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检索条件"任意字段=Conference on Image Processing - Machine Vision Applications VIII"

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An Improved Reversible Data Hiding Scheme Based on Referred Frequencies for a VQ Compressed Index 2

An Improved Reversible Data Hiding Scheme Based on Referred ...

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2nd International conference on machine vision, image processing and Imaging Technology, MVIPIT 2024

作者： Hwang, Min-Shiang Fatima, Kanza Wang, Yu-Lun Chan, Chi-Shiang Wu, Chia-Chun Department of Computer Science & Information Engineering Fintech and Blockchain Research Center Asia University Taichung Taiwan Department of Computer Science & Information Engineering Asia University Taichung Taiwan Department of Management Information Systems National Chung Hsing University Taichung Taiwan Department of M-Commerce and Multimedia Applications Asia University Taichung Taiwan Department of Computer Science and Information Engineering National Quemoy University Kinmen Taiwan

ISBN: (纸本)9798331543037

Nowadays, we usually compress images before uploading them to social media. However, images on social media can easily be copied, so embedding secret messages in compressed images has become increasingly popular. There are many compression methods, such as Huffman, VQ, ZIP, AMBTC, RAR, JPEG, etc. In this article, we propose an improved data hiding in VQ compression method to achieve better capacity and high quality. Experimental results show that our data-hiding approach is practical. ©2024 IEEE.

关键词： compressed image data hiding LSB matching reversible data hiding steganography vector quantization

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An Advanced Multi-Scale Feature Compression using Selective Learning Strategy for Video Coding for machines

An Advanced Multi-Scale Feature Compression using Selective ...

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2023 IEEE International conference on Visual Communications and image processing, VCIP 2023

作者： Yoon, Yong-Uk Han, Gyu-Woong Lee, Jooyoung Jeong, Se Yoon Kim, Jae-Gon Korea Aerospace University Department of Electronics and Information Engineering Gyeonggi-do Goyang-si Korea Republic of Electronics and Telecommunications Research Institute Daejeon Korea Republic of Korea Aerospace University School of Electronics and Information Engineering Gyeonggi-do Goyang-si Korea Republic of

ISBN: (纸本)9798350359855

machine vision-based applications have witnessed widespread adoption in diverse fields. Efficiently processing and compressing the vast amounts of video data collected by machines is crucial for these applications. To address this need, the Moving Picture Experts Group (MPEG) is developing a new' video coding standard known as Video Coding for machines (VCM), specifically optimized for video consumed by machines in vision applications. This paper proposes an advanced multi-scale feature compression (advMSFC) method with a selective learning strategy (SLS) for the Feature Compression for VCM (FCVCM). By applying the SLS, the proposed method converts multi-scale features into a single-scale feature arranged based on channel-wise importance, enabling adaptive feature channel truncation based on the QP. The truncated feature is efficiently compressed using the latest video codec, Versatile Video Coding (VVC). The proposed method outperforms the VCM feature anchor in instance segmentation, object detection, and object tracking tasks, achieving significant Bjontegaard delta-rate (BD-rate) gains. The adaptability of our method using a single trained model for various QPs shows promise for efficient feature compression in machine vision applications. © 2023 IEEE.

关键词： Computer vision

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Wild Animal Detection System on a Farm Using image processing

Wild Animal Detection System on a Farm Using Image Processin...

引用

International conference on Advances and applications of Artificial Intelligence and machine Learning, ICAAAIML 2023

作者： Barhate, Madhuri Gulhane, Nandini Gunjal, Tanvi Gudi, Swaroop Gulhane, Vedant Wagade, Prasad Gude, Ayush Vishwakarma Institute of Technology Maharashtra Pune411037 India

ISBN: (纸本)9789819795062

Human civilization is based on agriculture, and one of the biggest threats to agricultural productivity is the existence of wild animals on farmlands. Animals are taking over some farmlands, causing significant crop losses, property damage, and livestock risks. As a result, suitable solutions must be developed. A system to support farmers is necessary because the conventional method of constant guard monitoring is impractical. Modern technologies like machine learning, computer vision, and image processing are used by this wild animal detection system to effectively manage conflicts between humans and wildlife. machine learning and computer vision rely on the integration of particular algorithms to guarantee the accuracy and effectiveness of the system. With an emphasis on sustainability, the technology protects property and crops while also aiding in the conservation of wildlife. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Livestock

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A Deep Assessment of Thermal image-Base Object Detection for a Wide Range of applications 2

A Deep Assessment of Thermal Image-Base Object Detection for...

