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Att-CapsViT: Attention-based CapsNet and vision Transformer Hybride Network for Handwriting Chinese Characters Recognition

Att-CapsViT: Attention-based CapsNet and Vision Transformer ...

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Algorithm, image processing and Machine vision (AIPMV), international conference on

作者： Xiangling Zheng Nanxiang Zhou Zibin Geng School of Information Technology & Management University of International Business and Economics Beijing China School of Computer Science and Artificial Intelligence Wuhan University of Technology Wuhan China

ISBN: (数字)9798350390254

ISBN: (纸本)9798350390261

The research on text recognition has long been under the close scrutiny of scholars, especially the recognition of complex Chinese characters, which is highly favored by the academic community. In recent years, handwriting recognition has achieved remarkable results within the traditional framework of deep learning. However, this approach still falls short in capturing the subtle features and spatial hierarchical relationships within Chinese characters. Furthermore, the complexity of Chinese handwritten characters and the diversity of writing styles pose higher challenges to models. To address this, we propose an attention-based CapsN et and vision transformer hybride network for handwriting Chinese characters recognition (Att-CapsViT), which combines the attention mechanism with capsule networks and visual transformer. The Att-CapsViT model integrates the attention mechanism, combining the local detail feature capturing capability of capsule networks (CapsNets) with the global contextual awareness of visual transformer (ViT) to achieve multi-level feature extraction from local to global. This experiment was validated using two open-source datasets, successfully confirming the robust performance of the Att-CapsViT model in the recognition of Chinese handwritten characters, providing a new perspective for the field of complex text recognition.

关键词： Handwriting recognition computer vision Visualization Attention mechanisms Text recognition Diversity reception Feature extraction Transformers Complexity theory Character recognition

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Automatic Vehicle Damage Detection Classification framework using Fast and Mask Deep learning 2

Automatic Vehicle Damage Detection Classification framework ...

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2nd international conference on computer Science, Engineering and Applications, ICCSEA 2022

作者： Reddy, Dasari Anantha Shambharkar, Saroj Jyothsna, Kandi Kumar, V Manoj Bhoyar, Chandrashekhar N Somkunwar, Rachna K. Computer Science and Engineering Klef Aziznagar Hyderabad India K.I.T.S.Ramtek Department of Information Technology Nagpur India Computer Science and Engineering Mlrit Dundigal Hyderabad India Computer Science and Artificial Intelligence SR University Ananthsagar Warngal India Priyadarshini College of Engineering Electronics Engineering Department Nagpur India Dr. D. Y. Patil College of Technology Pimpri Department of Computer Engineering Pune India

ISBN: (数字)9781665458344

ISBN: (纸本)9781665458344

Now a day's road accidents occurred very frequently in every part of the world. Damage investigation and detection after every accident becomes a very challenging and primary job for vehicle insurance department personnel and vehicle maintenance and rental industries. There is a necessity for some Automatic Vehicle Damage Detection System with the capability of detecting the minor and major damages that are occurred in a vehicle after any type of accident so that an appropriate appraisal can be made and a suitable amount paid to a guilty customer. There is an extensive growth in technology, which leads to the extensive development in computer System vision techniques. A tremendous amount of computer vision technology has been applied to various real-world life problems. There is a need for an effective automatic vehicle damage system to reduce the vast need for manual manpower required for this damage detection work after the occurrence of an accident. In most of the existing research work, the major problem faced by the researchers is there exist very few datasets to train the system which can be used to classify the damage. And the major challenge is the accuracy of those classifiers to classify the vehicle images into their appropriate damage classes. As the number of vehicles on the roadside goes on increasing, there is a need for automatic systems to ease the post-accident or damage processing goes on increasing. In our research work, we have proposed a Deep Learning-based Vehicle Damage detection assessment algorithm. The algorithm is based on image classification and objection detection methodology capable of automatically determining the actual damage position in a vehicle, degree, and type of damage from the various images received from the guilty user or from any person which will help them to provide a suitable maintenance amount,the calculation done by the insurance company. The proposed work provides a brief idea about the damages occurred during accide

关键词： Object detection

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LFRNet: Low-Light Face Super-Resolution With Light Frequency Representation

LFRNet: Low-Light Face Super-Resolution With Light Frequency...

