Topic
Structuring element
About: Structuring element is a research topic. Over the lifetime, 997 publications have been published within this topic receiving 26839 citations.
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16 Sep 1996
TL;DR: This paper presents some results to decompose circular structuring elements into 3/spl times/3 elements, and gives a hexadecagon that can be decomposed, and which optimally fits a disk.
Abstract: This paper presents some results to decompose circular structuring elements into 3/spl times/3 elements. Decomposition allows one to improve the expended time in computing morphological operations. Generally, the shape of the structuring element determines the image transformation. Morphological operations with disks can be used as shape and size descriptors. The optimal discrete approximation of a disk can not be decomposed into 3/spl times/3 factors. Therefore, for a given radius, we give a hexadecagon that can be decomposed, and which optimally fits a disk. Afterwards, we present the decomposition of the disk in terms of the hexadecagon parameters. The decomposition prime factors can be given by different families of basic structuring elements.
2 citations
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06 Sep 1989TL;DR: In this article, a morphological approach to scale-space filtering has been developed, which nonlinearly smooth the image without blurring the features (edges) have been used.
Abstract: Summary form only given. Multidimensional operators based on mathematical morphology have been proposed for image segmentation. Mathematical morphology is basically a set theory. It provides the concept of a structuring element to probe the image with arbitrary geometric patterns, in order to capture the topological properties of the image. The classical operators have been extended to multidimensions. A morphological approach to scale-space filtering has been developed. Multiscale morphological openings that nonlinearly smooth the image without blurring the features (edges) have been used. The approach has been formulated within the framework of alternating sequential filters (ASF). >
2 citations
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01 Oct 2019TL;DR: This paper discusses about topology preserving 3-D skeletonization sequential algorithm which computes curve skeletons for the given solid object, which does not require any computations which are complex, large number of windows which make implementation a complex.
Abstract: For shape analysis in 3-D, probably skeletonization will become a momentous tool for analyzing the different shapes in 3-D, as similar to the 2-D. This paper discusses about topology preserving 3-D skeletonization sequential algorithm which computes curve skeletons for the given solid object. The original geometry of the given object will be well-preserved by curve skeleton. In the proposed algorithm, each voxel of the three-dimensional image is classified as two classes: boundary, corner. The object is scanned with the use of a structuring element, border and corner voxels are classified based on the certain condition proposed in this paper. This algorithm does not require any computations which are complex, large number of windows which make implementation a complex. The proposed algorithm is various 3-D simulated images and resulting skeletons are found to satisfactory. The 3-D simulated images and its skeletons are viewed with the help of ImageJ software.
2 citations
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14 Oct 1996
TL;DR: This is the first attempt in which tabu search has been used in computer vision, and TS is a flexible framework of a variety of strategies originating from artificial intelligence and is therefore open to further improvement.
Abstract: In this paper, we propose a novel method for extracting the optimal structure element for MST-based shape description. Specifically, we use tabu search to solve the optimal structure element extraction problem for morphological signature transform (MST)-based shape description. To the best of our knowledge, this is the first attempt in which tabu search has been used in computer vision. Our tabu search (TS) has a number of advantages: (1) TS avoids entrapment in local minima and continues the search to give a near-optimal final solution; (2) TS is very general and conceptually much simpler than either simulated annealing or genetic algorithms; (3) TS is very easy to implement and the entire procedure only occupies a few lines of code; (4) TS is a flexible framework of a variety of strategies originating from artificial intelligence and is therefore open to further improvement.
2 citations
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TL;DR: A novel morphological edge detection using adaptive weighted morphological operators is presented that utilizes a set of SEs to detect the edge strength with a view to decrease the spurious detail edge and suppressed the noise.
Abstract: In this paper, a novel morphological edge detection using adaptive weighted morphological operators is presented. The newly introduced operators employ weighted structuring element (SE) and apply multiplication or division in place of addition and subtraction in classical morphological operations. It judges its edge and its direction by means of training method and differentiable equivalent representations for the operators, efficient adaptive algorithms to optimize SEs are derived. The gradient of the adaptive weighted morphology utilizes a set of SEs to detect the edge strength with a view to decrease the spurious detail edge and suppressed the noise. Results will be presenting for images in comparison with the others edging detectors.
2 citations