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2nd ieee international conference on image processing and computer applications, icipca 2024

作者： Lv, Zhengen Liu, Yihui Cai, Yunjie Liu, Qi College of Mechanical and Control Engineering Guilin University of Technology Guilin China School of Mechanical and Electronic Engineering Jingdezhen Ceramic University Jingdezhen China College of Mechanical and Electrical Engineering Guangxi Agricultural Engineering Vocational Technical College Chongzuo China School of Energy and Electrical Engineering Qinghai University Xining China

ISBN: (纸本)9798350360240

image segmentation and recognition of oracle bone topography is a common challenge for in-depth study of oracle bones. The key lies in how to accurately and efficiently process and recognize the original oracle bone topography images and solve the practical problems in the research based on the image digitization results. The research of this topic helps to promote the research of oracle bone holography and the construction of digitization project. image segmentation of oracle bone tablet is to use digital image processing and computer vision technology to extract independent text regions with distinctive image features and non-overlapping each other in the complex background of the original topographic image. The study of automatic segmentation and recognition of oracle bone characters on original topos has important social significance. In this paper, the image preprocessing model based on the channel attention mechanism is firstly utilized to initially judge and identify the interference elements present in the original oracle bone topography image. Meanwhile, a joint filtering noise reduction model is established to effectively remove the location-like noise and artificial texture in the image. Then, a fast oracle bone image segmentation model based on neural network is established, and each single character region in the image is successfully segmented out by building and training the model through deep learning. In this paper, 76 common oracle scripts and their variant scripts are trained and recognized in combination with the oracle recognition enhancement model, and a one-to-many link between Chinese characters and oracle images is established. The accuracy and efficiency of oracle bone recognition are further improved by segmenting and classifying the test set images for recognition. © 2024 ieee.

关键词： image segmentation

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Neural Architecture Search Based on Simple and Parameter-Free Attention for Hyperspectral image Classification 2

Neural Architecture Search Based on Simple and Parameter-Fre...

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2nd ieee international conference on image processing and computer applications, icipca 2024

作者： Hu, Zhonggang Yang, Yuxia Lu, Yibo Software Engineering Institute of Guangzhou Department of Network Technology Guangzhou China

ISBN: (纸本)9798350360240

Recently, neural architecture search (NAS) methods have demonstrated remarkable results in hyperspectral image (HSI) classification. However, deep network structures often require consideration of model overfitting, and highly similar spatial-spectral features make HSI inefficient feature description. To tackle this issue, we propose a NAS method based on a simple and parameter-free (Sim) attention module (SimAM), called SAM-NAS, to effectively enhance classification accuracy by leveraging spatial-spectral details. To be more precise, SAM-NAS utilizes the SimAM to highlight the linear divisibility of each pixel in HSI along the spatial-spectral dimensions, highlighting the validity of sensitive pixels. SimAM can add attention weights to HSI three-dimensional datas simultaneously while allowing the module to maintain a lightweight attribute. Then, the weighted spatial-spectral features are fed into the NAS to obtain the final neural network classification model. Our suggested method shows encouraging outcomes in classification by examining two genuine HSI datasets. © 2024 ieee.

关键词： Pixels

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Transmission Line Inspection image Intelligent Diagnosis System 2

Transmission Line Inspection Image Intelligent Diagnosis Sys...

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2nd ieee international conference on image processing and computer applications, icipca 2024

作者： Zheng, Bangzhu Liu, Haitao Ma, Haiteng Liu, Yin Shen, Xingyi Liang, Hua Wen, Jie Guangdong Power Grid Co. Ltd. Maoming Power Supply Bureau Guangdong Province Maoming City525000 China

ISBN: (数字)9798350360240

ISBN: (纸本)9798350360240

This paper introduces the image diagnosis technology of power inspection based on computer vision. image import, database access, text output and other functional modules are designed using VisualStudio2010. ADO technology is used to access and change the database. The framework and method of diagnosis result fusion based on multidimensional fusion are proposed. The fault diagnosis criterion of the system is established by using genetic algorithm. Under the premise of ensuring the optimal solution, the key information is retained and the minimum expression of knowledge is obtained, so as to realize the rapid and accurate fault diagnosis. The experimental results show that the method is effective, fast and suitable for large-scale power grid, especially for transmission lines. Low error rate and friendly man-machine interface. It can meet the needs of power line inspection. The automatic inspection of the transmission line image is realized, which standardizes the working process and greatly improves the working efficiency. © 2024 ieee.

