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

共 12976 条记录，以下是961-970 订阅

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A Review on the Applications of Machine Learning and Deep Learning algorithms for image Recognition

A Review on the Applications of Machine Learning and Deep Le...

引用

Sentiment Analysis and Deep Learning (ICSADL), International conference on

作者： Ketan Wanjari Prateek Verma Department of Computer Science and Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (Deemed to be University) Sawangi (Meghe) Wardha Maharashtra India Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (Deemed to be University) Sawangi (Meghe) Wardha Maharashtra India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

Modern image recognition has experienced dramatic improvements because of Machine Learning and Deep Learning algorithms together. This study investigates CNNs and SVMs for recognition enhancement while reviewing image recognition technologies extensively through literature. This paper demonstrates how the applications of healthcare and security systems and social media analysis influence society. New developments in technology have not resolved multiple unresolved obstacles such as data bias as well as computational complexity and privacy concerns together with real-time processing restrictions. The latest image processing techniques include ViTs alongside GANs and Few-Shot Learning but developers need to achieve better results in future improvements. The main goal of this research examines present-day advancements in ML and DL with a review of their capabilities as well as constraints before recommending future study paths to overcome problems encountered today. This paper evaluates both the future potential and benefits alongside drawbacks of ML and DL models applied to image recognition.

关键词： Deep learning Ethics Data privacy image recognition Machine learning algorithms Reviews Social networking (online) Vectors Security Stakeholders

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Abnormal Behavior Detection in Electronic-Exam Videos Using BeatGAN 8

Abnormal Behavior Detection in Electronic-Exam Videos Using ...

引用

8th International Iranian conference on Signal processing and Intelligent systems, ICSPIS 2022

作者： Atabay, Habibollah Agh Hassanpour, Hamid Shahrood University of Technology Image Processing & Data Mining Lab Shahrood Iran

ISBN: (纸本)9781665476232

Abnormal Behavior Detection is a core ability to recognize cheating in electronic exams (e-exams), especially in the scenes where a fraudulent candidate hides unauthorized resources from the view of the proctor. This work utilizes the BeatGAN algorithm to detect behavioral abnormalities in the skeleton-based motion information of candidates during remote e-exams. We examine the algorithm on the previously published dataset of skeleton features extracted from e-exam videos. The results show that BeatGAN outperforms previously investigated algorithms in sequence anomaly detection using the raw joint-coordinate, implicit bone-length-and-angle and explicit bone-length feature types. But, in event anomaly detection, BeatGAN does not improve the state-of-the-art results on the dataset. © 2022 IEEE.

关键词： Anomaly detection

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An Accurate and Time-Efficient Subtractor by Cross Format Coding in Stochastic Computing

An Accurate and Time-Efficient Subtractor by Cross Format Co...

引用

IEEE Asia Pacific conference on Circuits and systems (APCCAS)

作者： Zhang, Zhihuai Zhang, Weiqian Xiong, Shisheng Zhao, Yudi Fudan Univ Sch Informat Sci & Technol Micronano Syst Ctr Shanghai Peoples R China Beijing Informat Sci & Technol Univ Minist Informat Ind Key Lab Informat & Commun Syst Beijing Peoples R China

ISBN: (纸本)9781665450737

Different from binary computation, stochastic computation (SC), as a new paradigm, uses stochastic bit stream (SBS) to encode data. By simplifying computing elements, the circuit area can be greatly reduced. SBS can be generated by a stochastic number generator (SNG) with a variety of formats. In this work, we use unipolar (UP) and bipolar (BP) formats to optimize the traditional SC subtractor, which is named the UP-to-BP Subtractor (UBS). A new cross format coding (CFC) method is proposed for stochastic computing, which combines the UP and BP format, and is applied to Sobel edge detection in image processing algorithms. The fault tolerance and detection efficacy of the proposed CFC method and conventional binary computing are compared in this paper. By using the CFC method, the detected F-Score is improved by 0.15(23%). If the F-score remains unchanged, the processing speed can be about 10 times faster.

关键词： stochastic computing stochastic subtractor image processing edge detection

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Efficiency of deep neural networks for reinforced concrete shear walls failure mode detection 27

Efficiency of deep neural networks for reinforced concrete s...

引用

27th International Computer conference, Computer Society of Iran, CSICC 2022

作者： Barkhordari, Mohammad Sadegh Massone, Leonardo M. Department Of Civil And Environmental Engineering Iran University Of Chile Department Of Civil Engineering Santiago Chile

ISBN: (纸本)9781665480277

After a severe earthquake, it is critical to check the health of hospitals, buildings, fire stations, to name but a few. New technologies such as deep learning algorithms, image processing, and signal processing methods have become more appealing in such quick applications because of their higher reliabilities and efficiency than traditional visual methods. On the other hand, reinforced concrete shear walls are the key structural elements of the most buildings as lateral force resisting systems in seismic areas. Determining the failure mode of reinforced concrete shear walls is a crucial task that can help engineers and designers choose the best retrofitting options. The goal of this research is to assess the ability of deep neural networks for predicting the failure mode of the reinforced concrete shear walls. Furthermore, the SHapley Additive exPlanation approach is used to demonstrate the relevance and contribution of the factors that influence the reinforced concrete shear walls failure mode determination. © 2022 IEEE.

