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检索条件"任意字段=Conference on Image Processing - Algorithms and Systems IV"

共 13657 条记录，以下是481-490 订阅

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Research and application of active monitoring technology for power transmission and transformation construction safety in complex environments 9

Research and application of active monitoring technology for...

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9th International conference on Energy System, Electricity, and Power, ESEP 2024

作者： Lin, Zhikan Li, Wenxin Jin, Di Liu, Yuzheng Zhou, Guosheng Chen, Wei Lin, Yujie Lin, Cheng Wu, Jinshan Lin, Junjie Qiu, Peng State Grid Fujian Electric Power Co. Ltd. Construction Branch Fujian Building A No.9 Qinyuan Branch Road Jin'an District Fujian Province Fuzhou City China

ISBN: (纸本)9781510691728

Monitoring systems at power transmission and transformation construction sites often face challenges from complex environments such as haze, low lighting, and strong winds, leading to degraded video quality and stability. To enhance the reliability of these monitoring systems, this study employs three algorithms: image dehazing, low-light enhancement, and wind-induced blur removal. These algorithms are integrated through adaptive weight adjustment and parallel processing. This integration strategy enables the monitoring system to select the optimal algorithm in real-time based on environmental conditions and maintain stable image quality under multiple adverse factors. Experimental results show that the image dehazing algorithm improves PSNR to 28.37 dB in hazy environments, the low-light enhancement algorithm achieves a PSNR of 25.34 dB at night, and the wind-induced blur removal algorithm maintains an SSIM of 0.903 at a wind speed of 4 m/s. Transmission tests in weak signal areas indicate that a compression ratio of 30: 1 to 40: 1 can effectively balance video quality and bandwidth requirements. The monitoring system that integrates multiple algorithms demonstrates good adaptability and robustness in complex environments, providing reliable technical support for safety monitoring at power transmission and transformation construction sites. © 2025 SPIE.

关键词： Haze processing image dehazing Low-light enhancement Power transmission and transformation engineering Real-time monitoring system Safety monitoring

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image processing-Based Fire Detection and Protection System Using OPENCV

Image Processing-Based Fire Detection and Protection System ...

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2023 International conference on Sustainable Emerging Innovations in Engineering and Technology, ICSEIET 2023

作者： Verma, Vivek Kr. Jain, Khushi Kumari, Jaya Kumar, Rohit Kumar, Umang ABES Engineering College Dept. of Electrical and Electronics Engineering Ghaziabad India

ISBN: (纸本)9798350329186

The objective of this project is to develop an advanced fire detection and protection system using image processing technology to provide early warning of fires. Traditional fire detection systems rely on smoke sensors that have limitations and are only capable of sensing fires in restricted areas. Our fire detection method based on a color model offers numerous advantages over traditional smoke detection methods. Its simplicity, feasibility, and comprehensibility make it a reliable and effective approach for detecting fires. To overcome these limitations, a Computer Vision Based Early Fire Detection system has been proposed which uses machine learning algorithms for enhanced accuracy. The system has been implemented using PyCharm IDE and either a primary or secondary camera. The input source is a webcam, which captures the video feed from the surrounding area for analysis. The code has been written in Python language using the OpenCV2 library for image processing. Then hardware is implemented which generates alerts to help prevent damage or injury. © 2023 IEEE.

关键词： Fires

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Enhanced Camouflage Detection Using Advanced image processing Techniques for Real-Time Object Recognition and Pattern Differentiation 5

Enhanced Camouflage Detection Using Advanced Image Processin...

