scholarly journals A Survey of Orthogonal Moments for Image Representation: Theory, Implementation, and Evaluation

2023 ◽  
Vol 55 (1) ◽  
pp. 1-35
Author(s):  
Shuren Qi ◽  
Yushu Zhang ◽  
Chao Wang ◽  
Jiantao Zhou ◽  
Xiaochun Cao
Keyword(s):  
Image Representation ◽  
Semantic Description ◽  
Analysis Software ◽  
Theory Analysis ◽  
Orthogonal Moments ◽  
Geometric Invariance ◽  
Mathematical Properties ◽  
Real World Applications ◽  
Comprehensive Survey ◽  
Core Conditions

Image representation is an important topic in computer vision and pattern recognition. It plays a fundamental role in a range of applications toward understanding visual contents. Moment-based image representation has been reported to be effective in satisfying the core conditions of semantic description due to its beneficial mathematical properties, especially geometric invariance and independence. This article presents a comprehensive survey of the orthogonal moments for image representation, covering recent advances in fast/accurate calculation, robustness/invariance optimization, definition extension, and application. We also create a software package for a variety of widely used orthogonal moments and evaluate such methods in a same base. The presented theory analysis, software implementation, and evaluation results can support the community, particularly in developing novel techniques and promoting real-world applications.

scholarly journals Invariant Image Representation Using Novel Fractional-Order Polar Harmonic Fourier Moments

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1544
Author(s):  
Chunpeng Wang ◽  
Hongling Gao ◽  
Meihong Yang ◽  
Jian Li ◽  
Bin Ma ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Fractional Order ◽  
Continuous Functions ◽  
Kernel Functions ◽  
Superior Performance ◽  
Image Description ◽  
Orthogonal Moments ◽  
Integer Order ◽  
Geometric Invariance ◽  
Order Continuous

Continuous orthogonal moments, for which continuous functions are used as kernel functions, are invariant to rotation and scaling, and they have been greatly developed over the recent years. Among continuous orthogonal moments, polar harmonic Fourier moments (PHFMs) have superior performance and strong image description ability. In order to improve the performance of PHFMs in noise resistance and image reconstruction, PHFMs, which can only take integer numbers, are extended to fractional-order polar harmonic Fourier moments (FrPHFMs) in this paper. Firstly, the radial polynomials of integer-order PHFMs are modified to obtain fractional-order radial polynomials, and FrPHFMs are constructed based on the fractional-order radial polynomials; subsequently, the strong reconstruction ability, orthogonality, and geometric invariance of the proposed FrPHFMs are proven; and, finally, the performance of the proposed FrPHFMs is compared with that of integer-order PHFMs, fractional-order radial harmonic Fourier moments (FrRHFMs), fractional-order polar harmonic transforms (FrPHTs), and fractional-order Zernike moments (FrZMs). The experimental results show that the FrPHFMs constructed in this paper are superior to integer-order PHFMs and other fractional-order continuous orthogonal moments in terms of performance in image reconstruction and object recognition, as well as that the proposed FrPHFMs have strong image description ability and good stability.

scholarly journals Transfer Learning and Deep Domain Adaptation

2020 ◽  
Author(s):  
Wen Xu ◽  
Jing He ◽  
Yanfeng Shu
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Transfer Learning ◽  
Real World ◽  
Deep Neural Networks ◽  
Domain Adaptation ◽  
Fine Tuning ◽  
Real World Applications ◽  
Comprehensive Survey ◽  
Sample Reconstruction

Transfer learning is an emerging technique in machine learning, by which we can solve a new task with the knowledge obtained from an old task in order to address the lack of labeled data. In particular deep domain adaptation (a branch of transfer learning) gets the most attention in recently published articles. The intuition behind this is that deep neural networks usually have a large capacity to learn representation from one dataset and part of the information can be further used for a new task. In this research, we firstly present the complete scenarios of transfer learning according to the domains and tasks. Secondly, we conduct a comprehensive survey related to deep domain adaptation and categorize the recent advances into three types based on implementing approaches: fine-tuning networks, adversarial domain adaptation, and sample-reconstruction approaches. Thirdly, we discuss the details of these methods and introduce some typical real-world applications. Finally, we conclude our work and explore some potential issues to be further addressed.

scholarly journals 3D Image Representation Using Separable Discrete Orthogonal Moments

2019 ◽  
Vol 148 ◽  
pp. 389-398 ◽  
Author(s):  
Imad Batioua ◽  
Rachid Benouini ◽  
Khalid Zenkouar ◽  
Said Najah ◽  
Hakim El Fadili ◽  
...  
Keyword(s):  
Image Representation ◽  
3D Image ◽  
Orthogonal Moments ◽  
Discrete Orthogonal

Software Design for Theory Analysis of Underwater Electromagnetic Field in Shallow Sea Environment

