Structuring element

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

Image Regularization Using Total Variation and Morphological Gradient Priors with Optimization of Structuring Element

Abstract: As an image prior for image restoration, the sum of morphological gradients for an image has previously been proposed. Optimization of the structuring element (SE) used for this morphological gradient using a genetic algorithm (GA) has also been proposed. This method uses the minimized value of the objective function of the restoration problem as the fitness of the GA. However, this value does not necessarily coincide with an objective evaluation such as the mean square error. Therefore, in this paper, we formulate the objective function using the morphological gradient and total variation as a new image prior for an image restoration problem. The proposed objective function makes it possible to almost match the fitness to the objective evaluation and can improve the restoration accuracy. It also solves the problem of the artifact due to the unsuitability of the SE for the image. An experiment shows the effectiveness of the proposed image restoration method.

Topology Based Corner Detection

Abstract: A new corner detection algorithm based on the topological median filter is proposed. Topological quasi dilation and erosion operators with circular structuring element are used to detect the corners on gray level images.

Wear Particles Image Segmentation Method Based on Multiscale Mathematical Morphology

Abstract: Image segmentation plays an important role in wear particles analysis. A new segmentation method based on multiscale mathematical morphology is proposed for wear particles image segmentation. The newly introduced method employs different scale structuring elements to detect the image edge, the final edge is calculated by the weighted average method. Edge details can be remained by small scale structuring element (SE) and noise can be depressed effectively by large scale SE, therefore, the new method has great effect in edge accuracy, strong and weak edge extraction and noise suppression. The efficiency of the method is evaluated by a set of wear particles images. The comparison with the single scale SE and other traditional methods demonstrates the improvement of the new algorithm.

Fast Binary Dilation/Erosion Algorithm Using Kernel Subdivision

Abstract: Numerous algorithms have been proposed in the literature to speed up dilation/erosion operations. The motivation has been to reduce computational complexity by exploiting the structuring element and the image object properties. This paper presents a new algorithm for binary morphological dilation and erosion called the Kernel Sub-Division algorithm and discusses its implementation in the two dimensional case. It decomposes the n-dimensional structuring element, into several subsets and operates on the object contours in the image. The image characteristics are exploited by subdividing the object contours into bins while performing contour processing. The elegance of the algorithm lies in its retaining the correspondence to the output of the classical implementation with massive speed gain. The results of the algorithm on a statistically significant test set of images, showed that it performed five times better than the classical implementation for a 3x3 kernel. It also demonstrated a marginal rise in execution time with increasing size of the kernel.

Multi FPGA processor for gray-scale morphology

Abstract: Local operations in image processing are often used, namely during the preprocessing step. On one hand, their implementation is expensive, on the other hand, they become efficient when they use a wide area neigbourhood. In this paper we propose a general method for synthesis of linear and non linear filters (mathematical morphology operators). The main interest of this method is that it can be applied to various types of filters: separable or not, factorizable or not and for any size of the filter kernel (convolution) or the structuring element (mathematical morphology). Prom this method, one can get an architecture in a straightforward way. We propose a re-configurable architecture, based upon reprogrammable circuits (FPGA: Field Programmable Gate Array). This multi FPGA architecture allows a real time implementation of any r × c size kernel (processing at video rate).