关键词： Deep learning Visualization Buildings Neurons Signal processing algorithms Signal processing Concrete

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An Efficient image-Based Virtual Scene Generation and Stitching Algorithm

An Efficient Image-Based Virtual Scene Generation and Stitch...

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Multi-Agent systems for Collaborative Intelligence (ICMSCI), International conference on

作者： Luo Wei Chongqing Three Gorges Vocational College Chongqing Wanzhou China

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

With the development of computer graphics and image processing, the virtual scene generation and splicing technology has been widely used in various fields of computer-aided image analysis. In this context, this study proposes a new and efficient image stitching algorithm to solve the image stitching artifacts caused by perspective changes and exposure differences in multi-camera systems, and expands it to the field of virtual images. First, as a pre-processing part, the model analyzes the image through nonlinear filtering and mesh deformation optimization to ensure image quality. Subsequently, the model presents a new image stitching algorithm based on the image matching method of minimizing projection deviation and thin plate spline theory. This algorithm further proposes an improved EIEOWF algorithm based on circular weighted fusion to achieve the task of smooth transition stitching of images. Finally, the stitching technology was extended to virtual scene generation, and OpenGL technology was used to achieve real-time 3D texture mapping. The test part is verified through experiments on the UDIS-D data set. The results show that the average splicing accuracy of the proposed algorithm in 100 sets of tests is 95%, with a standard deviation of 2%, which is significantly better than the existing comparison algorithm.

关键词： Three-dimensional displays Accuracy Splicing Heuristic algorithms Reliability theory Real-time systems Splines (mathematics) image stitching Standards Optimization

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Application of Deep Learning Based on Edge Computing of Wireless Sensor Networks

Application of Deep Learning Based on Edge Computing of Wire...

引用

Advanced algorithms and Control Engineering (ICAACE), International conference on

作者： Huaqiao Duan Qijing Wang Nanjing University Suzhou China

ISBN: (数字)9798331535087

ISBN: (纸本)9798331535094

With the rapid advancement of the Internet of Things (IoT) technology, Wireless Sensor Networks (WSN) have become a fundamental component of contemporary society. Nevertheless, the surge in complex application scenarios coupled with an exponential increase in data volumes has rendered the traditional cloud computing processing model insufficient. The edge computing-based deep learning framework can effectively handle these large-scale image datasets without compromising privacy and security, facilitating intelligent analysis and decision-making. This study,through experimental design, we developed a deep learning model grounded in edge computing, tailored for addressing specific challenges in image data processing within wireless sensor networks, such as image recognition, classification, and analysis. For concrete metrics, consider a scenario where edge computing-based deep learning models were deployed for real-time traffic flow analysis. These models, by processing data locally, can reduce data transmission times by up to 70%, enhancing response times from seconds to milliseconds. Furthermore, in privacy-sensitive applications such as wildlife monitoring, edge computing ensures data is processed on-site, reducing the risk of sensitive data exposure by over 80%. Lastly, in terms of energy consumption, edge devices operating on optimized deep learning algorithms can achieve a reduction of up to 40% compared to traditional cloud-based processing methods, underlining the significant efficiency and performance gains achievable with this technology integration.

关键词： Deep learning Wireless sensor networks Cloud computing Computational modeling image edge detection Data processing Data models Real-time systems Time factors Edge computing

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Overview of image Detection algorithms: Performance Analysis and Technology Trends

Overview of Image Detection Algorithms: Performance Analysis...

引用

International conference on Informatics and Computing (ICIC)

作者： Nurul Hidayat R. Rizal Isnanto Department of Informatics Jenderal Soedirman University Purwokerto Indonesia Department of Computer Engineering Diponegoro University Semarang Indonesia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

image detection plays a vital role in digital image processing and artificial intelligence, with applications ranging from security surveillance to autonomous vehicles and medical image analysis. This study employs a qualitative approach to review the performance of image detection algorithms, focusing on deep learning methods such as YOLO, SSD, and CNN. Through computational experiments using quantitative data, this research provides performance comparisons based on accuracy, precision, recall, and computational efficiency. The results highlight YOLO’s superior performance in terms of both accuracy ($\mathbf{9 2} .5 \%$) and inference speed (75.2 fps), making it suitable for real-time applications. This study contributes by addressing the gap in balancing computational efficiency and adaptability for real-world applications.

关键词： YOLO Deep learning Machine learning algorithms Accuracy Computational modeling Real-time systems Inference algorithms Computational efficiency Detection algorithms Edge computing

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Contrast Enhancement and ROI detection in satellite images Application to the environment and sustainable development

Contrast Enhancement and ROI detection in satellite images A...