2014 ◽  
Vol 998-999 ◽  
pp. 1508-1511
Author(s):  
Pei Cui ◽  
Liang Wu ◽  
Kai Na Jiang
Keyword(s):  
Electromagnetic Field ◽  
Comparative Analysis ◽  
Software Design ◽  
Environmental Parameters ◽  
Environmental Parameter ◽  
Shallow Sea ◽  
Analysis Software ◽  
Theory Analysis ◽  
Field Source ◽  
Parameter Values

The driving sources of the marine environment underwater electromagnetic field are complicated, the environmental parameters working on field source amplitude are numerous. The paper filters out the main respective factor through comparative analysis, on the basis of which composes theoretical analysis software of electromagnetic field underwater and the main environmental factors. The result shows that it is easy to get the variations of field source amplitude and main environmental parameters by setting the main environmental parameter values and field source forms.

scholarly journals Image representation based on fractional order Legendre and Laguerre orthogonal moments

2021 ◽  
Vol 8 (2) ◽  
pp. 54-59
Author(s):  
R. M. Farouk ◽  
◽  
Qamar A. A. Awad ◽  
Keyword(s):  
Fractional Order ◽  
Image Representation ◽  
Computational Time ◽  
Gray Level ◽  
Global Features ◽  
Orthogonal Functions ◽  
Orthogonal Moments ◽  
Fractional Moments ◽  
Gray Level Image ◽  
Detection Filter

In this paper, we have introduced new sets of fractional order orthogonal basis moments based on Fractional order Legendre orthogonal Functions (FLeFs) and Fractional order Laguerre orthogonal Functions (FLaFs) for image representation. We have generated a novel set of Fractional order Legendre orthogonal Moments (FLeMs) from fractional order Legendre orthogonal functions and a new set of Fractional order Laguerre orthogonal Moments (FLaMs) from the fractional order Laguerre orthogonal functions. The new presented sets of (FLeMs) and (FLaMs) are tested with the recently introduced Fractional order Chebyshev orthogonal Moments (FCMs). This edge detection filter can be used successfully in the gray level image and color images. The new sets of fractional moments are used to reconstruct the gray level image. The numerical results show FLeMs and FLaMs are promised techniques for image representation. The computational time of the proposed techniques is compared with the computational time of Chebyshev orthogonal Moments techniques and gives better results. Also, the fractional parameters give the flexibility of studying global features of the image at different positions of moments.

scholarly journals Multifractal formalisms for multivariate analysis

2019 ◽  
Vol 475 (2229) ◽  
pp. 20190150 ◽  
Author(s):  
Stéphane Jaffard ◽  
Stéphane Seuret ◽  
Herwig Wendt ◽  
Roberto Leonarduzzi ◽  
Patrice Abry
Keyword(s):  
Multivariate Analysis ◽  
Multifractal Analysis ◽  
Theoretical Study ◽  
Univariate Analysis ◽  
Natural Extension ◽  
Mathematical Properties ◽  
Standard Signal ◽  
Real World Applications ◽  
Signal Image ◽  
Joint Multifractal Analysis

Multifractal analysis, that quantifies the fluctuations of regularities in time series or textures, has become a standard signal/image processing tool. It has been successfully used in a large variety of applicative contexts. Yet, successes are confined to the analysis of one signal or image at a time (univariate analysis). This is because multivariate (or joint) multifractal analysis remains so far rarely used in practice and has barely been studied theoretically. In view of the myriad of modern real-world applications that rely on the joint (multivariate) analysis of collections of signals or images, univariate analysis constitutes a major limitation. The goal of the present work is to theoretically ground multivariate multifractal analysis by studying the properties and limitations of the most natural extension of the univariate formalism to a multivariate formulation. It is notably shown that while performing well for a class of model processes, this natural extension is not valid in general. Based on the theoretical study of the mechanisms leading to failure, we propose alternative formulations and examine their mathematical properties.

A Comprehensive Survey on Edge Computing for the IoT

2019 ◽  
pp. 37-45 ◽  
Author(s):  
Pravin A. ◽  
Prem Jacob ◽  
G. Nagarajan
Keyword(s):  
Cloud Computing ◽  
Power Consumption ◽  
Response Time ◽  
Real World ◽  
Data Security ◽  
Edge Computing ◽  
Consumption Data ◽  
Real World Applications ◽  
Comprehensive Survey ◽  
Overall Performance

The IoT concept is used in various applications and it uses different devices for collecting data and processing the data. Various sets of devices such as sensors generate a large amount of data and the data will be forwarded to the appropriate devices for processing. The devices used will range from small devices to larger devices. The edge computing becomes the major role in overcoming the difficulties in cloud computing, the nearby devices are used as servers for providing better services. Most of the issues such as power consumption, data security, and response time will be addressed. The IoT plays a major role in many real-world applications. In this chapter, the basics and the use of the Edge computing concept in different applications are discussed. Edge computing can be used to increase the overall performance of the IoT. The performance of various applications in terms of edge computing and other methodologies are analyzed.

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