引用

Algorithm, image processing and Machine vision (AIPMV), international conference on

作者： Bingxin Zha Huijie Zhu Haolin Zhang Shengying Yang School of Information and Electronic Engineering Zhejiang University of Science and Technology Hangzhou China Huzhou Zhongke Fanzai Electric Power Technology Development Co. Ltd Huzhou China Arm Technology (China) Co. Ltd Shanghai China

ISBN: (数字)9798350390254

ISBN: (纸本)9798350390261

In the field of computer vision, the task of facial super-resolution (FSR) is crucial for applications such as surveillance and photo restoration. However, factors such as noise and artifacts in real-world scenarios severely degrade image quality. Although existing methods use geometric priors and facial heatmap localization to improve FSR performance, these priors are often inaccurate under low-light conditions, affecting the results. This paper proposes an innovative low-light facial enhancement network aimed at integrating FSR and low-light enhancement tasks. We designed a Light Frequency Inference Block (LFIB) to capture and refine brightness and texture features at different frequencies, combining it with Transformer modules in an Encoder-Decoder architecture. The LFIB module separates degraded images into low-frequency and high-frequency brightness features and employs spatial cross-attention to capture facial texture details, effectively addressing the degradation issues in facial images. Experimental results demonstrate that our method excels in the low-light facial super-resolution task, outperforming existing methods on various metrics. Additionally, it shows good generalization capabilities in real-world scenarios, confirming its potential for practical applications.

关键词： Measurement image quality Visualization Superresolution Brightness computer architecture Transformers Feature extraction image restoration Faces

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A Strabismus Detection System Based on Digital Twin

A Strabismus Detection System Based on Digital Twin

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Sensors, Electronics and computer Engineering (ICSECE), IEEE international conference on

作者： Guohua Xiong Xianfeng Wang Guangdong Construction Polytechnic Guangzhou China

ISBN: (数字)9798350373646

ISBN: (纸本)9798350373653

Strabismus is a disease caused by extraocular muscles, which not only has serious consequences on vision, but also has a serious impact on patients' psychology, work and employment, quality of life, and other aspects. With the development of deep learning and artificial intelligence, modern computational image processing technology has been further improved, especially in areas such as image denoising, edge detection, image segmentation, target recognition, feature extraction, etc., making computer-aided intelligent strabismus diagnosis technology increasingly attracting the attention of researchers. However, the research in the field of intelligent strabismus diagnosis is not yet mature. This article constructs twin data based on the eye images of strabismus patients, references typical strabismus sign detection data and image libraries, and then uses the improved Faster R-CNN algorithm to implement an intelligent strabismus detection system based on “digital twins”, providing a certain reference for the in-depth research of intelligent strabismus detection and diagnosis.

关键词： image segmentation Target recognition image edge detection Psychology Muscles Sensor systems Libraries Digital twins Sensors image denoising

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Research on blood type card image automatic recognition algorithm 2

Research on blood type card image automatic recognition algo...

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2nd international conference on computer vision, image, and Deep Learning

作者： Wang, Meijiao Ji, Xiaoqiang Changchun College of Electronic Technology Jilin Changchun130114 China

ISBN: (数字)9781510646827

ISBN: (纸本)9781510646810

ABO blood type automatic recognition can realize the automatic detection of blood type, effectively improve the speed and accuracy of blood type analysis, and has a wide range of clinical applications. In this paper, an automatic blood type recognition algorithm based on image processing is designed. Firstly, the image of blood type card is preprocessed, including image enhancement and median filtering. Then, the micro column tubules are segmented by template matching. Then, the threshold analysis is carried out, and the gray image is transformed into a binary image. Finally, the distribution of red blood cell aggregates in micro column tubules is identified, and determine the ABO blood type of the sample. The experimental results show that the algorithm can effectively segment the micro column tubules on the blood type card, realize the effective recognition of red blood cell distribution, and complete the effective interpretation of blood group. © 2021 SPIE.

