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

共 1688 条记录，以下是51-60 订阅

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HCT: Hybrid CNN-Transformer Networks for Super-Resolution 3

HCT: Hybrid CNN-Transformer Networks for Super-Resolution

引用

3rd international conference on Consumer Electronics and Computer engineering, ICCECE 2023

作者： Zhang, Jiabin Wang, Xiaoru Xia, Han Li, Xiaolong Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9798350331578

Recently, several computer vision tasks have begun to adopt transformer-based approaches with promising results. Using a completely transformer-based architecture in image recovery achieves better performance than the existing CNN approach, but the existing vision transformers lack the scalability for high-resolution images, which means that transformers are underutilized in image restoration tasks. We propose a hybrid architecture (HCT) that uses both CNN and transformer to improve image restoration. HCT consists of transformer and CNN branches. By fully integrating the two branches, we strengthen the network's ability of parameter sharing and local information aggregation, and also increase the network's ability to integrate global information, and finally achieve the purpose of improving the image recovery effect. Our proposed transformer branch uses a spatial fusion adaptive attention model that blends local and global attention improving image restoration while reducing computing costs. Extensive experiments show that HCT achieves competitive results in super-resolution tasks. © 2023 IEEE.

关键词： image reconstruction

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image Edge Detection Method in Bridge Structure Safety Assessment 3

Image Edge Detection Method in Bridge Structure Safety Asses...

引用

3rd international conference on Digital Data processing, DDP 2023

作者： Xu, Xin Sichuan University Jinjiang College Meishan Sichuan China

ISBN: (纸本)9798350329018

Bridge transportation plays a decisive role in China's transportation industry. It can be said that it is the lifeline of transportation, and the bearing capacity of bridges is also the key. Based on the in-depth study of the application of mathematical morphology in edge detection, this paper proposes an image edge detection method in bridge structure safety assessment. It uses this method to extract image edges. Secondly, the scale of morphological structure elements is adjusted to synthesize the edge features of each scale, and an ideal image edge is obtained in the presence of noise. The comparative experimental study on crack detection of in-service bridges under outdoor conditions shows that the system has high engineering application value for long-distance, non-destructive testing of minor bridge damage. At the same time, the final research conclusion also shows that when using digital image edge detection technology for bridge structure safety assessment, it will help to improve the speed of bridge online detection and reduce the cost of detection, which is of great significance to promote the development of bridge detection technology. © 2023 IEEE.

关键词： Edge detection

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Object Size Detection Based on image processing 3

Object Size Detection Based on Image Processing

引用

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

作者： Zhao, Mingrui Shi, Chunjing School of Mechanical Engineering Shenyang Ligong University Shenyang110159 China

ISBN: (纸本)9781510667563

This paper proposes a novel method for detecting object sizes in images. Traditional methods have limitations due to image quality, which affects detection accuracy. To address this problem, a new approach is proposed that combines Zernike moments and deep learning models. This approach combines the strengths of both methods and overcomes the limitations of traditional approaches. The proposed approach improves the accuracy of object size detection and is robust to image noise and distortion. Experimental results demonstrate the effectiveness of this approach, which outperforms existing methods in terms of accuracy and robustness. © 2023 SPIE.

关键词： Deep learning

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Computer vision assisted Deep Learning based Sitting Posture Suggestion System 3

Computer Vision assisted Deep Learning based Sitting Posture...

引用

3rd international conference for Innovation in Technology, INOCON 2024

作者： Vagale, Ghanashyam Prajwal, G.K. Kumar, Rohit Dharishini, P. Padma Priya Ramaiah University of Applied Sciences Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350381931

This paper presents an innovative framework that employs camera-captured visual data to detect and suggest optimal sitting postures. The framework consists of two crucial components: a video capture object and an object detection system that incorporates Deep Learning to enhance efficiency and reliability. The camera initially captures the user's posture image, which is then subjected to video processing to extract video metadata. Subsequently, the object is created by extracting the image from the video, and the object detection algorithm is applied to provide posture recommendations. The algorithm continually monitors posture correctness and provides suggestions to improve it as necessary, ultimately benefiting the user's daily life and mitigating potential long-term problems. Additionally, the algorithm can be further developed to recognize posture patterns and suggest corrective exercises or techniques. Furthermore, the algorithm's efficiency can be enhanced by optimizing landmark detection for more effective outcomes. This cutting-edge framework offers immense potential for improving posture and overall health, and its development can significantly enhance the quality of life for individuals. © 2024 IEEE.

