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5th International conference on Communications, Signal processing, and their Applications (ICCSPA)

作者： Mahmoud, Adel Farid, Samuel Maged, Mark Mohamed, Othman Karam, Reham Salah, Khaled El-Kharashi, M. Watheq Ain Shams Univ Dept Comp & Syst Fac Engn Cairo Egypt Siemens Digital Ind Software Fremont CA 94538 USA

ISBN: (纸本)9781665482370

Data compression is a trending field that is used in data storage and data transmission systems. Lossy compression means that data cannot be completely retrieved while in lossless compression the compressed data must be reconstructed exactly. Lossless data compression is used in compressing binary files, telemetry data and high-fidelity medical and scientific images where details are crucial. There is no generic compression algorithm that gives best compression ratio on all data pattern. In this paper, we propose a hybrid lossless hardware architecture that compresses most of data patterns such as repeated data, Gaussian distribution data and images. A profiling-before-compressing and then choosing the right compression hardware is proposed. The proposed design is a highly parallelized architecture that can compress/decompress 64 bytes/cycle with minor overhead. Moreover, it provides high compression ratio on small block sizes as well as large ones.

关键词： Lossless Data Compression Lossless image Compression Hardware Accelerator Compression algorithms Rice-Golomb Predictive Coding XORing

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Research on the Application of image Recognition and processing Technology in Integrated Media Information Dissemination

Research on the Application of Image Recognition and Process...

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Digital Analysis and processing, Intelligent Computation (DAPIC), International conference on

作者： Shiyu Pan Jiangsu College of Tourism Yangzhou Jiangsu China

ISBN: (数字)9798331536169

ISBN: (纸本)9798331536176

With the rapid development of the era of integrated media, the form of information dissemination is becoming more and more diversified, in which the image as an important carrier of information dissemination, its quality and processing efficiency directly affect the dissemination effect. By virtue of its unique advantages in data analysis, image optimization and content distribution, image recognition and processing technology has become an important support means in the field of integrated media information dissemination. This paper firstly analyzes the current situation of the application of image recognition and processing technology in the information dissemination of integrated media; secondly, it discusses the core methods of image recognition and processing technology, including deep learning algorithms, feature extraction technology and real-time image processing strategies; finally, it elaborates the specific application effects of these technologies in the fields of news reporting, advertising and marketing, and live video broadcasting through practical application examples. The study shows that image recognition and processing technology not only improves the intelligent level of integrated media content production, but also provides new possibilities for the innovation of information dissemination mode. In the future, with the further improvement of algorithm performance and the technological integration of multimedia platforms, this field is expected to gain wider application.

关键词： Industries Technological innovation image recognition Face recognition Production Media Real-time systems User experience Optimization Standards

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Presenting a Method for Gluing Error Detection Using image processing

Presenting a Method for Gluing Error Detection Using Image P...

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International conference of Signal processing and Intelligent systems (ICSPIS)

作者： Moein Shishebor Vali Derhami Masoud Khouri Computer Engineering Department Yazd University Yazd Iran

ISBN: (数字)9798331532543

ISBN: (纸本)9798331532550

The application of artificial intelligence and image processing for automatic fault detection in industrial settings holds substantial significance. However, image processing systems encounter challenges in achieving accurate automatic detection, including variable lighting conditions, low image quality, and the complexity of observed objects. This study specifically addresses the detection of gluing errors in automotive parts. An improved template matching algorithm is employed to identify boundary errors. The proposed method incorporates SIFT (Scale-Invariant Feature Transform) for feature extraction, allowing for the identification of key local features in images regardless of changes in scale, rotation, or lighting conditions. To address variations in scale and alignment, affine transformations are employed to standardize the images, ensuring consistent dimensions and orientations. After this preprocessing step, brute-force matching techniques are utilized to accurately match the extracted features from the modified images with a target reference, refining the output and enhancing the detection accuracy of improperly glued areas. Results from executing the algorithm on input images demonstrate the high performance of the proposed method in gluing error detection.

关键词： Accuracy Refining Signal processing algorithms Lighting Transforms Signal processing Feature extraction Reliability Standards Automotive engineering

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A Study of Machine Learning and Deep Learning algorithms for Deep Fake images Prediction

A Study of Machine Learning and Deep Learning Algorithms for...

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Data Science and Network Security (ICDSNS), International conference on

作者： Padmavati E. Gundgurti K. Baburao Department of Computer Science and Engineering CMR University Bengaluru India

ISBN: (数字)9798350373110

ISBN: (纸本)9798350373127

Recent developments in synthetic data have made it feasible to develop high-quality images in a way that humans cannot recognize the difference between images generated through Artificial Intelligence (AI) and real-time images. AI and Machine Learning (ML) in general have shown impressive results recently in a variety of tasks including natural image processing, particularly with the development of Deep Learning (DL). Particularly, image classification in medical images requires a high degree of accountability and reliability. However, previous researches require further analysis, often with a better understanding of the image classification process. To identify and categorize images as either synthetic or natural, this review offers a variety of ML and DL techniques. This survey looks at developments in digital imagery to strengthen these AI systems and increase their ability to perform effectively.