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5th International conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Manikandan, G. Deepa, R. Jayalakshmi, V. Thilakavathy, P. Surendran, R. St. Joseph's College of Engineering Anna University Department of Artificial Intelligence and Machine Learning Chennai India School of Computing SRM Institute of Science and Technology Department of Computing Technologies Chennai India SRM Institute of Science and Technology Department of Computer Science and Applications Chennai India Vels Institute of Science and Engineering Vels Institute of Science Technology and Advanced Studies Computer Science and Engineering Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530013

The identification of objects that blend in with their surroundings has long been a concern in fields including defence, wildlife monitoring, and surveillance due to the difficulty of detecting such objects. This finding is significant because it could greatly enhance our capacity to detect camouflaged objects, transforming the field of real-time object detection. Existing image processing methods often fail miserably when adequately capturing this aspect. This dissertation's primary focus is identifying devices with complicated patterns, legacy interference, and inadequate contrast;nonetheless, in disguised scenarios, all of these features cause identification to be unsuccessful. This research often suggests a new approach called the Linear Edge Detecting Scheme for Camouflaged Object Detection (LEDS-COD) in these difficult cases. The LEDS-COD system can enhance item visibility in low-comparison settings by highlighting critical edges and descriptions. Pattern differentiation methodologies and modern facility-side recognition techniques are combined to accomplish this goal. The approach successfully differentiated items because it zeroed in on texture and comparative details that would otherwise go unnoticed by humans or advanced algorithms. LEDS-COD is useful in many areas, including the Navy, autonomous surveillance systems, and animal protection, where the real-time identification of hidden devices is essential. Results from a simulation study using various datasets, including concealed objects, show that LEDS-COD surpasses conventional detection methods in recognition accuracy, processing speed, and perseverance. The recommended technique exceeded the requirements for obtaining improved object detection estimates for challenging conditions like those with complex backdrops and changing illumination. According to this study, LEDS-COD can be devastating in improving real-time camouflage detection. © 2024 IEEE.

关键词： Accuracy image processing Surveillance image edge detection Wildlife Lighting Object detection Real-time systems Object recognition Protection

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image Enhancement of Underwater images Using Deep Learning Techniques

Image Enhancement of Underwater Images Using Deep Learning T...

引用

International conference on Data Analytics and Insights, ICDAI 2023

作者： Dasari, Siva Krishna Sravani, Laveti Kumar, Molleti Upendra Rama Venkata Sai, N. Department of Computer Science and Engineering GMR Institute of Technology Andhra Pradesh Vizianagaram Rajam India

ISBN: (纸本)9789819938773

In the present era enhancing the under image quality play a major role for research purpose and ocean exploration. The market for high-quality underwater images is expanding exponentially. Despite this, there are a number of issues including blurriness, dim lighting, poor contrast, and colour fidelity. Light attenuation, absorption, and scattering lower the quality. As a result, it is essential to enhance underwater images, and different image processing techniques are used for enhancement. The deep learning methodology used in this work to improves image quality and produce the best outcomes when compared to conventional image enhancing techniques. The architecture used is GAN with the combination of image processing algorithms such as histogram equalization as a preprocessing step for implementing deep learning models. The algorithms include histogram equalization and Dark Channel Prior. These methods normalize the contrast of images and removes the hazing or dark parts of the images. The dataset used in this work is EUVP (Paired) which contains about 2186 real-world underwater images. Three parameters, UICM, MSE, and PSNR are used to assess quality of enhanced images. The optimum technique for improving the images is determined by comparing the performance of the models after the dataset has been trained through them. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： image enhancement

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On the Computational Complexity of 2D Filtering by Winograd Method 11

On the Computational Complexity of 2D Filtering by Winograd ...

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11th Mediterranean conference on Embedded Computing (MECO) / 3rd Summer School on Cyber-Physical + systems and Internet of Things (CPS and IoT)

作者： Lyakhov, Pavel A. Abdulsalyamova, Albina S. Semyonova, Nataliya F. Nagornov, Nikolay N. Voznesensky, Alexander S. Kaplun, Dmitrii, I North Caucasus Fed Univ North Caucasus Ctr Math Res Stavropol Russia North Caucasus Fed Univ Dept Math Modeling Stavropol Russia St Petersburg Electrotech Univ LETI Dept Automat & Control Proc St Petersburg Russia

ISBN: (纸本)9781665468282

Increasing the speed of digital image processing is an important problem in various fields. This paper proposes a way to increase the speed of 2D image filtering using the Winograd method. New algorithms for image processing based on the Winograd method have been proposed. The theoretical analysis results showed that an increase in the number of pixels in the resulting image fragment leads to a decrease in multiplications and the computational complexity of digital filtering. Winograd methods reduced multiplications to 4 times by increasing additions to 2.88 times using filter 3x3. The results obtained can be used in various image processing fields that require real-time processing using modern microelectronic devices.

