scholarly journals A Review of Keypoints’ Detection and Feature Description in Image Registration

2021 ◽  
Vol 2021 ◽  
pp. 1-25
Author(s):  
Cuiyin Liu ◽  
Jishang Xu ◽  
Feng Wang
Keyword(s):  
Deep Learning ◽  
Image Registration ◽  
Feature Detection ◽  
Geometric Transformation ◽  
Learning Technology ◽  
Learning Methods ◽  
Application Problem ◽  
Selection For ◽  
Keypoints Detection ◽  
Imaging Conditions

For image registration, feature detection and description are critical steps that identify the keypoints and describe them for the subsequent matching to estimate the geometric transformation parameters between two images. Recently, there has been a large increase in the research methods of detection operators and description operators, from traditional methods to deep learning methods. To solve the problem, that is, which operator is suitable for specific application problems under different imaging conditions, the paper systematically reviewed commonly used descriptors and detectors from artificial methods to deep learning methods, and the corresponding principle, analysis, and comparative experiments are given as well. We introduce the handcrafted detectors including FAST, BRISK, ORB, SURF, SIFT, and KAZE and the handcrafted descriptors including BRISK, FREAK, BRIEF, SURF, ORB, SIFT, KAZE. At the same time, we review detectors based on deep learning technology including DetNet, TILDE, LIFT, multiscale detector, SuperPoint, and descriptors based on deep learning including pretrained descriptor, Siamese descriptor, LIFT, triplet network, and SuperPoint. Two group of comparison experiments are compared comprehensively and objectively on representative datasets. Finally, we concluded with insightful discussions and conclusions of descriptor and detector selection for specific application problem and hope this survey can be a reference for researchers and engineers in image registration and related fields.

scholarly journals Indicator Selection for Topic Popularity Definition Based on AHP and Deep Learning Models

2020 ◽  
Vol 2020 ◽  
pp. 1-11 ◽  
Author(s):  
Yuling Hong ◽  
Qishan Zhang
Keyword(s):  
Deep Learning ◽  
Social Network ◽  
Evaluation Method ◽  
Short Term Memory ◽  
Selection Model ◽  
Learning Technology ◽  
Prevention Measures ◽  
Learning Methods ◽  
Indicator Selection ◽  
The Social

Purpose. The purpose of this article is to predict the topic popularity on the social network accurately. Indicator selection model for a new definition of topic popularity with degree of grey incidence (DGI) is undertook based on an improved analytic hierarchy process (AHP). Design/Methodology/Approach. Through screening the importance of indicators by the deep learning methods such as recurrent neural networks (RNNs), long short-term memory (LSTM), and gated recurrent unit (GRU), a selection model of topic popularity indicators based on AHP is set up. Findings. The results show that when topic popularity is being built quantitatively based on the DGI method and different weights of topic indicators are obtained from the help of AHP, the average accuracy of topic popularity prediction can reach 97.66%. The training speed is higher and the prediction precision is higher. Practical Implications. The method proposed in the paper can be used to calculate the popularity of each hot topic and generate the ranking list of topics’ popularities. Moreover, its future popularity can be predicted by deep learning methods. At the same time, a new application field of deep learning technology has been further discovered and verified. Originality/Value. This can lay a theoretical foundation for the formulation of topic popularity tendency prevention measures on the social network and provide an evaluation method which is consistent with the actual situation.

scholarly journals A Stacked Fully Convolutional Networks with Feature Alignment Framework for Multi-Label Land-cover Segmentation

2019 ◽  
Vol 11 (9) ◽  
pp. 1051 ◽  
Author(s):  
Guangming Wu ◽  
Yimin Guo ◽  
Xiaoya Song ◽  
Zhiling Guo ◽  
Haoran Zhang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Land Cover ◽  
Superior Performance ◽  
Learning Methods ◽  
Convolutional Networks ◽  
Fully Convolutional Networks ◽  
Optimal Feature ◽  
Imaging Conditions ◽  
Very High ◽  
Feature Alignment

Applying deep-learning methods, especially fully convolutional networks (FCNs), has become a popular option for land-cover classification or segmentation in remote sensing. Compared with traditional solutions, these approaches have shown promising generalization capabilities and precision levels in various datasets of different scales, resolutions, and imaging conditions. To achieve superior performance, a lot of research has focused on constructing more complex or deeper networks. However, using an ensemble of different fully convolutional models to achieve better generalization and to prevent overfitting has long been ignored. In this research, we design four stacked fully convolutional networks (SFCNs), and a feature alignment framework for multi-label land-cover segmentation. The proposed feature alignment framework introduces an alignment loss of features extracted from basic models to balance their similarity and variety. Experiments on a very high resolution(VHR) image dataset with six categories of land-covers indicates that the proposed SFCNs can gain better performance when compared to existing deep learning methods. In the 2nd variant of SFCN, the optimal feature alignment gains increments of 4.2% (0.772 vs. 0.741), 6.8% (0.629 vs. 0.589), and 5.5% (0.727 vs. 0.689) for its f1-score, jaccard index, and kappa coefficient, respectively.

scholarly journals Fully Leveraging Deep Learning Methods for Constructing Retinal Fundus Photomontages

2021 ◽  
Vol 11 (4) ◽  
pp. 1754
Author(s):  
Jooyoung Kim ◽  
Sojung Go ◽  
Kyoungjin Noh ◽  
Sangjun Park ◽  
Soochahn Lee
Keyword(s):  
Deep Learning ◽  
Image Registration ◽  
Experimental Evaluation ◽  
Image Texture ◽  
Peripheral Retina ◽  
Image Artifacts ◽  
Retinal Diseases ◽  
Fundus Images ◽  
Learning Methods ◽  
Retinal Fundus