关键词： Deep learning Surveys Machine learning algorithms Reviews Network security Prediction algorithms Real-time systems Reliability image classification Synthetic data

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Real-Time Dehazing and Defogging: A Comprehensive Analysis for Single image Haze and Fog Removal

Real-Time Dehazing and Defogging: A Comprehensive Analysis f...

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Information Technology, Electronics and Intelligent Communication systems (ICITEICS), IEEE International conference on

作者： Apurva Kumari A. Pavan Kumar B.V.S Tarun Teja Department of Electronics and Communication Engineering B V Raju Institute of Technology Telangana

ISBN: (数字)9798350382693

ISBN: (纸本)9798350382709

Implementing image dehazing and defogging on a Field Programmable Gate Array (FPGA) offers efficiency. Dehazing an image becomes particularly challenging in the presence of fog or haze. However, employing a dark channel prior to dehazing allows the removal of haze particles from the image. Different modules are used in this process, such as Dynamic Atmospheric Light Estimation (DALE), Scene Recovery (SR), and Transmission Map Estimation (TME). The FPGA runs these modules in hardware and produces effective outcomes by employing image processing algorithms. In this case, using FPGA technology offers a number of benefits. The dehazing process can be accelerated by using FPGA's built-in parallel processing capabilities to execute numerous operations at once. Furthermore, FPGA implementations provide better throughput and reduced latency in comparison to conventional approaches, making them well-suited for real-time applications such as image dehazing and D

关键词： Estimation Transportation Parallel processing Logic gates Throughput Real-time systems Safety

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Knowledge Modeling of Correlation Structure in Supply Chain System and Analysis of Incident Diffusion Path 22

Knowledge Modeling of Correlation Structure in Supply Chain ...

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3rd Asia-Pacific conference on image processing, Electronics and Computers, IPEC 2022

作者： Wu, Yujia Cao, Yan Wang, Qiangfeng School of Mechatronic Engineering Xi'an Technological University Xi'an China

ISBN: (纸本)9781450395786

Aiming at the problem of analyzing the diffusion path of emergencies in the modern supply chain system, a modeling method for the related structural knowledge of the supply chain system is proposed, and the characteristics and composition of this kind of knowledge are analyzed. According to the characteristics of supply chain problems in the production-manufacturing-sales process of products, the design structure matrix is introduced into the knowledge expression process of relational structure events, and node information is used to describe the members in the supply chain system, and arcs are used to describe the relationship between members. By making full use of the advantages of DSM in expressing the interaction between the elements in the matrix and the characteristics of being conducive to the visual analysis of complex projects, a matrix expression method of relational structural knowledge is proposed. In the application stage of knowledge, cluster analysis is performed on DMS by clustering algorithm, and the diffusion scope and propagation path of emergencies in the supply chain system are analyzed by member clustering. Using the same set of data to compare with the algorithms of other scholars, the improved clustering DMS method reduces the number of iterations and improves the efficiency of system feedback. © 2022 ACM.

关键词： Supply chains

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image processing-Based Dust Detection for Solar Panels

Image Processing-Based Dust Detection for Solar Panels

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Smart systems for Electrical, Electronics, Communication and Computer Engineering (ICSSEECC), International conference on

作者： Sree Sowndarya Barani K Keerthana S Hariharasudhan S Mohana SundaramN B.E. Electrical and Electronics Engineering Kumaraguru College of Technology Tamilnadu India Electrical and Electronics Engineering Kumaraguru College of Technology Tamilnadu India

ISBN: (数字)9798350378177

ISBN: (纸本)9798350378184

This study focuses on dust detection in solar panels by utilizing image processing techniques. Dust detection helps in forecasting the maintenance needs and ensures system reliability. Inefficient maintenance practices of solar panels lead to increased downtime and decreased energy production. Implementation of dust detection holds importance in ensuring consistent power output. In the existing methodologies, Artificial Neural Network algorithms are implemented for dust detection. Artificial Neural Network algorithms show a considerably lower accuracy in image processing due to overfitting and complexity of image data. In the proposed system, a comparative analysis is performed by implementing Multi-Layer Perceptron technique and Dense Net algorithm. Using Multi-Layer Perceptron, a Sequential model is built with one flatten layer and two dense layers. It shows an accuracy of 88%. The Dense Net algorithm was also implemented on the processed image dataset and it shows an accuracy of 98%. This helps in increasing the accuracy and minimizing error. The Thonny IDE is employed for the implementation of the models, with validation conducted using hardware components such as Raspberry Pi, solar panels, and camera modules. Real-time images are captured to assess the performance. The utilization of the Convolution Neural Network algorithm, specifically Dense Net, for dust detection has led to enhanced system accuracy and efficiency.

