral Approximation of Spherical Structuring Element for Volumetric Morphology

Abstract: Performing dilation and erosion using large structuring elements can be computationally slow – a problem especially pronounced when processing volumetric data. To reduce the computational complexity of dilation/erosion using spherical structuring elements, we propose a method for approximating a sphere with a zonohedron. Since zonohedra can be created via successive dilations/erosions of line segments, this allows morphological operations to be performed in constant time per voxel. As the complexity of commonly used methods typically scales with the size of the structuring element, our method significantly improves the run time. We use the proposed approximation to detect large spherical objects in volumetric data. Results are compared with other image analysis frameworks demonstrating constant run time and significant performance gains.

Automatic Brain Tumor Segmentation Using Multi-OTSU Thresholding and Morphological Reconstruction

Abstract: Images segmentation aims to divide an image into several segments. They can be selected according to the composition of the region of interest, the types of tissues, and the functional zones [1]. In this paper, we present a new segmentation method, based on the multi-thresholding method and morphological reconstruction for brain tumor separation from Magnetic Resonance Imaging (MRI). Firstly, we use a pre-processing to enhance image contrast and quality by intensity adjustment. Secondly, the improved image is segmented using the multi-Otsu method and finally, a morphological reconstruction was performed with the appropriate structuring element parameter on the segmented image to determine the tumor. A comparison with some state-of-the-art algorithms demonstrated the efficiency of the proposed method with regard to accuracy.