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检索条件"任意字段=3rd International Conference on Image Processing and Vision Engineering, IMPROVE 2023"

共 1700 条记录，以下是71-80 订阅

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Novel Preprocessing and Data Augmentation Method for Bearing Fault Diagnosis 3

Novel Preprocessing and Data Augmentation Method for Bearing...

引用

3rd international conference on Electrical Machines and Drives, ICEMD 2023

作者： Montasseri, Mohammad Mahdi Faiz, Jawad Parvin, Farbod Kalhor, Ahmad University of Tehran School of Electrical and Computer Engineering Tehran Iran

ISBN: (纸本)9798350393743

To avoid financial and life risks, early diagnose of bearing faults is particularly important, and this has made intelligent fault diagnosis popular. Intelligent fault diagnosis faces challenges such as the need for sufficient data and suitable performance for noisy data. This paper presents a preprocessing and data augmentation method to improve the performance of a few-shot learning method based on these two challenges. In this method first vibration signal is converted to two-dimensional images by means of mel spectrogram, then two image processing methods, namely, histogram equalization and gamma correction are applied. Finally, a data augmentation method based on gamma correction method is used. By using the Case Western Reserve University dataset, it is shown that the proposed method can improve the fault diagnosis process. © 2023 IEEE.

关键词： image enhancement

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Lane Environment Detection with only On-Board Camera 3

Lane Environment Detection with only On-Board Camera

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3rd international conference on Electronic Information engineering and Computer Communication, EIECC 2023

作者： Li, Chen Liu, Zhengqiong Ding, Zhizhong School of Computer Science and Information Engineering Hefei University of Technology Hefei China

ISBN: (纸本)9798350359961

Lane environment detection is a vital prerequisite for realizing autonomous driving in complex scenarios. However, existing methods face significant challenges, primarily containing the absence of lane-level positioning information, the imbalance between accuracy and processing speed, and the prohibitively high cost of equipment. To address these challenges, this paper introduces a hybrid method that combines deep learning and traditional image processing techniques. The proposed approach uses the YOLO (You Only Look Once) for environmental vehicle detection, augmented by a post-processing module that provides detailed lane-level positioning information. Notably, the post-processing stage integrates deep learning and traditional computer vision methods, ensuring a fast detection speed while maintaining a desirable detection accuracy. An additional advantage of the proposed method is its reliance solely on an on-board camera, effectively lowering the equipment cost. Furthermore, the method provides more comprehensive detection by incorporating geometric method to estimate the distance between the ego-vehicle and surrounding vehicles. The proposed approach addresses the aforementioned challenges and provides a comprehensive solution for effective and low-cost lane environment detection. To be specific, our proposed method runs at 58 fps and achieves accuracy at 95.22%. © 2023 IEEE.

关键词： Deep Learning image processing Lane Environment Detection Lane-level Positioning Vehicle Detection

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Review of Artificial Intelligence methods for detecting cancer in medical image processing 3

Review of Artificial Intelligence methods for detecting canc...

引用

3rd international conference on Artificial Intelligence and Smart Energy, ICAIS 2023

作者： Sarkar, Priya Saha, Ashim National Institute of Technology Agaratala Computer Science and Engineering Agartala India

ISBN: (纸本)9781665462167

Cancer is the unchecked spread of aberrant cells throughout the body. Cancer is a general word for a set of diseases brought on by the growth of abnormal cells in various bodily parts. Lung cancer, breast cancer, skin cancer, oral cancer, colon cancer, and prostate cancer are just a few of the more than a hundred different forms of cancer. Delays in treatment might result in major health problems and even death. This research reviews strategies for detecting liver, brain, and lung cancer using image processing. Automated and computer-aided detection systems (CAD) with artificial intelligence are the approaches utilized for detection, and these systems are good at processing big datasets to produce accurate and effective results in the detection of cancer. To be compatible with AI, these processing systems must overcome numerous obstacles, including picture capture, pre-processing, data management, segmentation, and classification algorithms. The numerous picture acquisition and segmentation approaches are reviewed in this work. These methods are now essential to meet the needs of the expanding patient population and to improve the healthcare system. © 2023 IEEE.

关键词： Diseases

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The Employment of AI and Machine Learning methods for Mushroom images Classification using Sequential CNN Model 3

The Employment of AI and Machine Learning methods for Mushro...