关键词： Cameras

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Dehua porcelain product defect recognition based on image processing 3

Dehua porcelain product defect recognition based on image pr...

引用

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

作者： Gao, Siqi Fuzhou University Xiamen Institute of Arts and Craftsline China

ISBN: (纸本)9781510668522

In recent years, with the continuous development of the ceramic industry, the problem of Product defect has become increasingly prominent. Therefore, this article conducted recognition research on Dehua porcelain through image processing technology. Firstly, the background and research significance of this article were elaborated;Secondly, a detailed analysis was conducted on the detection technology and application status of powder and particle size in ceramic processing;Next, the principle and implementation process of defect recognition based on machine vision were elaborated, and experimental results were compared and error corrected. At the same time, improved algorithms were proposed to improve identification accuracy;Finally, this paper tests the performance of the porcelain Product defect recognition model, and the test results show that the resource occupancy rate of this model is between 2% and 4%, and the defect recognition rate is relatively high, which can meet the demand. Compared with the traditional visual inspection, the defect recognition method based on image processing can realize the automatic detection process. It can improve production efficiency and reduce the impact of human errors and subjective judgments. This method can identify the defects of Dehua porcelain products in real time and reduce the time and labor cost of traditional detection methods. © COPYRIGHT SPIE.

关键词： image processing

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Classification of Diabetic Retinopathy Using image Pre-processing Techniques 3

Classification of Diabetic Retinopathy Using Image Pre-proce...

引用

3rd IEEE international conference on Intelligent Technologies, CONIT 2023

作者： Duvvuri, Kavya Kanisettypalli, Harshitha Nikhil, Masabattula Teja Palaniswamy, Suja Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798350338607

Most diseases that affect humans cause an effect on the eyes which is observed first by a physician while treating a patient. Eye diseases are conditions that affect the functioning of the eye and lead to loss of vision. Globally, at least 2.2 billion people have near or distant vision impairment. Most of these eye diseases have long-term effects so, early detection of the eye disease is very important to avoid the consequences. Some eye diseases include cataracts, glaucoma, diabetic retinopathy, and retinal detachment, etc. Almost 10% of people are facing an eye disease called diabetic retinopathy (DR). In this paper, multiclassification of DR images has been done after performing a few preprocessing techniques like erosion, histogram equalization, and comparing the accuracy of the CNN model before and after the pre-processing. The classification model yielded an accuracy of 86.4% on retinal fundus images filtered from the Kaggle DR detection database. The obtained results show that the proposed preprocessing methods are best suitable for diagnosing diabetic retinopathy from retinal fundus images. © 2023 IEEE.

关键词： image classification

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Frequency Decomposition Network for Fast Joint image Demosaic, Denoising and Super-Resolution 3

Frequency Decomposition Network for Fast Joint Image Demosai...

引用

3rd international conference on Consumer Electronics and Computer engineering, ICCECE 2023

作者： Niu, Guangxiao School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798350331578

image denoising (DN), demosaicing (DM) and super-resolution (SR) are the key tasks of the low-level vision. Joint demosaicing, denoising and Super-resolution (JDDSR) can effectively improve the image quality. However, the previous methods explored the feasibility of tasks more than the characteristics of DM, DN and SR. Meanwhile, the joint training also brought computational burden and the three tasks process information at different frequencies. DN and DM pay more attention to low-frequency (LF) information, while SR is used to recover the lost high-frequency (HF) information. In this work, we use the way of Laplace pyramid to separate the HF and LF of the image, and use different branches to learn the information of different frequencies. In order to reduce the computational burden, we redesign the network architecture and use the form of non-parametric up-sampling to generate the results. Experiments demonstrate that our method can achieve results similar to existing methods with very small computational effort and storage. © 2023 IEEE.

