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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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Proceedings ArticleDOI
27 Jun 2016
TL;DR: This work proposes an FPGA-based intellectual property core with enhanced flexibility: it is programmable, reconfigurable, and parameterizable, and demonstrates that the core is suitable for real-time binary image processing applications.
Abstract: Binary-image processing cores are extremely useful in many image and video applications such as object recognition, tracking, motion detection, and identification. To address the variety of applications and binary-image kernels, we propose an FPGA-based intellectual property core with enhanced flexibility: it is programmable, reconfigurable, and parameterizable. The core performs single binary image kernels (morphological operations) and even complete algorithms composed by sequences of operations; the algorithms' control does not require an external processor as in previous approaches. The reconfiguration features allow adapting the image size, structuring element, and even image parallelism for some operations at run-time. Finally, the parameterization allows defining the maximum image, feature, and command-buffer sizes as well as the number of pixel processing units at compile time. The careful combination of programmability, reconfigurability, and parameterization produces a flexible yet efficient binary-image processing architecture. A detailed experimental validation using a Virtex 5 platform assesses the advantages of the proposed architecture versus previous approaches. The results show that the core can process about 1500 frames per second for 32 operations for a 1024 × 1024 image and 5×5 structure-element at 100MHz frequency. The results demonstrate that the core is suitable for real-time binary image processing applications.

2 citations

Journal ArticleDOI
25 Jan 2019
TL;DR: This article presents a method of QRS complex detection and more precisely the R wave in an electrocardiogram (ECG) based on the mathematics morphology which calls upon the four operators’ morphology, erosion, dilation, opening and closing.
Abstract: This article presents a method of QRS complex detection and more precisely the R wave in an electrocardiogram (ECG) based on the mathematics morphology which calls upon the four operators’ morphology, erosion, dilation, opening and closing. These operators are combined with a window relocated which is called the structuring element. Morphological filtering uses the structuring element to extract the shape information from ECG signal. The effectiveness of the proposed algorithm is tested by using recordings obtained from the MIT-BIH arrhythmia database. Experiment results show that the proposed algorithm outperforms the other algorithms.

2 citations

Journal ArticleDOI
TL;DR: Pore and grain regions were separated via thresholding techniques from sandstone images and a mathematical morphology‐based framework was followed to pack the random pore space with overlapping and nonoverlapping disks of various shapes and sizes.
Abstract: Summary Pore and grain regions were separated via thresholding techniques from sandstone images. A mathematical morphologybased framework was followed to pack the random pore space with overlapping and nonoverlapping disks. This framework has several advantages in implementation and is generally applicable to multiscale images. The random pore space was reconstructed in two ways from the minimum morphological information through: (a) overlapping and (b) nonoverlapping disks of various shapes and sizes. The structuring elements employed to carry out this analysis included octagon, square and rhombus templates. The results achieved through these two types of reconstruction of sandstone pores are compared. These results provided the basis on which to test the accuracy of these techniques. Reconstruction recovery was tested by computing shapiness indices for the reconstructed pores achieved through the two methods.

2 citations

Book
23 Jan 2019
TL;DR: This project implements vHGW algorithm for erosion and dilation independent of structuring element size on CUDA programming environment with GPU hardware as GeForce GTX 480 and shows maximum performance gain of 20 times than the conventional serial implementation of algorithm in terms of execution time.
Abstract: A mathematical morphology is used as a tool for extracting image components that are useful in the representation and description of region shape. The mathematical morphology operations of dilation, erosion, opening, and closing are important building blocks of many other image processing algorithms. The data parallel programming provides an opportunity for performance acceleration using highly parallel processors such as GPU. NVIDIA CUDA architecture offers relatively inexpensive and powerful framework for performing these operations. However the generic morphological erosion and dilation operation in CUDA NPP library is relatively naive, but it provides impressive speed ups only for a limited range of structuring element sizes. The vHGW algorithm is one of the fastest for computing morphological operations on a serial CPU. This algorithm is compute intensive and can be accelerated with the help of GPU. This project implements vHGW algorithm for erosion and dilation independent of structuring element size has been implemented for different types of structuring elements of an arbitrary length and along arbitrary angle on CUDA programming environment with GPU hardware as GeForce GTX 480. The results show maximum performance gain of 20 times than the conventional serial implementation of algorithm in terms of execution time.

2 citations

Proceedings ArticleDOI
31 Mar 2009
TL;DR: Two typical experiments using the theory of GS and EMF show that the novel method can extract information of important components from complex image efficiently and the advantage is given out comparing to the traditional structuring element and morphological filtering.
Abstract: Structuring element and morphological filtering are important methods to process and analyze images. The conception and character of generalized structuring element (GS) and expended morphological filtering (EMF) are presented. The advantage of GS and EMF is given out comparing to the traditional structuring element and morphological filtering. Two typical experiments using the theory of GS and EMF show that the novel method can extract information of important components from complex image efficiently.

2 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20236
202214
202112
202019
201929
201824