引用

3rd international conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Gill, Kanwarpartap Singh Anand, Vatsala Chauhan, Rahul Kukreti, Sanjeev Gupta, Rupesh Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun India Graphic Era Deemed to Be University Computer Science & Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350319125

The employment of AI and machine learning methods for mushroom classification has several potential uses and applications in the area of AI and related disciplines. It is possible to learn more about biodiversity and ecological health with the use of mushroom taxonomy. The use of AI models for mushroom identification and classification may help us better understand the ecology of a specific area. The work of mushroom picture classification may be both difficult and fascinating. Classifying mushrooms is crucial for a number of reasons, including determining which species are edible and which are dangerous, researching biodiversity, and ecological issues. This study uses a proposed Sequential Convolutional Neural Network (CNN) model for mushroom image classification. Pre-processing of data is done using the graphics processing unit (GPU). The Loss and Accuracy curves are then analysed to provide a visual representation of the Deep Learning model. The proposed model has an expected accuracy of 80%, which would facilitate further study into the categorization of mushrooms. © 2023 IEEE.

关键词： Artificial Intelligence Classification CNN Computer vision Deep Learning image processing Model Training Mushroom image Remote Sensing Sequential Model

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Bi-Cubic Interpolation image Scaling Algorithm Based on FPGA Implementation 3

Bi-Cubic Interpolation Image Scaling Algorithm Based on FPGA...

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3rd international conference on Computer Science, Electronic Information engineering and Intelligent Control Technology, CEI 2023

作者： Zhou, Zeng Wang, Yan Xu, Conghao Zhang, Yang University of South China College of Electrical Engineering Hengyang Hunan China

ISBN: (纸本)9798350372052

image resizing plays a very important role in image processing, offering good scalability and extensibility. Currently, there are some issues with existing scaling interpolation algorithms, such as complex hardware implementation, high resource consumption, long operation times, and poor realtime performance. To address these challenges, this paper introduces a design method based on the image bicubic interpolation scaling algorithm and optimizes the floating-point operation aspect of image processing. Utilizing the FPGA platform, a step-by-step scaling approach is employed, first performing vertical scaling and then horizontal scaling to achieve parallel processing. This design significantly improves scaling efficiency, and its feasibility is demonstrated through functional simulation. © 2023 IEEE.

关键词： Field programmable gate arrays (FPGA)

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A new algorithm for image edge detection based on phase stretch transform 3

A new algorithm for image edge detection based on phase stre...

引用

3rd international conference on Advanced Algorithms and Signal image processing, AASIP 2023

作者： Dandan, He Parthasarathy, Rajamohan School of Information Engineering PingDingShan University 467000 China Centre for Network Security and IoT Faculty of Engineering Built Environment and Information Technology SEGi University 47810 Malaysia

ISBN: (纸本)9781510668522

Edge is the important feature in images, which has important applications in image processing, image analysis and image understanding. Accordingly, Edge detection is the basic problem in image processing and computer vision, and is one of the important research content in image processing. In this paper, the edge detection principle of phase stretch transform is analyzed, and the mathematical expression of s-Type phase filter with linear or sublinear group delay is derived. Three new phase filters are designed and good edge detection results are obtained. The phase stretch transform based image edge detection is extended to the fractional-order Fourier transform domain, and the simulation results show that the phase stretch transform in the fractional-order Fourier transform domain can also be used for image edge detection and feature description. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Edge detection

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Research on the Application of Moving Target Tracking Based on Binocular vision 3

Research on the Application of Moving Target Tracking Based ...

引用

3rd IEEE international conference on Electronic Technology, Communication and Information, ICETCI 2023

作者： Xia, Xinwei Xuzhou University of Technology Xuzhou221000 China

ISBN: (纸本)9798350398410

Moving target tracking is one of the most popular research topics in machine vision and target tracking. Its primary purpose is to identify and locate target objects using cameras and other sensors, capture trajectories using depth and positioning results, enable robots and vehicles to avoid obstacles and help realize autonomous driving. Currently, the methods of moving target tracking include radar-based point cloud schemes and camera-based binocular vision schemes. The first scheme is difficult to implement at a low cost due to the cost of radar. The latter algorithm has high complexity, but the cost is low, which is one of the main current research directions. This paper focuses on the research of moving target tracking based on binocular vision. The researchers further construct the moving target tracking algorithm by studying the traditional tracking method and the principle of binocular range. In addition, the ROI extraction procedure was improved, depth and distance calculation methods were established, and global stereo matching, tracking, and positioning were realized. It is expected that the research in this paper can provide some reference for machine vision and robot vision obstacle avoidance. © 2023 IEEE.

关键词： Stereo image processing

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Research on image Restoration processing Based on Gaussian Blur Algorithm 3

Research on Image Restoration Processing Based on Gaussian B...