关键词： Winograd method 2D filtering digital image processing filtering algorithms computational complexity

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image processing in Retail Marketing: Innovations for Customer Engagement, Sales, and Operational Efficiency 12th

Image Processing in Retail Marketing: Innovations for Cust...

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12th International conference on Recent Trends in Computing, ICRTC 2024

作者： Rastogi, Isha Sinha, Utkrishta Gaur, Anubha Department of Information Technology KIET Group of Institutions Ghaziabad India

ISBN: (纸本)9789819788354

The retail industry, marked by fierce competition and evolving consumer preferences, demands innovative approaches to boost customer satisfaction, drive sales, and streamline operations. This study presents a comprehensive strategy leveraging cutting-edge image processing technologies such as facial recognition, object detection, and emotional analysis. The methodologies proposed encompass emotional profiling through facial recognition, personalized offline store experiences enabled by facial recognition technology, and automated inventory management via object detection algorithms. Real-world applications and case studies validate the efficacy of these techniques, showcasing their transformative potential in redefining the retail landscape. By integrating advanced image processing solutions, retailers can not only meet the dynamic demands of modern consumers but also gain insights to optimize business processes and deliver unparalleled shopping experiences. This research underscores the pivotal role of technology in shaping the future of retail, offering practical insights for industry stakeholders to navigate and thrive in an increasingly competitive market landscape. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Inventory control

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Identification of Counterfeit Indian Currency Note using image processing and Machine Learning Classifiers 3

Identification of Counterfeit Indian Currency Note using Ima...

引用

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

作者： Sharan, Vivek Kaur, Amandeep Singh, Parvinder Central University of Punjab Dept. of Computer Science and Technology Punjab Bathinda India

ISBN: (纸本)9781665462167

Technology is continuously changing our life. Day by day, it makes our life easy, but some challenges and issues exist. Counterfeit currency is one of them. It happens because of the production and circulation of currency without the permission of an authorized system. Some people use scanning and printing technology to produce such notes and circulate them around us, which is a kind of *** leads to personal loss and degrades the Country's economy. Such notes are very similar to the original, which becomes a problem for ordinary people to identify the authenticity of the currency, especially for visually impaired people. However, most researchers proposed different methods to differentiate real notes from fake ones based on the currency's shape, colors, and size. The notes are easily detected when the condition of the note is good but difficult when it deteriorates over time. Extracting features from such notes is another challenging task. Some systems are available to only specific sectors and not easily available to common people. Therefore, a system that can distinguish between real and fake notes is required. This article classifies Indian Currency notes as real or fake with four supervised Machine Learning algorithms i.e., Support Vector Classifier, K-Nearest Neighbor, Decision Tree, and Logistic Regression followed by image processing techniques. This study has implemented these algorithms on the dataset of 1372 currency image samples, out of which 762 are real, and the remaining are fake, available on the UCI machine learning repository. Further, the performance of all the algorithms is measured in terms of accuracy, recall, Precision, and F1-score and after analyzing, it is observed that the K-Nearest Neighbor leverage outstanding results compared to other algorithms. © 2023 IEEE.