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International conference on image and Signal processing and their Applications (ISPA)

作者： Aicha Mehidi Malika Mimi Abdallah Mehidi Soraya Zehani Electrical Engineering Department Laboratory of Signals and Systems Faculty of Sciences and Technology University of Abdelhamid Ibn Badis Mostaganem Algeria Laboratory of Signals and Systems Faculty of Sciences and Technology University of Abdelhamid Ibn Badis Mostaganem Algeria Electrical Engineering Department Laboratory of Vision and Communication Systems Faculty of Sciences and Technology University of Biskra

ISBN: (数字)9798350309249

ISBN: (纸本)9798350309256

During the last few years, remote sensing is considerably used for Earth observation for the environment and sustainable development. The temporal classes of satellite images provide better information for monitoring the earth's surface at different scales; therefore these images are becoming a very relevant opportunity of investigating. The numerical analyses of image processing are based on the artificial intelligence (AI) tool rather than the traditional methods. The environmental indicators from these images offer very important statistics, the carried out work of dynamic phenomena, the observation event and interpretation of evolving circumstances like: atmospheric conditions, crises and natural disasters taking the opportunity to be studied, several problems come out of deforestation and monitoring of water resources (water bodies). The purpose of this study is focused on drawing out details from the database used and then to give an offer for a texture analysis strategy. Firstly, using specific development and then developing the existing software in the second hand. This work is to establish an algorithm for the detection of region of interest (water body) which is based on the theory of fuzzy logic and the hypothesis of Fuzzy C-Means.

关键词： Earth image segmentation Software algorithms Signal processing algorithms Satellite images Sustainable development Artificial intelligence

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A Comparative Review on Design and Development of Automation System for Grading of Onion

A Comparative Review on Design and Development of Automation...

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AIML-Applications for Engineering & Technology (ICAET), International conference on

作者： Rahul Suryawanshi Jayesh Jagtap Dnyaneshwari Shegokar Siddhesh Jaju Chandan M. N Avinash M. Badadhe Department of Automation and Robotics JSPM's Rajarshi Shahu College of Engineering Pune India

ISBN: (数字)9798350355611

ISBN: (纸本)9798350355628

Grading of onion is important for the purpose of quality as well as market value, and it has, in the past, used traditional methods. Onion grading has thus been automated, which has been a focus of numerous researchers because of the rising need for quality produce. Thus, the findings highlighted in this work show the progress and current approaches and methodologies designed for the design and implementation of automation systems for grading onions. This work outlines various methods such as image processing, gas sensor technology, Near Infra-Red (NIR) spectroscopy, X-ray imaging, Laser Doppler Vibrometry (LDV), and some artificial intelligence approaches that have been used in improving onion sorting systems. In addition, it reveals the drawbacks of these technologies, such as the realization of high image processing, the availability of small data sets, and the stability of the sortation systems. Based on the findings of this review, some recommendations for future research are suggested, such as the integration of multi-sensor systems and the enhancement of the efficiency of the automated onion grading systems using different types of algorithms.

关键词： Automation Reviews image processing X-ray lasers Laser stability Doppler effect Gas lasers Artificial intelligence X-ray imaging Sorting

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Traffic Sign and Obstacle Detection for Autonomous Vehicle Navigation using Edge Computing

Traffic Sign and Obstacle Detection for Autonomous Vehicle N...

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International conference on Wireless Communications, Signal processing and Networking (WiSPNET)

作者： M. Vijayalakshmi M. Bharathwaj D. Anjelin Deva Dharshini Department of IST CEG Anna University Chennai India

ISBN: (数字)9798331524227

ISBN: (纸本)9798331524234

The advancement of autonomous vehicle technology necessitates robust systems for real-time traffic sign recognition and obstacle detection to ensure safe navigation. This paper proposes a comprehensive autonomous vehicle (AV) simulation framework utilizing the Carla simulator, advanced image processing, and state-of-the-art machine learning models to enhance autonomous navigation capabilities. The system initiates by loading a specified virtual map within the Carla simulator and deploying an ego vehicle equipped with a camera sensor to capture environmental images. These images undergo preprocessing techniques, to ensure suitability for analysis. Traffic sign recognition system uses a pre-trained YOLOv7 model, enabling the AV to identify and respond to various traffic signals amidst occlusion, adverse weather, and variations in placement and size. Concurrently, a pre-trained Faster R-CNN model detects obstacles such as vehicles and pedestrians along the vehicle’s path. A dedicated distance estimation module further analyzes these preprocessed images to calculate the relative distances of detected obstacles using depth estimation techniques, providing crucial spatial information. The decision-making algorithm resolves conflicts between recommended actions from traffic sign recognition and obstacle detection modules, prioritizing safety-critical commands to ensure operational safety and efficiency. The models are stored on the network edge, facilitating efficient deployment and real-time processing. This framework demonstrates an effective integration of simulation, image analysis, and decision-making, advancing the reliability and performance of autonomous navigation systems.

关键词： Navigation Heuristic algorithms Decision making Signal processing algorithms Cameras Real-time systems Road safety Classification algorithms Autonomous vehicles Edge computing

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