Structuring element

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

Fault Diagnosis of Rolling Element Bearing Using an Adaptive Multiscale Enhanced Combination Gradient Morphological Filter

Abstract: The extraction of the vibration impulse signal plays a crucial role in the fault diagnosis of rolling element bearing. However, the detection of weak fault signals generally suffers the strong background noise. To solve this problem, a new adaptive multiscale enhanced combination gradient morphological filter (MECGMF) is proposed for the fault diagnosis of rolling element bearing. In this method, according to the filtering ability of four basic morphological filter operators, an enhanced combination gradient morphological operation (ECGMF) is first proposed. This design enhances the ability of MECGMF to extract impulse signals from strong background noise. And accordingly, a new adaptive selection strategy named kurtosis fault feature ratio (KFFR) is proposed to select an optimal structuring element (SE) scale. Subsequently, the optimal SE scale is the largest measure of multiscale morphological filtering for extracting bearing fault information. In the meanwhile, the effectiveness of the proposed method is verified by simulation and experiment. Finally, the experimental results demonstrate that MECGMF can effectively restrain the noise interference and extract fault characteristic signals of rolling element bearing from strong background noise. Moreover, comparative tests show that the proposed method is more effective in detecting wind turbine bearing failures.

Mathematical morphology for tensor data induced by the Loewner orderingin higher dimensions

Abstract: Positive semidefinite matrix fields are becoming increasingly important in digital imaging. One reason for this tendency consists of the introduction of diffusion tensor magnetic resonance imaging (DTMRI). In order to perform shape analysis, enhancement or segmentation of such tensor fields, appropriate image processing tools must be developed. This paper extends fundamental morphological operations to the matrix-valued setting. We start by presenting novel definitions for the maximum and minimum of a set of matrices since these notions lie at the heart of the morphological operations. In contrast to naive approaches like the component-wise maximum or minimum of the matrix channels, our approach is based on the Loewner ordering for symmetric matrices. The notions of maximum and minimum deduced from this partial ordering satisfy desirable properties such as rotation invariance, preservation of positive semidefiniteness, and continuous dependence on the input data. We introduce erosion, dilation, opening, closing, top hats, morphological derivatives, shock filters, and mid-range filters for positive semidefinite matrix-valued images. These morphological operations incorporate information simultaneously from all matrix channels rather than treating them independently. Experiments on DT-MRI images with ball- and rod-shaped structuring elements illustrate the properties and performance of our morphological operators for matrix-valued data.

Generic Ribbons: A Morphological Approach Towards Natural Shape Decomposition

Abstract: In this paper, we propose a shape decomposition scheme which decomposes complex shapes into their natural components. Each component is represented by a shape primitives called generic ribbon. A generic ribbon is defined by sweeping a structuring element along a specified trajectory which is decomposed from the morphological skeleton of the shape through the skeleton decomposition. Five decomposition rules are devised to facilitate a natural decomposition. The decomposed generic ribbons form a hierarchical representation in which each component is described by a feature vector placed at a certain level according to its significance in the representation. The hierarchical structure makes the representation reliable and simplifies the matching process during recognition.

A Lattice-Based Approach to Mathematical Morphology for Greyscale and Colour Images

Abstract: Mathematical morphology (MM) is a theory for the analysis of spatial structures, based on set-theoretical notions and on the concept of translation. MM has many applications in image analysis such as edge detection, noise removal, object recognition, pattern recognition and image segmentation in a.o. geosciences, materials science, the biological and medical world [13, 15]. MM was originally developed for binary images only. The basic tools of MM are the morphological operators, which transform an image A we want to analyse, using a structuring element B into a new image P(A, B) in order to obtain additional information about the objects in A like shape, size, orientation, image measurements. Apart from the threshold and umbra approach, binary morphology can be extended to morphology for greyscale images using fuzzy set theory, called fuzzy morphology. In this work we will present a new vector-based approach for the extension of MM for greyscale images to colour morphology. We will extend the basic morphological operators dilation and erosion based on the threshold and fuzzy set approach to colour images. Finally in the last section we illustrate an image denoising method using MM to reduce stripes' artefacts in satellite images.

Automatic Detection of Orientation of Mapped Units via Directional Granulometric Analysis

Abstract: Automatic detection of orientation of mapped units via directional granulometries is addressed in this letter. A flat symmetric structuring element (B) of size 3 × 3 with nine elements, which is a disk in eight-connectivity grid, is decomposed into four 1-D directional structuring elements (Bis). Multiscale opening transformations are performed on each mapped unit with respect to these four directional structuring elements to eventually compute direction-specific morphologic entropy values. Based on these values, the orientations of mapped units are classified into four classes that include those units with orientations of: i) South East-North West (B1), ii) North-South (B2), iii) South West-North East (B3), and iv) East-West (B4). We demonstrated this approach on five model objects, and nine major river basins extracted from DEM of Indian peninsular. This approach yields quantitative results, based on which the mapped units could be automatically classified into four different orientations.