关键词： Temperature sensors Temperature distribution Accuracy image processing Fault detection Voltage Hardware

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Research on the Detection Algorithm of Rural Road Surface Damage Based on image processing 2nd

Research on the Detection Algorithm of Rural Road Surface Da...

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2nd International conference on image, Vision and Intelligent systems, ICIVIS 2022

作者： Ye, Pei Jiang, Tao Zhan, Hui Wuchang Institute of Technology HB WuHan China Wuhan University of Technology HB WuHan China

ISBN: (纸本)9789819909223

China's new rural construction project has developed rapidly. The rural landscape has changed a lot. The scale of the rural roads is getting larger and larger. The maintenance cost of rural road diseases is also very large. People use computer technology and artificial intelligence technology to improve the detection effect of road viruses in rural areas. This paper has collected a large number of pictures of rural road surface diseases. The types of disease include: heet damage, potholes, cracks and peeling. We use the SSD algorithm to process and identify these images. with some results, We have achieved some results, but the overall identification rate is not particularly high. To further improve the identification effect, we propose an improved SSD algorithm. We found that the improved SSD algorithm also had some results in the identification of rural road surface diseases. We compare the two algorithms, and we find that improving the SSD algorithm has more advantages. The improved SSD algorithm has a higher recognition rate and a faster recognition speed. We believe that this algorithm can provide a new idea for rural pavement disease identification. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： image processing

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跟踪成像复合轴光学系统设计

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光子学报 2024年第10期53卷 192-202页

作者：许彤裕李旭阳任志广魏锦洋陆志贤边立国姚凯中中国科学院西安光学精密机械研究所空间光学技术研究室西安710119 中国科学院大学北京100049

空间望远镜的部件振动与相对运动会导致像移,为抑制振动引起的像移,可在光路中加入快速反射镜对像移进行抑制(二级稳像),然而快反镜较小的通光口径与稳像时造成的像旋限制了应用范围。为此,围绕以快反镜实现二级稳像的同轴共光路跟踪成... 详细信息

空间望远镜的部件振动与相对运动会导致像移,为抑制振动引起的像移,可在光路中加入快速反射镜对像移进行抑制(二级稳像),然而快反镜较小的通光口径与稳像时造成的像旋限制了应用范围。为此,围绕以快反镜实现二级稳像的同轴共光路跟踪成像复合轴光学系统,分析对比反射式与折反式两种同轴无焦系统各自的特点,选择可实现较大扩束比的折反式无焦系统作为复合轴光学系统的前组,以满足光路中快反镜小通光口径的要求。根据像移补偿与像旋产生的机理构建数学模型,使用MATLAB分析仿真快反镜补偿线性像移时,探测器不同位置上像旋对调制传递函数的影响,并根据后组焦距与边缘视场调制传递函数的关系图,选择合适的后组焦距将像旋对调制传递函数的影响降低在合理范围。设计出焦距为9200 mm、快反镜通光口径小于30 mm的复合轴光学系统,并将像旋造成的调制传递函数下降控制在10%内。

关键词：光学设计快速反射镜无焦系统复合轴大孔径

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NEAT-Net: An Unsupervised Cross-View Prior Inpainting Network for CBCT Metal Artifact Reduction 46

NEAT-Net: An Unsupervised Cross-View Prior Inpainting Networ...

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46th Annual International conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Yang, Yang Tong, Zhan Zhong, xinyun Shi, Yijie xi, Yan Yu, Wenxue Wu, Zhan Chen, Yang First-Imaging Tech Shanghai China Southeast University Laboratory of Image Science and Technology The School of Computer Science and Engineering Nanjing China Southeast University Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing The Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Nanjing China

ISBN: (纸本)9798350371499

Intraoperative Cone-Beam Computed Tomography (CBCT) facilitates intraoperative navigation for Minimally Invasive Spine Surgery (MISS). However, high-attenuation metal implants used in MISS often cause metal artifacts in the reconstructed CBCT images. Current algorithms do not consider the cross-view information in the projection-domain for metal artifact reduction (MAR). Inaccurate projection-domain inpainting results in CBCT MAR lead to tissue blurring and secondary artifacts, significantly compromising the accuracy of CBCT-guided MISS and increasing surgical risks. To address the above challenge, in this paper, we propose a novel unsupervised cross-view prior inpainting network for CBCT Metal Artifact Reduction named NEAT-Net. Firstly, a cross-view prior multi-scale inpainting module is constructed to learn the inter-view complementary information. Secondly, a hybrid feature attention module is proposed to adaptively fuse cross-view features. In addition, an unsupervised training approach is proposed to directly learn from metal-affected data. Extensive experiments are conducted to verify the effectiveness of our algorithm on a real clinical dataset. © 2024 IEEE.

关键词： Computerized tomography

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