Interactive Algorithms in Complex Image Processing Systems Based on Big Data

In the era of big data, images and videos are one of the main means of information dissemination. In recent years, research on the problem of image and video reorganization and integration has become a hot topic in digital image processing technology. Using a computer for image processing, complicat...

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Main Authors: Yuanjin Xu, Xiaojun Liu
Format: Article
Language:English
Published: Hindawi-Wiley 2020-01-01
Series:Complexity
Online Access:http://dx.doi.org/10.1155/2020/5929584
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spelling doaj-a8ec09364c034aedabdec480b211aeb62020-11-25T02:40:06ZengHindawi-WileyComplexity1076-27871099-05262020-01-01202010.1155/2020/59295845929584Interactive Algorithms in Complex Image Processing Systems Based on Big DataYuanjin Xu0Xiaojun Liu1Institute of Mathematical Geology and Remote Sensing Geology, School of Earth Resources, China University of Geosciences, 388 Lumo Road, Wuhan 430074, ChinaSchool of Electromechanical and Automobile Engineering, Huanggang Normal University, Huanggang 438000, Hubei, ChinaIn the era of big data, images and videos are one of the main means of information dissemination. In recent years, research on the problem of image and video reorganization and integration has become a hot topic in digital image processing technology. Using a computer for image processing, complicated programming is unavoidable. Therefore, it is necessary to optimize the interactive algorithms for image processing. In this paper, the content of image processing experiment is screened and integrated, and an image processing experiment system based on Matlab GUI platform is established for different levels of image processing knowledge modules. In order to verify the effectiveness and practicability of the optimization algorithm proposed in this paper, experimental simulations were performed on complex natural images and complex human eye images. The speed of the USB camera is generally between 15 frames/second and 25 frames/second, and in a 240 × 320 picture, the interactive algorithm in this article only needs about 59 ms, which is enough to complete the automatic interaction in the video in real time, which is convenient for subsequent image extraction. The experimental results show that the interactive algorithm in the complex image processing system in this paper optimizes the image extraction rate and improves the antinoise performance of the segmentation and the segmentation effect of the deep depression region.http://dx.doi.org/10.1155/2020/5929584
collection DOAJ
language English
format Article
sources DOAJ
author Yuanjin Xu
Xiaojun Liu
spellingShingle Yuanjin Xu
Xiaojun Liu
Interactive Algorithms in Complex Image Processing Systems Based on Big Data
Complexity
author_facet Yuanjin Xu
Xiaojun Liu
author_sort Yuanjin Xu
title Interactive Algorithms in Complex Image Processing Systems Based on Big Data
title_short Interactive Algorithms in Complex Image Processing Systems Based on Big Data
title_full Interactive Algorithms in Complex Image Processing Systems Based on Big Data
title_fullStr Interactive Algorithms in Complex Image Processing Systems Based on Big Data
title_full_unstemmed Interactive Algorithms in Complex Image Processing Systems Based on Big Data
title_sort interactive algorithms in complex image processing systems based on big data
publisher Hindawi-Wiley
series Complexity
issn 1076-2787
1099-0526
publishDate 2020-01-01
description In the era of big data, images and videos are one of the main means of information dissemination. In recent years, research on the problem of image and video reorganization and integration has become a hot topic in digital image processing technology. Using a computer for image processing, complicated programming is unavoidable. Therefore, it is necessary to optimize the interactive algorithms for image processing. In this paper, the content of image processing experiment is screened and integrated, and an image processing experiment system based on Matlab GUI platform is established for different levels of image processing knowledge modules. In order to verify the effectiveness and practicability of the optimization algorithm proposed in this paper, experimental simulations were performed on complex natural images and complex human eye images. The speed of the USB camera is generally between 15 frames/second and 25 frames/second, and in a 240 × 320 picture, the interactive algorithm in this article only needs about 59 ms, which is enough to complete the automatic interaction in the video in real time, which is convenient for subsequent image extraction. The experimental results show that the interactive algorithm in the complex image processing system in this paper optimizes the image extraction rate and improves the antinoise performance of the segmentation and the segmentation effect of the deep depression region.
url http://dx.doi.org/10.1155/2020/5929584
work_keys_str_mv AT yuanjinxu interactivealgorithmsincompleximageprocessingsystemsbasedonbigdata
AT xiaojunliu interactivealgorithmsincompleximageprocessingsystemsbasedonbigdata
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