A Modeling Method for Automatic Extraction of Offshore Aquaculture Zones Based on Semantic Segmentation

Baikai Sui; Tao Jiang; Zhen Zhang; Xinliang Pan; Chenxi Liu

doi:10.3390/ijgi9030145

A Modeling Method for Automatic Extraction of Offshore Aquaculture Zones Based on Semantic Segmentation

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi9030145 ◽

2020 ◽

Vol 9 (3) ◽

pp. 145 ◽

Cited By ~ 4

Author(s):

Baikai Sui ◽

Tao Jiang ◽

Zhen Zhang ◽

Xinliang Pan ◽

Chenxi Liu

Keyword(s):

Recognition Rate ◽

Semantic Segmentation ◽

Recognition Algorithm ◽

Identification Accuracy ◽

Generative Adversarial Networks ◽

Automatic Extraction ◽

Large Area ◽

Marine Aquaculture ◽

Floating Raft ◽

Offshore Aquaculture

Monitoring of offshore aquaculture zones is important to marine ecological environment protection and maritime safety and security. Remote sensing technology has the advantages of large-area simultaneous observation and strong timeliness, which provide normalized monitoring of marine aquaculture zones. Aiming at the problems of weak generalization ability and low recognition rate in weak signal environments of traditional target recognition algorithm, this paper proposes a method for automatic extraction of offshore fish cage and floating raft aquaculture zones based on semantic segmentation. This method uses Generative Adversarial Networks to expand the data to compensate for the lack of training samples, and uses ratio of green band to red band (G/R) instead of red band to enhance the characteristics of aquaculture spectral information, combined with atrous convolution and atrous space pyramid pooling to enhance the context semantic information, to extract and identify two types of offshore fish cage zones and floating raft aquaculture zones. The experiment is carried out in the eastern coastal waters of Shandong Province, China, and the overall identification accuracy of the two types of aquaculture zones can reach 94.8%. The results show that the method proposed in this paper can realize high-precision extraction both of offshore fish cage and floating raft aquaculture zones.

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Human action recognition using simple geometric features and a finite state machine

Image Processing & Communications ◽

10.2478/v10248-012-0079-y ◽

2013 ◽

Vol 18 (2-3) ◽

pp. 49-60 ◽

Cited By ~ 2

Author(s):

Damian Dudzńiski ◽

Tomasz Kryjak ◽

Zbigniew Mikrut

Keyword(s):

Action Recognition ◽

Finite State Machine ◽

Recognition Rate ◽

Human Action Recognition ◽

Human Action ◽

Video Stream ◽

State Machine ◽

Recognition Algorithm ◽

Finite State ◽

Correct Recognition Rate

Abstract In this paper a human action recognition algorithm, which uses background generation with shadow elimination, silhouette description based on simple geometrical features and a finite state machine for recognizing particular actions is described. The performed tests indicate that this approach obtains a 81 % correct recognition rate allowing real-time image processing of a 360 X 288 video stream.

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Semantic Segmentation of High Resolution Satellite Imagery using Generative Adversarial Networks with Progressive Growing

Remote Sensing Letters ◽

10.1080/2150704x.2021.1895444 ◽

2021 ◽

Vol 12 (5) ◽

pp. 439-448

Author(s):

Edward Collier ◽

Supratik Mukhopadhyay ◽

Kate Duffy ◽

Sangram Ganguly ◽

Geri Madanguit ◽

...

Keyword(s):

High Resolution ◽

Satellite Imagery ◽

Semantic Segmentation ◽

Generative Adversarial Networks ◽

Adversarial Networks ◽

High Resolution Satellite Imagery

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Research on Modulation Recognition Algorithm of Digital Communication Signal Based on Wavelet Denoising

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.608-609.459 ◽

2014 ◽

Vol 608-609 ◽

pp. 459-467 ◽

Cited By ~ 1

Author(s):

Xiao Yu Gu

Keyword(s):