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2nd International conference on Artificial Intelligence and machine Learning applications, AIMLA 2024

作者： Pandurangan, Kamaleswari Nagappan, Krishnaraj SRM University School of Computing Dept. of Networking and Communication SRM Institute Science and Technology Kattankulathur India

ISBN: (纸本)9798350349221

Computer vision enables for the detection of things even in densely populated areas. This work investigates autonomous object detection and activity recognition in static and dynamic images, as well as their potential and restrictions. Several object identification challenges are addressed in relation to illness diagnosis, identifying wild animals, and identifying industrial activities, such as processing, segmentation, and extraction techniques for various object features. It is also critical to highlight the limitations, gaps, technical problems, applicability, and state-of-the-art of the various techniques. Shape-based detection, skin color extraction, edge identification, and an object detector are all approaches we use to identify and classify objects. This contrasts with motion detection, which is less precise when utilized at night. Thermal imaging has been used to track objects in a variety of applications, including surveillance systems and the identification of people and wildlife. We plan to evaluate new object detectors such as YOLO and RCNN. Another route for future research was suggested: investigate various object identification systems, particularly those that deal with thermal video or image detection. © 2024 IEEE.

关键词： Object detection

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Research on the Layout System of Electric Wire Tower Construction Site Based on machine vision

Research on the Layout System of Electric Wire Tower Constru...

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International conference on Smart applications and Sustainability in the Artificial Intelligence of Things, SAS-AIoT 2024

作者： Gao, Xing Huang, Wenfang Luo, Le Zhang, Bo Xu, Ke Chengdu Chengdian Electric Power Engineering Design Co. Ltd Sichuan Chengdu610000 China

ISBN: (纸本)9783031782756

Nowadays, the optimization of the construction site layout of power line tower grouping not only affects the project progress, but also relates to the construction safety. Therefore, this study proposes a machine vision (MV)-based construction site layout system for power line tower formation to improve the accuracy and efficiency of construction layout. In this study, MV technology is adopted to collect on-site video and image data through multiple high-definition cameras installed on the construction site. This data is processed in real time to identify mechanical equipment and staff in the construction area using image recognition algorithms, and to accurately track their location and movement trajectories. In addition, the system combines environmental information and construction progress data from the site to automatically plan and adjust the layout of construction resources through an integrated decision support module. This process covers a number of technical aspects such as data acquisition, image processing, pattern recognition and layout optimization. The accident rate has also decreased with the introduction of the MV system. Specific data showed that the accident rate was reduced from 2 accidents per 1000 h of work to 1.2. The MV-based construction site layout system for electric line tower grouping not only optimizes the construction process and increases efficiency and safety, but also demonstrates the great potential of MV technology for complex industrial applications. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： machine vision

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A machine Learning-based Approach for Automatic Grading and Quality Inspection of Indian Mangoes 2

A Machine Learning-based Approach for Automatic Grading and ...

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2nd IEEE-Industrial-Electronics-Society Annual On-Line conference (ONCON)

作者： Bagchi, Sourav Aditya, Janumpally Varun Kumari, Sneha Dhanraj, Malla Jenamani, Mamata Indian Inst Technol Kharagpur Dept Ind & Syst Engn Kharagpur W Bengal India

ISBN: (纸本)9798350357974

Manual visual assessment of mangoes has been problematic for the agriculture sector because of its time-consuming nature and inconsistent evaluation and sorting methods. The advent of automated flaw identification using computer vision and machine learning offers a notable shift and improvement in the visual inspection process. A common issue with mangoes is the presence of dark patches, indicative of disease or rot, which negatively affect the appearance and quality of the fruit. This paper introduces a framework using computer vision which utilizes image analysis and machine learning methods to identify these dark spots, taking into account the mangoes' texture. The proposed framework has a simplified configuration and tuning process, enhancing its ease of deployment in real-world applications. This innovation aligns with the advancements in integrating cutting-edge technologies to optimize efficiency and consistency in agricultural practices, thereby contributing to the evolution of smart agriculture and addressing the challenges and opportunities presented by the next wave of industrial revolution.

关键词： Computer vision System Dark Patches detection image processing Local Binary Pattern (LBP) machine Learning Random Forest SVM Classifier Grading

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An Improved Depth image Clustering Algorithm Based on Spatial Information 2

An Improved Depth Image Clustering Algorithm Based on Spatia...

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2nd International conference on image processing, Computer vision and machine Learning, ICICML 2023

作者： Yan, Li Su, Shan Hu, Xiao Zhou, Yuquan Chen, Changjun Wuhan University School of Geodesy and Geomatics Wuhan China Hubei Luojia Laboratory Wuhan China

ISBN: (纸本)9798350331417

Depth image spatial clustering is an important task in the fields of computer vision and machine learning, aiming to group pixels or point cloud data of depth images into clusters with similar features. This is crucial for tasks such as object recognition, scene segmentation, 3D reconstruction, and other applications. The K-means algorithm is a commonly used clustering method that divides data points into K clusters, ensuring that each data point belongs to the cluster whose center is closest to it. However, traditional K-means algorithms face several challenges in depth image spatial clustering, such as sensitivity to the choice of K and the influence of cluster center initialization on the results. In this study, we propose an improved depth image clustering method based on spatial information. The method includes depth image edge extraction, intra-edge region growing, unclassified point re-clustering, and connected region merging. Through experiments conducted on datasets from TUM, Bonn, and our collected data, our algorithm demonstrates superior performance in depth image clustering tasks, higher clustering accuracy, fewer unclassified points, and faster convergence speed. © 2023 IEEE.