关键词： Template matching

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Non-Linear Time Sequence Problems Based on GA-BP Neural Network Prediction Model

Non-Linear Time Sequence Problems Based on GA-BP Neural Netw...

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image processing and computer Applications (ICIPCA), IEEE international conference on

作者： Teng Wang Yuxuan Chen College of Technology and Data Yantai Nanshan University Yantai China School of Software Engineering South China University of Technology Guangzhou China

ISBN: (数字)9798350360240

ISBN: (纸本)9798350384161

In the fields of image recognition and computer vision, data mining and predictive analytics, BP neural network as a kind of simple structure and powerful model has been widely concerned by scholars, and the improvement of BP neural network is very important. In this paper, we used genetic algorithm to optimize BP neural network, and optimize the selection of weights and thresholds of BP neural network through the characteristics of local optimization of genetic algorithm. Compared with the single BP neural network, the root mean square error (RMSE) of the optimized BP neural network had been reduced by 0.82%, the mean absolute error (MAE) had been reduced by 0.85%, and the coefficient of determination had been improved by 1.34%• The experimental results have shown that the optimization of BP neural network with genetic algorithm is more advantageous than the single BP neural network, and the optimized BP neural network has a strong ability to deal with the complex problems with high dimensions and multi-objectives.

关键词： Machine learning algorithms image recognition image coding Neural networks Predictive models Genetics Root mean square Predictive analytics Optimization Genetic algorithms

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Measurement Method of Key Parameters of Automobile Connector Based on Binocular Stereo vision

Measurement Method of Key Parameters of Automobile Connector...

引用

Sensors, Electronics and computer Engineering (ICSECE), IEEE international conference on

作者： Guangze Bai Jun Gu Yan Xing Guiyan Xue Xu Guo Wanze Song Congbo Luo Yu Gu Lai Wei Xue Liu Automotive Institute Changchun Vocational and Technical College Changchun China Changchun Jielin Intelligent Technology Co. Ltd Changchun China Changchun City Construction Engineering School Changchun China Changchun Automotive Technical College Changchun China Xinhe Measurement and Control Technology (Changchun) Co. Ltd Changchun China

ISBN: (数字)9798350373646

ISBN: (纸本)9798350373653

As a kind of interconnection device widely used in automobile, the quality and reliability of automobile connector directly affect the overall safety of automobile. As a key parameter of connectors, pin spacing has important effects on electrical characteristics and durability of connectors. Therefore, a method for measuring pin spacing of automotive connectors based on binocular stereo vision is proposed in this paper. Firstly, a visual measurement system is designed in this paper, which provides hardware support for pin detection and pin spacing measurement of the connectors. After that, this paper uses the image processing method to detect the pin of the car connector, mainly carries out the pre-processing of the car connector image and edge feature extraction, and on the basis of the edge feature extraction, the corner feature detection is carried out, and finally the pin detection of the car connector is completed. Finally, according to the detection results of the car connector pins, the paper adopts binocular stereo vision method to measure the pin spacing of the connector pins, and the final measurement accuracy reaches millimeter level.

关键词： Connectors Visualization image edge detection Feature extraction Pins Stereo vision Sensors Safety Automobiles Reliability

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Design and Analysis of Efficient On-Star Compression Algorithms for FPGA Platforms

Design and Analysis of Efficient On-Star Compression Algorit...