关键词： Power distribution lines

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processing of Speech Signals Based on MATLAB & FDAtool 2

Processing of Speech Signals Based on MATLAB & FDAtool

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2nd ieee international conference on image processing and computer applications, icipca 2024

作者： Yan, Lihong Huan, Jiayu Gao, Han Zhang, Wei Ma, Jiaqi Xi'an Eurasia University Shaanxi Xi'an710065 China

ISBN: (纸本)9798350360240

In order to solve the problem that the speech signal with noise in the communication, the paper proposes a method to reduce the noise of speech signal based on MATLAB & FDAtool. FDAtool is a GUI interface, which has the characteristics of graphic visualization, intuitive and effective to analysis of the system frequency spectrum. By analysis the time and frequency domain characteristics of speech and the noise signal, it makes use of the FDAtool and window functions to design the FIR filter to process the speech signal. The design results show that the combination of window function and FDAtool can effective perform signal processing and reduce the noise. © 2024 ieee.

关键词： MATLAB

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Bearing Fault image Recognition in Strong Noise Based on Optimized CNN Architecture 2

Bearing Fault Image Recognition in Strong Noise Based on Opt...

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2nd ieee international conference on image processing and computer applications, icipca 2024

作者： Li, Qiang Gai, Haowen Hu, Kaijie Northeastern University Shenyang China Tianjin College University of Science and Technology Beijing Tianjin China Zhonghuan Information College Tianjin University of Technology Tianjin China

ISBN: (纸本)9798350360240

To address the problem of low accuracy and poor stability of bearing diagnostic models under strong background noise, a bearing fault image recognition method is proposed that reduces the randomness of the model by avoiding the need for artificial parameterization, which can introduce random factors. This method is based on hyperparameter optimization of the GoogLeNet convolutional neural network model and decision fusion. First of all, two-dimensional wavelet time-frequency variation of the original signal of the bearing vibration to construct an image dataset, one-dimensional classification is into a two-dimensional image problem;Secondly, three lightweight convolutional neural network architectures are selected for the noise immunity test, to get the best noise-resistant network architecture GoogLeNet;Finally, hyper-parameter optimization is performed for the network, comparing the mesh method, the progressive mesh method, and the group optimization algorithm, respectively, and getting The optimal parameters are then analyzed for decision fusion and visualization of the network. To verify the method proposed in this paper, we used the open Case Western Reserve University bearing dataset. The experimental verification demonstrates that the proposed method achieves a correctness rate of 94.33% in decision fusion under noise, with good accuracy and stability. © 2024 ieee.

关键词： Mesh generation

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Research on Optimization Algorithm of image Recognition Performance Based on Convolution Neural Network computer Big Data Technology 2

Research on Optimization Algorithm of Image Recognition Perf...

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2nd ieee international conference on image processing and computer applications, icipca 2024

作者： Pan, Liqiong Anhui Sanlian University Anhui Hefei China

ISBN: (纸本)9798350360240

With the development of computer vision and deep learning technology, the application of fusion convolutional neural network (CNN) and big data technology in the field of image recognition is increasingly extensive and deep. In this paper, a fusion convolutional neural network model named CI-Net is designed to optimize image recognition performance based on CNN. The model uses the technology of integrating multi-size convolution kernel in the same layer structure to extract image features from different scales through multiple independent branches, which not only effectively broadens the width of the network to capture rich and diverse feature information, but also enhances the robustness and generalization ability of the network. In terms of model optimization, the paper abandons the traditional fully connected layer and adopts convolutional layer instead, which significantly reduces the total number of model parameters and effectively reduces the risk of overfitting. Experiments show that the structure design of CI-Net network is scientific and reasonable, and its convergence speed is faster in the training process. Under the premise of keeping the number of parameters far lower than that of similar convolutional neural network models, it can still achieve lower recognition error rate, fully demonstrating its superior performance and efficient generalization ability in image recognition tasks. This research is of great value for improving the performance of image recognition system based on computer big data technology. © 2024 ieee.

关键词： Convolutional neural networks

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Precise Movement Control of Agricultural Robot Car Based on PID Control Algorithm 2

Precise Movement Control of Agricultural Robot Car Based on ...

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2nd ieee international conference on image processing and computer applications, icipca 2024

作者： Xiao, Lingfei School of International Education Wuhan University of Technology Wuhan China

ISBN: (纸本)9798350360240

Proportional integral derivative (PID) algorithm control is the most common control method in robot control, and agricultural harvesting robots have been a very popular robot application in recent years. This algorithm can achieve precise movement of the car, thereby promoting the implementation of robot automation for picking. This article utilizes the simulation function in Simulink in Matlab software to explore the impact of different parameter choices in the PID process on control effectiveness. Based on the analysis of the results, appropriate parameters for the proportional, integral, and differential links were ultimately obtained, and ideal control effects were achieved. In the discussion section, this article gradually analyzed the role of each link in PID control and the different values of parameters that can lead to different control results. Based on the research results obtained, there are still many challenges and opportunities surrounding of intelligent agricultural robot. This article provides some suggestions for the future development and improvement of PID algorithm control, so that it can be applied to more complex situations and continue to explore new research directions and application scenarios. Through such efforts, it's believed that the intelligence of agricultural robot will become an important development direction in the future agricultural production field. © 2024 ieee.

