Texture classification using partial differential equation approach and wavelet transform

Hiremath, P. S.; Bhusnurmath, Rohini A.

doi:10.1134/s1054661817030154

Cited by 5 publications

(2 citation statements)

References 13 publications

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“…But it is difficult for us to obtain analytical solutions for general differential equations, so we adopt the method of discretization of continuous problems, and through numerical analysis methods, we obtain its numerical solutions. The focus of the work of processing images with partial differential equations is on the establishment of image description models, and subsequent solutions are not so difficult [1,2]. The method of processing images by partial differential equations is continuous and integral.…”

Section: Introductionmentioning

confidence: 99%

Application of Image Recognition Algorithm Based on Partial Differential Equation and Wavelet Transform in the Intelligent Linkage System of Government Urban Public Management

2021

Advances in Mathematical Physics

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In the Internet age, social needs have become more diverse. The private sector has achieved great success in integrating resources to provide services to customers thanks to technological breakthroughs such as mobile Internet and big data, while the public sector has become even more dwarfed. The public sector urgently needs new theoretical support to integrate administrative resources more effectively, so as to provide the public with one-stop, holistic and seamless services as a whole. The “holistic governance” theory effectively responded to the abovementioned problems and gradually condensed the consensus of the academic and practical circles and became the most influential theory. The method of mathematical modeling is used to combine the improved wavelet domain Wiener filter and the comprehensive model of partial differential equations. This paper proposes an image recognition algorithm based on partial differential equations and wavelet transform. The algorithm uses weight coefficients to organically combine high-order partial differential equation models, retains the advantages of the second- and fourth-order partial differential recognition models, effectively improves the ability to maintain image edge detail information, and achieves better recognition results. This article starts with the description of the fragmentation of a street’s grassroots governance and sorts out the process of implementing overall governance with the “big linkage mechanism” as the main carrier. Due to the limitations of research ability and objective conditions, this article intercepts the “cross-section” of the big linkage mechanism in a certain street to demonstrate it and analyzes this “cross-section” more thoroughly. The experimental results of the data show that the image recognition algorithm based on partial differential equations and wavelet transform is effective and practical in the intelligent linkage system of the government urban public management.

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Section: Introductionmentioning

confidence: 99%

Application of Image Recognition Algorithm Based on Partial Differential Equation and Wavelet Transform in the Intelligent Linkage System of Government Urban Public Management

2021

Advances in Mathematical Physics

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“…To wavelet-based method, there are two types of features can be extracted from the wavelets coefficients (subbands): one is the wavelet-signature such as the norm-1 and norm-2 energies and standard deviations calculated from the coefficients of each wavelet subband [21]- [23]. The other one is the parameters of probability distribution model which is more efficient than wavelet-signature; the commonly-used probability distribution model includes Generalized Gaussian Model (GGM) [10] and Gaussian Mixture Model (GMM) [24]).…”

Section: Introductionmentioning

confidence: 99%

CDF Space Covariance Matrix of Gabor Wavelet With Convolutional Neural Network for Texture Recognition

Guan

Yang³

et al. 2019

IEEE Access

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A novel method for texture image recognition is proposed in this paper. The aim of the proposed method is to represent texture by combining the Gabor wavelet transform and deep learning which are efficient techniques for image analysis. We developed the cumulative distribution function (cdf) space covariance model of Gabor wavelet (CSCM-GW), which can jointly model multivariate data in cdf space, in the Gabor wavelet domain to represent texture. The images having different sizes will be transformed by CSCM-GW into same size covariance matrices. Because CSCM-GW is based on the covariance matrix which belongs to Riemannian space, it has the high computational cost in the recognition phase. Therefore, we proposed a novel method of texture recognition called CSCM-GWF-CNN which uses CNN to project the fused covariance of CSCM-GW into low-dimensional vector space for reducing the computational cost and improving the recognition performance. The experiments on Brodatz (111) and KTH-TIPS2-b texture databases show that the proposed method is efficient for texture representation and outperforms most of the state-of-the-art recognition methods. INDEX TERMS Texture recognition, covariance matrix, Gabor wavelet, multidimensional statistical model, convolutional neural network.

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WT and PDE Approach for Forest Species Recognition in Macroscopic Images

Bhusnurmath

Hiremath

2019

Communications in Computer and Information Science

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Texture classification using partial differential equation approach and wavelet transform

Cited by 5 publications

References 13 publications

Application of Image Recognition Algorithm Based on Partial Differential Equation and Wavelet Transform in the Intelligent Linkage System of Government Urban Public Management

Application of Image Recognition Algorithm Based on Partial Differential Equation and Wavelet Transform in the Intelligent Linkage System of Government Urban Public Management

CDF Space Covariance Matrix of Gabor Wavelet With Convolutional Neural Network for Texture Recognition

WT and PDE Approach for Forest Species Recognition in Macroscopic Images

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