Structuring element

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

Image Segmentation using Mathematical Morphology : A Study

Abstract: Mathematical morphology deals with the mathematical theory of describing shapes using sets. It is a nonlinear image processing method for the analysis and processing of geometrical structures. Mathematical morphology has been widely used for many applications of image processing and analysis. In image processing, it is used to investigate the interaction between an image and a certain chosen structuring element. Morphological techniques include erosion and dilation, opening and closing, outlining, and thinning and skeletonization. Mathematical morphology can be used for edge detection, image segmentation, noise elimination, feature extraction and other image processing problems. In this paper we are going to discuss document image segmentation using morphological operations. Keywords: Morphology, Dilation, Erosion, Opening, Closing

Map automatic scale reduction by means of mathematical morphology

Abstract: This paper deals with the use of mathematical morphology for the automatic smoothing of contours in the plotting of digital maps. Due to scale reduction this operation is normally performed by human experts, since it is essential not to flood the map with information and, at the same time, not to miss any important feature. If a regular zoom out is used, one has no control over the results. In digital cartography scale reduction needs to be made automatically, but with a certain degree of control over the results. The mathematical morphology operators of dilation and erosion permit scale reduction in a controlled way if an appropriate structuring element (shape and size) is chosen. Using a simple IBM-PC-XT based image processing system (SITIM) it was possible to test this method for two real cases, with promising results.

Adaptive algorithm based on morphological top-hat transformation to segment Chinese square seals in bank checks

Abstract: To segment a binary seal image out of a check image without distortion,an adaptive morphological segmentation algorithm based on a top-hat transformation is proposed to accurately extract a binary Chinese square seal from a bank check.A gray scale square seal is extracted from a coloured bank check according to the colour information.Different Chinese characters have different stroke features and background evenness.To respectively process each character in the square seal,the seal is divided into four sub-squares.Then,the background across each sub-square of the grey scale seal image is smoothed by the top-hat transformation.The size of the structuring element in the top-hat transformation can have a significant influence on the segmentation.So an adaptive method is proposed to iteratively estimate the proper size of the structuring element according to the local foreground area.In each sub-square,the optimal size of the structuring elements,respectively,for the imprint frame and the character are estimated.With their optimal structuring elements,the character and the imprint frame in each sub-square are filtered by the top-hat transform and binarized by the Otsu's method.The experimental result shows that when 350 different square seals are segmented in bank checks,only 2 segmented seals have distortions.It is proved that the proposed algorithm can correctly segment Chinese characters with intricate and dense strokes in a bank check square seal.Adhesion and incompleteness distortions in the segmentation results are reduced,even when the original square seal has poor quality.

Radar target classification using morphological image processing

Abstract: Morphological operators are commonly used in image processing. We study their suitability for use in synthetic aperture radar (SAR) image enhancement and target classification. Morphological operations are nonlinear operators defined by set theory. The dilation and erosion operations grow or shrinkimage features that match to a predefined structuring element. The opening and closing operations are combinations of successive dilation and erosion. These morphological operations can visually emphasize scattering of interest in an image. We investigate whether these operations can also improve target classification performance. The operators are nonlinear and image dependent; thus we cannot predict performance without empirical testing. We test and evaluate the morphological operators using simulated and measured SAR data. Results show the dilation operator is most promising for increasing match score and separation between classes in the decision space.

Performance and time requirement analysis of top-hat transform based small target detection algorithms

Abstract: Top-Hat transform is well known background suppression method used in small target detection. In this paper, we investigate various different Top-Hat transformation based small target detection approaches. All of the methods are implemented with their best parameter settings and applied to the same test image. The comparison among them is done in terms of three issues: 1. the detection performance (precision and false alarm rate), 2. the time requirement of the method and its usability for real time applications, 3. the number of parameters, which need user interaction. Results show that all of the algorithms require a prior knowledge of target size, which is either used as the structuring element size or as the threshold for post-processing. Algorithms, which use automatic approaches to select its parameters, are not generic to be applied to various images. But algorithms, which use adaptive methods for deciding on the threshold value, perform better than the others.