关键词： image denoising

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Research on Building Crack Width Monitoring Method Based on Computer vision 3

Research on Building Crack Width Monitoring Method Based on ...

引用

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

作者： Wang, Houyan Xiao, Shuoting Ningbo Youzuo Construction Technology Company Zhejiang Ningbo China Institute of Civil Engineering and Architecture Ural Federal University Ekaterinburg Russia

ISBN: (纸本)9798350307023

As construction projects enter a new era, building crack width monitoring has entered a stage of high-quality development. It is necessary to propose image processing propositions that better meet the needs of crack width monitoring for building safety by focusing on the deep learning idea with image segmentation as the core. Based on the dynamic evolution of image processing development, according to the internal logic of the convolutional neural network, a theoretical analysis framework for the development of image segmentation is constructed. It can explain the image segmentation development mechanism jointly generated by the image processing mechanism and the optimization loop mechanism involved in image sampling and segmentation. From the perspective of quality change and practical deduction of image segmentation development, the possibility of moving towards the high-quality development goal of building crack width monitoring is further explored. The purpose of developing building crack width monitoring is to provide crack width estimates that meet expected standards for construction projects, which is committed to continuously improving the quality of crack width and improving the satisfaction of crack width monitoring. Therefore, it is necessary to strengthen the image segmentation control based on the quality of the inner loop of the convolutional neural network. Establishing an interaction and feedback mechanism between image segmentation and perception of the quality of a construction project, as well as establishing an evaluation system for image segmentation and monitoring of the crack width of a building, which will achieve high-quality development of building crack width monitoring, promote construction project safety, and truly meet the needs of construction projects. © 2023 IEEE.

关键词： image segmentation

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Infrared small target recognition in waterways based on YOLOv5 algorithm 3

Infrared small target recognition in waterways based on YOLO...

引用

3rd international conference on image processing and Intelligent Control, IPIC 2023

作者： Fan, Yikai Zhang, Yingjun Dalian Maritime University Dalian116026 China

ISBN: (纸本)9781510668140

YOLOv5 is one of the target detection algorithms with fast detection speed and high accuracy, but it has the problems of insufficient sensory field and low accuracy of small target detection. In order to solve above problems, an improved YOLOv5 network model, i.e., an improved YOLOv5-TI model based on the attention mechanism, is proposed. The attention module is added to the backbone network when extracting features to improve the target detection accuracy, and the input features are shifted windowed for self-attention calculation to effectively utilize the features and improve the small target detection accuracy;the proposed model YOLOv5-TI is experimented on the self-built inland infrared dataset, and the mAP value reaches 95.5%, and the results show that YOLOv5-TI can effectively improve the target detection accuracy. The inland vessels equipped with visual intelligent perception system can effectively identify the targets on water, and they have wide applications in the fields of surface exploration and autonomous search and rescue. © 2023 SPIE.

关键词： Computer vision

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Development of A Classification Architecture for images Represented Using Quantum Theory : ∗using IBM QISKIT Liberaries 3

Development of A Classification Architecture for Images Repr...

引用

3rd international conference on Artificial Intelligence and Signal processing, AISP 2023

作者： Singh, Barkha Indu, S. Majumdar, Sudipta Delhi Technological University Department of Electronics and Communication Engineering Shah-bad Daulatpur Main Bawana Road Delhi110042 India Delhi Technological University Department of Electronics and Communication Engineering Delhi110042 India

ISBN: (纸本)9798350320749

Quantum image processing is paramount in image processing-based research areas nowadays due to the less computational time. This paper presents the quantum mechanic-based image denoising model. Also, This article discusses the quantum boolean mean filter and new quantum-boolean image denoising that are connected to computational basis states. This paper describes basic quantum information processing principles, new and better image representation approaches, quantum image processing implementation problems, quantum boolean mean filtering approach for image denoising observation using appropriate matrice, and finally, a work proposal for future work and conclusion. A comparison table of different classification techniques is also included in the paper. © 2023 IEEE.

关键词： image denoising

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