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3rd international Signal processing, Communications and engineering Management conference, ISPCEM 2023

作者： Tai, Li Zhang, Lin Zhou, Xinyu Zhang, Shiyu College of Information Engineering Liaoning Institute of Science and Engineering Jinzhou China School of Physical Education Bohai University Jinzhou China

ISBN: (纸本)9798350307023

Blur is a decrease in image clarity caused by incorrect focusing, motion blur, or lens distortion of the image. image restoration is the process of restoring degraded images as much as possible, following the inverse process of image degradation. Blur refers to the blurring or blurring of image details, and Gaussian blur is a commonly used model in image and signal degradation. This article is based on image processing theory. Firstly, a mathematical model for image degradation is constructed to lay the foundation for image restoration in response to this degradation process;Next, study Gaussian fuzzy logic and quantitatively describe the uncertainty within the image system using fuzzy membership functions;Then, the basic process of image restoration based on Gaussian blur algorithm is studied, including image denoising, obtaining the point spread function of the image, and using constrained least squares method to restore the image;Finally, study the quality evaluation of image restoration, including subjective and objective evaluations, and demonstrate the superiority of Gaussian blur algorithm through experimental comparative analysis. © 2023 IEEE.

关键词： image reconstruction

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processing landslide hazard prediction using satellite image segmentation 3

Processing landslide hazard prediction using satellite image...

引用

3rd international conference on Geographic Information and Remote Sensing Technology, GIRST 2024

作者： Chiu, Chenyuan Hsu, Sung-Chi Huang, Yishuo Zhang, Nan Peng, Chen Labib Ul Islam, Syed School of Civil Engineering Jiaying University Guangdong Meizhou514015 China Dept. of Construction Engineering Chaoyang Univ. of Technology 168 Jifong East Rd. Wufong Taichung41349 Taiwan School of Engineering University of Birmingham Edgbaston BirminghamB15 2TT United Kingdom Dept. of Civil Engineering University of Birmingham Edgbaston BirminghamB15 2TT United Kingdom

ISBN: (纸本)9781510689077

Remote sensing technology has become one of the indispensable techniques for the investigation of geologic disasters at home and abroad. However, the interpretation of bare ground classification by satellite multispectral images is often interfered by atmospheric clouds in mountainous areas, shadows on the mountains, and the phenomenon of Speckle Signals, which seriously affects the correctness of the interpretation images, and is prone to errors such as misjudgment or omission. In this study, the SPOT 5 satellite images of the study area were segmented by applying the Multiphase Level Set Framework using MATLAB computation. The results show that the problem of the speckle signals can be eliminated efficiently and quickly, and the complex terrain and features can be segmented into simple objects, which can solve the problem of the spatial recognition rate without being affected by the resolution of the image itself, and improve the image accuracy. This can solve the problem of spatial recognition without being affected by the resolution of the image itself, and improve the accuracy of satellite image interpretation, which can be used as a possible landslide trend. © 2025 SPIE.

关键词： Landslides

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improved clothing segmentation algorithm based on Deeplabv3 + network 3

Improved clothing segmentation algorithm based on Deeplabv3 ...

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3rd international conference on Computer vision and Pattern Analysis, ICCPA 2023

作者： Shi, Xiujin Wu, Danxin Donghua University Songjiang Shanghai China

ISBN: (纸本)9781510667563

In recent years, the fashion industry has developed rapidly, and the image is one of the main carriers for people to obtain clothing information. Therefore, the analysis and processing of clothing image through the technology in the field of artificial intelligence has become a major research content in the field of clothing development. image segmentation technology can be used to segment different clothing regions from the clothing image in order to facilitate the subsequent analysis and processing of clothing image, which is one of the basic research directions of clothing image analysis. At present, most of the methods belong to the traditional segmentation methods, or are based on the deep convolutional neural network (DCNN). Especially for Deeplab, FCN and their improved network segmentation models, although these network models have different degrees of improvement compared with the traditional methods, there are still some problems such as the fitting degree of clothing region segmentation is not high and the effect of clothing edge segmentation is poor. To solve these problems, this paper adds object context information extraction module and edge optimization module based on Deeplab v3+ network. The object context information extraction module focuses on the context information aggregation method in semantic segmentation, and enhances the feature representation of pixel points with the help of the feature information of the corresponding clothing region, so as to improve the label classification effect of pixel points in the clothing image and improve the fitting degree of clothing region segmentation. The edge optimization module makes the pixels in the edge area directly use the label prediction of the pixels in the same category area to greatly improve the edge segmentation effect. Our method performs well on the DeepFashion-MultiModal dataset, and experiments show that the improved Deeplabv3 + network outperforms the original Deeplabv3 + network and oth

关键词： Pixels

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