引用

computer, vision and Intelligent Technology (ICCVIT), international conference on

作者： Guoliang Zhang Xueying Wang Jianpeng Fan College of Electronic Science and Technology National University of Defense Technology Changsha China

ISBN: (数字)9798331540043

ISBN: (纸本)9798331540050

image compression has always been a hot topic in the field of aerospace. The onboard processing unit, due to its background of deep space operation, has greatly limited performance in terms of data storage and data transmission. Satellite remote sensing images are developing towards larger size, higher frame frequency, and more frequency bands, which increasingly demand storage space and processing power. There is an urgent need for a set of efficient and lightweight image compression hardware and software. Therefore, in this work, we have improved the Context-Based Adaptive Lossless image Compression (CALIC) algorithm, including error quantization and entropy coding, to meet the requirements for on-orbit processing in terms of compression time and compression ratio. At the same time, aiming at the working scenario of on-orbit processing, we chose Field-Programmable Gate Array (FPGA) as the platform to deploy the improved CALIC and designed the arithmetic coding Av value calculation process in parallel on this basis, which greatly reduced the processing time on the hardware side. We have carried out simulation verification of this paper’s image compression algorithm on the KODAK24 and SZTAKI_Airchange datasets. The experimental results show that our method strikes a good balance between compression ratio and compression rate compared with typical compression algorithms, making it suitable for on-orbit processing scenarios.

关键词： Technological innovation image coding Quantization (signal) Software algorithms Hardware Software Field programmable gate arrays Remote sensing Compression algorithms Arithmetic

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Research on the Application of Classical Painting Restoration Based on Improved GAN Model

Research on the Application of Classical Painting Restoratio...

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Electrical, Automation and computer Engineering (ICEACE), international conference on

作者： Jianlong Bi Siyu Yao Northwest Normal University Lanzhou China Ocean University of China Qingdao China

ISBN: (数字)9798350368208

ISBN: (纸本)9798350368215

This study aims to advance image repair technology. By introducing an algorithm based on improved GAN, we design a novel generator network structure and an improved discriminator network to improve the clarity of generated results, the accuracy of edge structures, and reduce the time consumption of the repair process. The experimental results show that our algorithm significantly improves performance in a variety of image restoration tasks. Although the model performs well in handling classic paintings with different levels of damage and artistic styles, there are still challenges in handling special textures and severe damage. The improvements we suggest include experimenting with more advanced optimization algorithms and adding diverse training data. This study provides a valuable reference for researchers in the field of computer vision and image processing. We look forward to continuing to optimize algorithms and expand application scenarios in the future to make greater contributions to the development of image restoration technology.

关键词： Heuristic algorithms image edge detection Computational modeling Training data Maintenance engineering Generative adversarial networks Generators image restoration Optimization Painting

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image Recognition in Low-Light Conditions with Deep Learning Model

Image Recognition in Low-Light Conditions with Deep Learning...

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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), international conference on

作者： R Balakrishnan Ruchira Rawat Sathish Kannan Warish Pate Harshal Pati S. Rajkumar Department of Computer Science SMS College of Arts and Science Coimbatore Tamil nadu India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of VLSI Microelectronics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Computer Science and Engineering Parul Institute of Engineering and Technology Parul University Post Limda Waghodia Gujarat India Computer Science and Engineering Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune Maharashtra India Department of Mechanical Engineering Faculty of Manufacturing Institute of Technology Hawassa University Ethiopia

Object identification has evolved as a critical application in computer vision during the past decade, experiencing intensive research, and broad practical implementations. While image processing normally works well in regular lighting circumstances, issues develop in low-light scenarios where images become chaotic and dark, causing difficulty for later computer vision tasks. However, conventional object detection algorithms frequently fail in low-light situations. This study investigates obj ect identification in extremely low-light circumstances in detail, outlining critical design concepts required for a robust low-light detection system. Colour distortion, loss of information, and blurriness are common in Low-Light images (LLI) acquired in poor lighting conditions, reducing the accuracy of obj ect recognition tasks. We use low-light object identification methods like Single Shot Multibox Detector (SSD) and Faster Region-based Convolutional Neural Network (R-CNN), together with image enhancement techniques like Multi-Branch Low-Light Enhancement Network (MBLLEN) and RetinexNet, to improve the precision of obj ect detection in LLI. On the publicly accessible ExDark dataset, the suggested method is trained and evaluated. The experimental findings show that combining MBLLEN image enhancement and SSD object identification resulted in greater precision, recall, and mean Average Precision (mAP) values of 0.77, 0.68, and 0.587, respectively. This study helps to understand and enhance object identification capacity in challenging low-light environments.

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