scholarly journals Video Recognition of Government Community Management Cases Based on Partial Differential Equation Method

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yumeng Sun
Keyword(s):  
Differential Equation ◽  
Partial Differential Equation ◽  
Image Segmentation ◽  
Image Recognition ◽  
Community Management ◽  
Main Function ◽  
Partial Differential ◽  
Traditional Algorithm ◽  
Video Recognition ◽  
The Government

With the development of urban economic construction and urban planning, higher requirements are put forward for the government community in the corresponding community management, community service, and other related things. As an important technical means to assist the government and community in management, video recognition technology plays an important role in the accurate management and service of the government and community. Traditional algorithms based on partial differential equations will destroy image edges and image details in video recognition. Based on this, this paper improves the traditional partial differential equation algorithm of image recognition, selects the GAC model based on image segmentation in the main function, and innovatively optimizes the stop function of its equation function, so as to improve the effect of community case image segmentation. In the image smoothing layer, this paper innovatively selects the second derivative based on image processing as the inherent feature of image recognition, so as to solve the rough problem of image edge and improve the processing efficiency of the algorithm. In order to further maintain the details of the relevant images of community cases, this paper integrates the Gaussian curvature driving function on the improved partial differential equation algorithm, so as to protect the details of the smooth region of the relevant recognition video and solve the disadvantages of the traditional algorithm. The experimental results show that the improved partial differential equation algorithm proposed in this paper improves the accuracy of video recognition by about 5% compared with the traditional algorithm. At the same time, the new algorithm can well ensure the detail integrity of the recognized video.

scholarly journals Barcode Location in Financial Statement System Based on the Partial Differential Equation Image Recognition Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Jinghua Ning ◽  
Song Yu
Keyword(s):  
Differential Equation ◽  
Partial Differential Equation ◽  
Information Sharing ◽  
Image Recognition ◽  
Resource Sharing ◽  
Financial Analysis ◽  
Financial Statements ◽  
Recognition Algorithm ◽  
Financial Statement ◽  
Partial Differential

Financial statements are the basis of financial analysis. Most of them are still using traditional financial statement software, and financial data cannot achieve better information sharing. Using a barcode to improve the efficiency of data sharing is undoubtedly the most convenient and fast way to realize financial statement information sharing. This paper studies the problem of the barcode image location and recognition in the financial statement system and tries to apply the partial differential equation image recognition algorithm to the barcode location in the financial statement system. In this paper, image technology is used to preprocess the code 39 barcode label image in the captured image, including color space conversion, image enhancement, threshold segmentation, binarization, and edge detection. The barcode position location is realized by using the method based on integral projection peak analysis and Hough transform line detection. It is proved that the positioning function of the barcode makes the data in financial statements realize resource sharing. Then, the performance of the completed barcode positioning and recognition algorithm is tested. The reliability and effectiveness of the algorithm are verified on the manually made test set.

An Efficient Image Segmentation Using Partial Differential Equation for Image Processing Applications

2021 ◽  
Vol 11 (10) ◽  
pp. 2538-2545
Author(s):  
P. Geetha ◽  
S. Nagarani
Keyword(s):  
Differential Equation ◽  
Mathematical Method ◽  
Image Processing ◽  
Partial Differential Equation ◽  
Image Segmentation ◽  
Image Restoration ◽  
Geometric Analysis ◽  
Stochastic Modelling ◽  
Partial Differential ◽  
Structural Work

Different processing of the images, such as the image captured, saved and retrieved from another use of the specific image, must be restructured in various ways in the process. More methods such as image restoration, picture segmentation, improvement of the picture etc can be used when processing images. Reconstructed in 3D picture 2D pictures are need to be proper. Including geometric wavelets and geometric analysis the structural work focused upon a variational and a selectable differential equation to test PDE’s which is a convergence of stochastic modelling and analysis of harmonics. This paper focuses primarily on the critical reviews of the image segmentation collection with the PDE application as a mathematical method and introduces the key tool of mathematics and techniques along with the literature-based analysis.

scholarly journals Research on Image Recognition of Building Wall Design Defects Based on Partial Differential Equation

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Xiwen Yu ◽  
Kai Wang ◽  
Shaoxuan Wang
Keyword(s):  
Differential Equation ◽  
Partial Differential Equation ◽  
Image Recognition ◽  
Surface Defects ◽  
Feature Space ◽  
Inner Product ◽  
High Dimensional ◽  
Order Partial Differential Equation ◽  
Partial Differential ◽  
Building Wall

The detection of building wall surface defects is of great significance to eliminate potential safety hazards. In this paper, a research on building wall design defect image recognition based on partial differential equation is proposed. Collect the image data of building surface defects, sample and quantify the collected images, and preprocess the defect images such as digital threshold segmentation, filtering, and enhancement. Then, the improved partial differential equation is used to recognize the image as a whole. The second-order partial differential diffusion equation and the fourth-order partial differential equation are used to recognize the high-frequency and low-frequency bands of the image, respectively. The kernel principal component analysis algorithm is used to transfer the overall image input space to the high-dimensional feature space. The kernel function is used to calculate the inner product in different subband images of the high-dimensional feature space to reduce the dimension of the overall image. The processed coefficients are inversely transformed by nondownsampling contour wave to realize the overall image recognition and ensure that the edge information of the source image does not disappear. Experimental results show that compared with other algorithms, the proposed algorithm has better effect and better stability.

Sign in / Sign up

Export Citation Format

Share Document