关键词： Decision trees

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Baker’s Map Based Chaotic image Encryption in Military Surveillance systems 1

引用

International conference on Internet of Everything and Quantum Information processing, IEQIP 2023

作者： Rajendiran, Nithya Paranthaman Department of Networking and Communications SRM Institute of Science and Technology Kattankulathur India

ISBN: (数字)9783031619298

ISBN: (纸本)9783031619281

One of the primary problems in military surveillance systems is the safety of the visual data gathered by the sensor nodes installed in the different areas of the Visual sensor network and the remotely operated devices. There are several cryptographic algorithms available to secure the visual information. Several chaotic schemes worked well because they were more resistant to threats and had greater entropy levels. But, it takes a lot of time to finish the encryption step. Traditional approaches had the drawback that increasing encryption strength slowed calculation speed. This paper presents a scheme called "baker’s map chaotic method based image encryption" to accelerate the encryption process in a network of visual sensors without weakening the encryption method. The findings indicate that the system’s functionality is improved when the computation time is reduced to sixty seconds and the PSNR is increased to 15 dB. The performance of the chaotic method is compared by using key space analysis, key sensitivity analysis, correlation coefficient analysis, noise analysis, Entropy analysis, and Speed analysis. The IBM SPSS statistical tool was also used to do statistical analysis. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Sensitivity analysis

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Study on Hyperspectral Remote Sensing images of GF-5 De-Blurring Based on Sparse Representation 2023

Study on Hyperspectral Remote Sensing Images of GF-5 De-Blur...

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5th International Symposium on Signal processing systems, SSPS 2023

作者： Wang, Hong School of Electronic Information Sichuan Modern Vocational College Chengdu China

ISBN: (纸本)9798400700040

Deblurring high resolution remote sensing image is a very important problem in remote sensing research. In this paper, we propose a new deblurring algorithm for high-resolution remote sensing images (HSI) based on sparse representation. The purpose of this study is to apply compressed sensing measurement and reconstruction technology to realize the processing of remote sensing image, and discuss the Under what circumstances can CS achieve better results in remote sensing image processing. The algorithm uses fast gradient projection algorithm to achieve deblurring and retain the important ground information of the original image. Experiments on remote sensing images obtained by GF-5 show that the algorithm can filter the blurring of remote sensing images well and improve the peak-to-noise ratio (PSNR) of images. The algorithm has better performance than other sparse representation algorithms. This paper explores the application of dictionary learning theory and sparse decomposition in remote sensing image processing. By further extending the algorithm proposed in this paper and adding new constraints, remote sensing image restoration, target recognition, deblurring, fusion and so on can be carried out. © 2023 ACM.

关键词： image reconstruction

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Advancements in image Deblurring and Performance Metrics Using Deep Learning Technique 12th

Advancements in Image Deblurring and Performance Metrics Usi...

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12th International conference on Recent Trends in Computing, ICRTC 2024

作者： Dhavalagimath, Shankramma S. Rajesh, T.M. Singh, Rakesh Kumar Department of Mathematics Dayananda Sagar University Bangalore562112 India Department of Computer Science and Engineering Dayananda Sagar University Bangalore562112 India

ISBN: (纸本)9789819788354

In computer vision and image processing, image deblurring is a crucial phase that attempts to restore the sharpness of the image and clarity of images that have been damaged due to motion blur, defocus, or other factors. The multiple image deblurring methods are thoroughly investigated in this work, along with the performance evaluation metrics that were employed to evaluate each method's efficiency. We started by examining the most recent deblurring techniques, which include both deep learning-based and traditional algorithms. Every technique's advantages, disadvantages, and suitability are examined in different scenarios. Furthermore, we investigate the finer details of performance metrics that are frequently used to rate the quality of deblurred images. These metrics include subjective assessments from user research &objective measurements like PSNR and SSIM. This study aims to elucidate the benefits and drawbacks of current deblurring methods in diverse contexts through extensive experimentation. Moreover, we examine the applicability and consistency of performance evaluation metrics for evaluating the accuracy and perceived quality of deblurred images. By supporting researchers and professionals in identifying suitable techniques and assessment standards for their particular applications, the knowledge gained from this study improves the field of image deblurring. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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