Structuring element

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

Iris image digital processing method for use in iris recognition system, involves enhancing contrast enhancement comprising linear piecewise stretching to adjust histogram of grey levels of image

Abstract: The method involves filtering an image with a structuring element corresponding to a disk with a diameter approximately corresponding to a thickness of an eyelash. A structuring element corresponding to a disk having a radius corresponding to a minimum expected radius of a pupil in the image is opened. A contrast enhancement comprising a linear piecewise stretching is enhanced to adjust histogram of grey levels of the image within a bounded interval, defined by the minimum and maximum intensity values desired in the final image. An independent claim is also included for a system for recognizing an iris of an eye.

Improved one-dimensional dilation-based top-hat algorithm for star segmentation under complicated background conditions.

Abstract: The white top-hat transformation has been widely used in small bright target extraction. It usually applies an erosion operation to remove the target and then a dilation operation to recover the intensity of the processed image. A bright target will be extracted by subtracting the opening operation (erosion followed by dilation) from the raw image. The drawback of this method is that its denoising ability is poor because the estimated background threshold by an opening operation is smaller than the raw image. This study puts forward the viewpoint that by use of a proposed one-dimensional (1D) symmetrical line-shaped structuring element a bright target can also be removed by the dilation operation. Consequently, the white top-hat transformation can be implemented by subtracting only the dilation operation from the raw image. To the best knowledge of the authors, it is the first time to use this method to achieve the top-hat transformation. The simulation experiment shows that the proposed 1D top-hat algorithm has excellent performance in denoising ability and detection ability. Moreover, real night experiments demonstrate that our proposed algorithm can work reliably under both complicated background conditions and good weather conditions. It is noticeable that the performance of computational efficiency and resource consumption have been considerably improved because a 1D structuring element is employed and the erosion operation is not included.

Mathematical Morphology

Abstract: This chapter describes the basic operations and applications of mathematical morphology, which is based on lattice theory and random geometry to study geometric structures. It defines the basic operators such as dilation and erosion and structuring elements of binary images and grayscale images. The commonly used image preprocessing strategies are image filtering and image reconstruction. The chapter describes the basic knowledge of morphological reconstruction as well as the basic operations of morphological reconstruction by introducing geodesic erosion, geodesic dilation, and their applications. It also describes the simplest yet effective segmentation algorithm, namely the watershed transform. The chapter introduces the commonly used watershed transforms based on distance, gradient, and control markers. It shows how to extend the mathematical morphology to the color space and then completed the basic operations such as the dilation and erosion in color space.

Morphological Sampling Theorem and its Extension to Grey-value Images

Abstract: Sampling is a basic operation in image processing. In classic literature, a morphological sampling theorem has been established, which shows how sampling interacts by morphological operations with image reconstruction. Many aspects of morphological sampling have been investigated for binary images, but only some of them have been explored for grey-value imagery. With this paper, we make a step towards completion of this open matter. By relying on the umbra notion, we show how to transfer classic theorems in binary morphology about the interaction of sampling with the fundamental morphological operations dilation, erosion, opening and closing, to the grey-value setting. In doing this we also extend the theory relating the morphological operations and corresponding reconstructions to use of non-flat structuring elements. We illustrate the theoretical developments at hand of examples.

Image Processing on Fuzzy Near Sets

Abstract: Fuzzy image processing involves various fuzzy approaches. These fuzzy approaches help in understanding and processing of images as fuzzy sets. Mathematical morphology is used for analysing the shapes and forms of the objects in images. In morphological methods structuring elements are applied to an input image which creates an output image of the same size. The size and shape of the neighbourhood is chosen in such a way that a morphological operation relating to different shapes in the input image is constructed. In this paper the pixel values of an image are converted to fuzzy near membership values and the corresponding image is found. Using the fuzzy near membership image, gray and binary images are formed using MATLAB. Mathematical morphology operations dilation, erosion, opening and closing for different structuring elements such as line, square, diamond, sphere, disk, cube, neighbourhood, octagon and rectangle are performed for fuzzy membership and binary images. Taking dilation and opening as upper approximations, erosion and closing as lower approximations, the exact measures are calculated for different structuring elements and the corresponding images formed. It is found that the exact measure for the structuring element neighbourhood of fuzzy near membership and binary images are exactly equal. The corresponding images are also exactly similar.