关键词： MATLAB

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Athlete Score Prediction and Influencing Factors Analysis Based on SVM, RF and BP Neural Networks 2

Athlete Score Prediction and Influencing Factors Analysis Ba...

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2nd ieee international conference on image processing and computer applications, icipca 2024

作者： Kuang, Xi Wang, Chenfei Lu, Zihe International School of Business and Finance Sun Yat-sen University Guangzhou China School of Mathematics Sun Yat-sen University Guangzhou China

ISBN: (纸本)9798350360240

The study develops a score prediction model to evaluate player performance. The model is established with two metrics (one-step accuracy and five-step accuracy) to measure its precision. Three different algorithms, including Random Forest (RF), Support Vector Machine (SVM), and BP neural network, are tested. Results indicate that the optimal method is the Random Forest algorithm, achieving the highest five-step accuracy of 84.12%. Additionally, player performance visualization in the context of finals is presented, enhancing the analytical process. Furthermore, two approaches are used to examine whether an athlete's score is related to momentum. The findings suggest that momentum and scores do not conform to a random distribution. The study also highlights the importance and correlation of all factors. The pearson correlation coefficient between momentum and the last 5-point score is found to be 0.255, ranking third among the simplified 9 indicators, only surpassed by historical rankings and servers. This implies a correlation between momentum and player performance. © 2024 ieee.

关键词： Prediction models

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image Stitching Techniques Applied to Plane or 3-D Models: A Review

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ieee SENSORS JOURNAL 2023年第8期23卷 8060-8079页

作者： Fu, Mengyin Liang, Hao Zhu, Chunhui Dong, Zhipeng Sun, Rundong Yue, Yufeng Yang, Yi Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China Nanjing Univ Sci & Technol Sch Automat Nanjing 210014 Peoples R China Beijing Inst Technol Integrated Nav & Intelligent Nav Lab Beijing 100081 Peoples R China Beijing Inst Technol Natl Key Lab Autonomous Intelligent Unmanned Syst Beijing 100081 Peoples R China

image stitching is a technique in which multiple overlapping images of the scene are stitched together to generate an image with a wide view and high resolution. image stitching methods can be broadly classified into feature-based and deep learning methods. Feature-based methods use manually designed features to establish transformation relationships between multiple images. This technology has played an important role in medical, industrial, military, and other fields. With the rise of deep learning in computer vision, it has also become the mainstream method in the field of image stitching. This article provides a systematic literature review of image stitching techniques applied on the plane and 3-D models for both feature-based and deep learning methods. We divide the stitching methods into two categories, namely, mosaic stitching methods for generating stitched plane images and panoramic stitching methods for generating stitched panoramic images. Based on the camera type, it is further divided into pinhole camera plane stitching methods, pinhole camera panoramic stitching methods, fisheye camera panoramic stitching methods, and light field camera plane stitching methods. An extensive search was conducted in international conference on image processing (ICIP), ieee Transactions on image processing (TIP), international conference on computer Vision (ICCV), European conference on computer Vision (ECCV), ieee conference on computer Vision and Pattern Recognition (CVPR), British Machine Vision conference (BMVC), international conference on Pattern Recognition (ICPR), international Journal of computer Vision (IJCV), ieee/ASME international conference on Advanced Intelligent Mechatronics (AIM), ieee Transactions on Intelligent Transportation Systems (ITS), ieee Transactions on Pattern Analysis and Machine Intelligence (TPAMI), and ACM SIGGRAPH computer Graphics (SIGGRAFH) to summarize related image stitching techniques;89 articles are selected for systematic literatur

关键词： Cameras image stitching Pipelines Sensors Light fields Bibliographies Systematics Geometric correction image models image stitching reconstruction registration

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A BP Neural Network-based Approach to Intelligent computer image Recognition 3

A BP Neural Network-based Approach to Intelligent Computer I...

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3rd ieee international conference on Power, Electronics and computer applications, ICPECA 2023

作者： Ze, Jingwen Tianjin University of Technology Tianjin China

ISBN: (纸本)9781665472784

The computer image recognition model based on BP neural network can effectively identify the convergence recognition error. To solve the problems of low convergence speed and inability to quickly reduce recognition errors in the practical application of traditional image recognition methods, an optimized model for computer image recognition based on GA & BP deep neural network algorithm is proposed through computer image data acquisition and processing, image recognition model construction based on BP neural network and computer image recognition model training. © 2023 ieee.

关键词： image recognition

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