关键词： Depth image clustering K-Means RGB-D datasets Spatial information

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Development and Optimization of Automotive Testing Using machine vision 1

引用

2nd International conference on Intelligent Computing Systems and applications, ICICSA 2023

作者： Jagadeesh, Kailash Mechanical Engineering Department NIT Trichy Tiruchirapalli India

ISBN: (数字)9789819754120

ISBN: (纸本)9789819754113

In this paper, a computer vision-based approach for optimizing component test benches in endurance testing of automotive components. As a use case, the paper explores testing of automotive throttle position sensor using fiducial markers and image-processing techniques. The proposed methodology is highly scalable and cost effective while maintaining the required testing accuracy. This research contributes to advancing computer vision techniques in optimizing testing methodologies in bulk manufacturing industries like the automotive industry. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Automotive industry

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Frame-Adaptive Multi-Object Tracking for Discontinuous image Sequences 2

Frame-Adaptive Multi-Object Tracking for Discontinuous Image...

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2nd International conference on Algorithm, image processing and machine vision, AIPMV 2024

作者： Li, Xiaolei Xiao, Xingjie Yang, Siyuan Sha, Moquan Sha, Zongyao Tu, Jianguang School of Remote Sensing and Information Engineering Wuhan University Wuhan China Wuhan Luojia Zhongheng Remote Sensing Data Technology Co Wuhan China China Unicom Smart City Research Institute Beijing China

ISBN: (纸本)9798350390254

In the field of multi-object tracking, this study introduces an innovative framework designed to address the challenges posed by frame loss in image sequences, particularly within the contexts of video surveillance and traffic management. Despite the significant achievements of deep learning-based tracking methods in processing high frame rate videos, frame loss remains a prevalent issue in real-world deployments due to network and hardware constraints, which can result in erroneous target associations and interrupted trajectory tracking. Our research begins by detecting frame loss through a timestamp- based analysis, this is followed by the deployment of an adaptive algorithm that fuses spatial and appearance metrics to compensate for the positional discrepancies and visual inconsistencies caused by missing frames, thus improving the precision of data association. We have curated a dataset that accurately reflects real-world environments, and conducted validation on subsampled versions of the MOT17 and MOT20 datasets, in addition to our original dataset. Empirical results indicate that our multi-object tracking approach demonstrates significant adaptability and robustness in handling discontinuous image sequences, reducing the number of ID switches by 54.1 % on our original dataset compared to state-of-the-art methods. The proposed framework has the potential to significantly enhance the performance of multi-object tracking systems in real-world scenarios where frame loss is a common occurrence, thereby improving the reliability and accuracy of video surveillance and traffic management applications. © 2024 IEEE.

关键词： Adaptive algorithms

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Breaking Through Color Casts: Enhancing image Fidelity with machine Learning-Based Correction

Breaking Through Color Casts: Enhancing Image Fidelity with ...

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2024 International conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Reddy, Choppa Jeevan Sai Akshay, Bavani Bhawane, Bange Reddy, Busireddy Parvathammagari Srikanth Satish, Addanki Sree Vidyanikethan Engineering College Tirupati India School of Engineering Mohan Babu University Tirupati Dept. of Ece India

ISBN: (纸本)9798350375237

L Color cast, an aberration common in digital images, poses challenges in various image processing applications, affecting image quality and visual perception. This research investigates diverse methodologies for color cast correction, ranging from traditional algorithms to modern machine learning-based approaches. Leveraging a comprehensive dataset of original and corrected images, the present study evaluates the efficacy of each method using quantitative metrics, including ACMO, BREN, GRAS, LAPM, LAPV, LAPD, and WAVV. Results indicate that while traditional techniques like Gray World Algorithm and White Patch Retinex Algorithm demonstrate moderate effectiveness, the implemented machine learning-based algorithm showcases superior performance across multiple color cast levels. By employing linear regression on RGB values, the method efficiently corrects color cast aberrations, yielding visually appealing and perceptually accurate results. Furthermore, the research highlights the significance of robust color constancy algorithms and their role in mitigating color cast distortions in digital images. This study contributes valuable insights into the field of color cast correction, offering practitioners in image processing and computer vision a comprehensive understanding of effective correction strategies. Future research directions may explore advanced machine learning models and integration with color constancy mechanisms to further enhance color cast correction techniques. © 2024 IEEE.

关键词： image enhancement

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