Pattern Recognition ◽

Feature Extraction ◽

Decision Theory ◽

Recognition Rate ◽

Recognition Algorithm ◽

Modulation Recognition ◽

The Core ◽

Feature Parameters ◽

Communication Signal ◽

Modulation Signal

The paper researches a recognition algorithm of modulation signal and modulation modes. The modulation modes to be recognized include 2ASK, 2FSK, 2PSK, 4ASK, 4FSK and 4PSK modulation. There are two methods recognizing modulation modes of digital signal, method based on decision theory and pattern-recognition method based on feature extraction. The method based on decision theory is not suitable for recognition with multiple modulation modes. The core of pattern recognition based on feature extraction is selection of feature parameters. So the paper uses the feature parameters with simple calculation, easy to be implemented and high recognition rate as the core. The extraction of feature parameters is based on instant feature of modulation signal after Hilbert transformation.

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Design of Gait Recognition System Based on FPGA and DSP

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.687-691.3861 ◽

2014 ◽

Vol 687-691 ◽

pp. 3861-3868

Author(s):

Zheng Hong Deng ◽

Li Tao Jiao ◽

Li Yan Liu ◽

Shan Shan Zhao

Keyword(s):

Monitoring System ◽

Nearest Neighbor ◽

Gait Recognition ◽

Recognition Rate ◽

Digital Signal ◽

Recognition System ◽

Recognition Algorithm ◽

Zernike Moment ◽

Intelligent Monitoring ◽

The One

According to the trend of the intelligent monitoring system, on the basis of the study of gait recognition algorithm, the intelligent monitoring system is designed based on FPGA and DSP; On the one hand, FPGA’s flexibility and fast parallel processing algorithms when designing can be both used to avoid that circuit can not be modified after designed; On the other hand, the advantage of processing the digital signal of DSP is fully taken. In the feature extraction and recognition, Zernike moment is selected, at the same time the system uses the nearest neighbor classification method which is more mature and has good real-time performance. Experiments show that the system has high recognition rate.

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Semantic Segmentation using Generative Adversarial Networks with a Feature Reconstruction Loss

10.1109/asiancon51346.2021.9544954 ◽

2021 ◽

Author(s):

Gaurav Sohaliya ◽

Kapil Sharma

Keyword(s):

Semantic Segmentation ◽

Generative Adversarial Networks ◽

Adversarial Networks ◽

Feature Reconstruction

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Incremental Graph Regulated Nonnegative Matrix Factorization for Face Recognition

Journal of Applied Mathematics ◽

10.1155/2014/928051 ◽

2014 ◽

Vol 2014 ◽

pp. 1-10 ◽

Cited By ~ 6

Author(s):

Zhe-Zhou Yu ◽

Yu-Hao Liu ◽

Bin Li ◽

Shu-Chao Pang ◽

Cheng-Cheng Jia

Keyword(s):

Face Recognition ◽

Matrix Factorization ◽

Information Needs ◽

Nonnegative Matrix Factorization ◽

Recognition Rate ◽

Nonnegative Matrix ◽

Recognition Algorithm ◽

Incremental Method ◽

Real World Application ◽

The Face

In a real world application, we seldom get all images at one time. Considering this case, if a company hired an employee, all his images information needs to be recorded into the system; if we rerun the face recognition algorithm, it will be time consuming. To address this problem, In this paper, firstly, we proposed a novel subspace incremental method called incremental graph regularized nonnegative matrix factorization (IGNMF) algorithm which imposes manifold into incremental nonnegative matrix factorization algorithm (INMF); thus, our new algorithm is able to preserve the geometric structure in the data under incremental study framework; secondly, considering we always get many face images belonging to one person or many different people as a batch, we improved our IGNMF algorithms to Batch-IGNMF algorithms (B-IGNMF), which implements incremental study in batches. Experiments show that (1) the recognition rate of our IGNMF and B-IGNMF algorithms is close to GNMF algorithm while it runs faster than GNMF. (2) The running times of our IGNMF and B-IGNMF algorithms are close to INMF while the recognition rate outperforms INMF. (3) Comparing with other popular NMF-based face recognition incremental algorithms, our IGNMF and B-IGNMF also outperform then both the recognition rate and the running time.