Retinal photomontages, which are constructed by aligning and integrating multiple fundus images, are useful in diagnosing retinal diseases affecting peripheral retina. We present a novel framework for constructing retinal photomontages that fully leverage recent deep learning methods. Deep learning based object detection is used to define the order of image registration and blending. Deep learning based vessel segmentation is used to enhance image texture to improve registration performance within a two step image registration framework comprising rigid and non-rigid registration. Experimental evaluation demonstrates the robustness of our montage construction method with an increased amount of successfully integrated images as well as reduction of image artifacts.

scholarly journals Deep Dehazing Network for Remote Sensing Image with Non-Uniform Haze

2021 ◽  
Vol 13 (21) ◽  
pp. 4443
Author(s):  
Bo Jiang ◽  
Guanting Chen ◽  
Jinshuai Wang ◽  
Hang Ma ◽  
Lin Wang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Wavelet Transform ◽  
Large Scale ◽  
Remote Sensing Image ◽  
Superior Performance ◽  
Learning Technology ◽  
Remote Sensing Images ◽  
Learning Methods ◽  
Atmospheric Scattering

The haze in remote sensing images can cause the decline of image quality and bring many obstacles to the applications of remote sensing images. Considering the non-uniform distribution of haze in remote sensing images, we propose a single remote sensing image dehazing method based on the encoder–decoder architecture, which combines both wavelet transform and deep learning technology. To address the clarity issue of remote sensing images with non-uniform haze, we preliminary process the input image by the dehazing method based on the atmospheric scattering model, and extract the first-order low-frequency sub-band information of its 2D stationary wavelet transform as an additional channel. Meanwhile, we establish a large-scale hazy remote sensing image dataset to train and test the proposed method. Extensive experiments show that the proposed method obtains greater advantages over typical traditional methods and deep learning methods qualitatively. For the quantitative aspects, we take the average of four typical deep learning methods with superior performance as a comparison object using 500 random test images, and the peak-signal-to-noise ratio (PSNR) value using the proposed method is improved by 3.5029 dB, and the structural similarity (SSIM) value is improved by 0.0295, respectively. Based on the above, the effectiveness of the proposed method for the problem of remote sensing non-uniform dehazing is verified comprehensively.

scholarly journals Review of machine learning and deep learning application in mine microseismic event classification

2021 ◽  
Vol 15 (1) ◽  
pp. 19-26
Author(s):  
Wang Jinqiang ◽  
Prabhat Basnet ◽  
Shakil Mahtab
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Rock Burst ◽  
Learning Technology ◽  
Rock Fracturing ◽  
Learning Methods ◽  
Historical Outline ◽  
Fracturing Process ◽  
Relevant Field ◽  
Learning Development

Purpose. To put forward the concept of machine learning and deep learning approach in Mining Engineering in order to get high accuracy in separating mine microseismic (MS) event from non-useful events such as noise events blasting events and others. Methods. Traditionally applied methods are described and their low impact on classifying MS events is discussed. General historical description of machine learning and deep learning methods is shortly elaborated and different approaches conducted using these methods for classifying MS events are analysed. Findings. Acquired MS data from rock fracturing process recorded by sensors are inaccurate due to complex mining environment. They always need preprocessing in order to classify actual seismic events. Traditional detecting and classifying methods do not always yield precise results, which is especially disappointing when different events have a similar nature. The breakthrough of machine learning and deep learning methods made it possible to classify various MS events with higher precision compared to the traditional one. This paper introduces a state-of-the-art review of the application of machine learning and deep learning in identifying mine MS events. Originality.Previously adopted methods are discussed in short, and a brief historical outline of Machine learning and deep learning development is presented. The recent advancement in discriminating MS events from other events is discussed in the context of these mechanisms, and finally conclusions and suggestions related to the relevant field are drawn. Practical implications. By means of machin learning and deep learning technology mine microseismic events can be identified accurately which allows to determine the source location so as to prevent rock burst. Keywords: rock burst, MS event, blasting event, noise event, machine learning, deep learning

Smart teaching mode based on particle swarm image recognition and human-computer interaction deep learning

2020 ◽  
Vol 39 (4) ◽  
pp. 5699-5711
Author(s):  
Shirong Long ◽  
Xuekong Zhao
Keyword(s):  
Feature Extraction ◽  
Particle Swarm Optimization ◽  
Deep Learning ◽  
Real Time ◽  
Image Recognition ◽  
Particle Swarm ◽  
Learning Technology ◽  
Search Performance ◽  
Swarm Optimization ◽  
Teaching Mode

The smart teaching mode overcomes the shortcomings of traditional teaching online and offline, but there are certain deficiencies in the real-time feature extraction of teachers and students. In view of this, this study uses the particle swarm image recognition and deep learning technology to process the intelligent classroom video teaching image and extracts the classroom task features in real time and sends them to the teacher. In order to overcome the shortcomings of the premature convergence of the standard particle swarm optimization algorithm, an improved strategy for multiple particle swarm optimization algorithms is proposed. In order to improve the premature problem in the search performance algorithm of PSO algorithm, this paper combines the algorithm with the useful attributes of other algorithms to improve the particle diversity in the algorithm, enhance the global search ability of the particle, and achieve effective feature extraction. The research indicates that the method proposed in this paper has certain practical effects and can provide theoretical reference for subsequent related research.

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