Structuring element

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

Classification of hyperspectral ROSIS data from urban areas

Abstract: Classification of high-resolution hyperspectral ROSIS 03 (Reflective Optics System Imaging Spectrometer) data based on extended morphological profiles is considered. For classification of high-resolution panchromatic data, simple morphological profiles are constructed with a repeated use of morphological opening and closing operators with a structuring element of increasing size, starting with the original panchromatic image. In order to apply the morphological approach to hyperspectral data, principal components of the hyperspectral imagery are computed. The most significant principal components are used as base images for an extended morphological profile, i.e., a profile based on more than one original image. The extended morphological profiles arc used as inputs to a neural network classifier. In experiments, two hyperspectral data sets from an urban area in Pavia, Italy are classified.

Gray-scale morphology for small object detection

Abstract: In this paper, we present morphological processing using median operation for small object detection. First, we perform median morphological operation on the gray-scale image with structuring element A and make all scene regions of size equal to the central A's area or larger brighter (for bright objects) or darker (for dark objects) and other regions approximate invariably. Second, we perform median morphological operation on the gray-scale image with larger structuring element B and make all scene regions of size equal to the central B's area or smaller darker (for bright objects) or brighter (for dark objects) and other regions approximate invariably. Third, we calculate the absolute difference of above two outputs. All object regions between the smallest and largest will be enhanced and all background regions will be weakened. Then a simple threshold can extract all objects with some smaller background regions. Finally, those smaller background regions whose areas are smaller than structuring element A can be eliminated by region labeling processing. We find that if (1) contrary to background, the object regions have the signature of discontinuity with their neighbor regions. (2) Each object concentrates relatively in a small region, which can be considered as a homogeneous compact region, our algorithm can achieve satisfactory detection performance.

A Survey on Various Fabric Defect Detection Methods

Abstract: A survey on various fabric defect detection algorithms is conducted. A local homogeneity, mathematical morphology based novel fabric flaw identification algorithm is implemented. Initial phase includes the construction of a neoteric homogeneity image (H-image), from which the local homogeneity of every pixel is calculated. From the H-image we arrive at the Histogram which is required to select the suitable threshold value which results in producing the Binary image. Using the Binary image we can excerpt the convenient size and shape of the Structuring Element (SE) that is needed for mathematical morphology. In a second phase, a sequence of Morphological operations are carried out on the image with the Structuring Element in order to identify any flaws in the garments. Simulation outputs depict perfect flaw identification having false alarms to be less. Secondly, we put forward an automated image processing for identifying stripe deficiencies in circular shaped knitted cloth materials. We represent how a clearly viewable flaw can be optically embellished to upgrade human verification and how image processing based on descriptor and machine learning could be utilized to permit automatic stripe identification. Finally in this study, data sets obtained by applying local binary pattern and gray level co-occurrence matrix feature extraction methods on Tilda textile data are trained with artificial neural networks and two different models are created and success rates are compared.

Environment for the automatic manipulation and analysis of morphological expressions

Abstract: This paper describes a LISP based environment for the automatic manipulation and analysis of morphological expressions. The foundation of this environment is an aggregation of morphological knowledge that includes signal and system property information rule bases for representing morphological relationships and inferencing mechanisms for using this collection of knowledge. The layers surrounding this foundation include representations of abstract signal and structuring element classes as well as actual structuring elements implementations of the morphological operators and the ability to optimally decompose structels. The representational requirements for automatically manipulating expressions and determining the computational cost are described and the capabilities of the environment are illustrated by examples of symbolic manipulations and expression analysis.

Morphological filter based on genetic learning

Abstract: A novel method for optimal morphological filtering parameters, namely the genetic training algorithm for morphological filters (GTAMF), which adopts new crossover and mutation operators called the curved cylinder crossover and master-slave mutation, is presented in this paper Experimental results show that this method is practical, easy to extend, and improves the performances of morphological filters The operation of a morphological filer can be divided into two basic problems that include morphological operation and structuring element (SE) selection The rules for morphological operations are predefined so the filter's properties depend merely on the selection of SE By means of adaptive optimizing training, structuring elements possess the shape and structural characteristics of image targets, namely some information can be obtained by SE Morphological filters formed in this way become intelligent and can provide good filtering results and robust adaptability to image targets with clutter background