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RICNN: A ResNet&Inception convolutional neural network for intrusion detection of abnormal traffic

Computer Science and Information Systems ◽

10.2298/csis210617055x ◽

2021 ◽

pp. 55-55

Author(s):

Benhui Xia ◽

Dezhi Han ◽

Ximing Yin ◽

Gao Na

Keyword(s):

Neural Network ◽

Intrusion Detection ◽

Convolutional Neural Network ◽

Detection Rate ◽

Recognition Rate ◽

Identification Accuracy ◽

Detection Accuracy ◽

Traffic Classification ◽

High Detection Rate ◽

Network Intrusion

To secure cloud computing and outsourced data while meeting the requirements of automation, many intrusion detection schemes based on deep learn ing are proposed. Though the detection rate of many network intrusion detection solutions can be quite high nowadays, their identification accuracy on imbalanced abnormal network traffic still remains low. Therefore, this paper proposes a ResNet &Inception-based convolutional neural network (RICNN) model to abnormal traffic classification. RICNN can learn more traffic features through the Inception unit, and the degradation problem of the network is eliminated through the direct map ping unit of ResNet, thus the improvement of the model?s generalization ability can be achievable. In addition, to simplify the network, an improved version of RICNN, which makes it possible to reduce the number of parameters that need to be learnt without degrading identification accuracy, is also proposed in this paper. The experimental results on the dataset CICIDS2017 show that RICNN not only achieves an overall accuracy of 99.386% but also has a high detection rate across different categories, especially for small samples. The comparison experiments show that the recognition rate of RICNN outperforms a variety of CNN models and RNN models, and the best detection accuracy can be achieved.

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Rotation Consistency-Preserved Generative Adversarial Networks for Cross-Domain Aerial Image Semantic Segmentation

10.1109/igarss47720.2021.9554606 ◽

2021 ◽

Author(s):

Te Shi ◽

Yansheng Li ◽

Yongjun Zhang

Keyword(s):

Semantic Segmentation ◽

Aerial Image ◽

Generative Adversarial Networks ◽

Adversarial Networks ◽

Cross Domain

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Vehicle License Plate Recognition Based on Wavelet Transform and Vertical Edge Matching

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001420500160 ◽

2019 ◽

Vol 34 (06) ◽

pp. 2050016

Author(s):

Zhongli Wang ◽

Xiping Ma ◽

Wenlin Huang

Keyword(s):

Wavelet Transform ◽

Road Traffic ◽

Recognition Rate ◽

Recognition Algorithm ◽

License Plate ◽

License Plate Recognition ◽

Vertical Edge ◽

Vertical Projection ◽

Edge Matching ◽

Vehicle License Plate Recognition

With the improvement of our country’s economic level and quality of life, the numbers and scales of highway networks and motor vehicles are constantly expanding, which makes the current road traffic burden more and more serious. As an important means of traffic automation management, license plate recognition (LPR) technology plays an important role in traffic surveillance and control. However, the recognition rate and accuracy of the traditional license plate recognition methods still need to be improved. In the case of poor surrounding environment, it is prone to localization failure, vehicle license plate recognition errors or unrecognizable phenomena. Wavelet transform, as another landmark signal processing method after Fourier transform, has been widely used in the field of image processing. In China, the number of horizontal lines is usually larger than that of vertical lines. If the two vertical boundaries of the license plate can be detected successfully, the four angles of the license plate can be determined efficiently to complete the license plate positioning. In view of the advantages of wavelet transform technology and the characteristics of vehicle license plate, in this paper, a vehicle license plate recognition algorithm based on wavelet transform and vertical edge matching is proposed. The edge of the license plate is detected by wavelet transform technology, and then the license plate is located by vertical edge matching technology. After the location is realized, the characters are segmented by vertical projection method and the characters are recognized by improved BP neural network algorithm. The experimental results show that the proposed vehicle license plate recognition algorithm based on wavelet transform and vertical edge matching performs well in algorithm performance, which provides a good reference for the development of vehicle license plate recognition system.

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