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

共 15687 条记录，以下是121-130 订阅

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Fire Incendie Detection A compartive study of Convolutional Neural Networks (CNN) and Support Vector Machines (SVM). 8

Fire Incendie Detection A compartive study of Convolutional ...

引用

8th IEEE International conference on image and Signal processing and their Applications, ISPA 2024

作者： Guerbai, Yasmine Saibi, Asma M'Hammed Bougara University Boumerdès Department of Electrical Systems Engineering Boumerdès Algeria

ISBN: (纸本)9798350309249

In recent years, the application of artificial intelligence (AI) techniques for fire detection has gained significant attention due to its potential for enhancing early fire detection systems. This study aims to compare the performance of deep learning convolutional neural networks (CNN) and support vector machine (SVM) machine learning algorithms in the context of fire detection. We present a comprehensive analysis and evaluation of the two approaches, highlighting their strengths and weaknesses, and discussing their potential for real-world fire detection applications. © 2024 IEEE.

关键词： Support vector machines

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Method for background replacement of certification photos

Method for background replacement of certification photos

引用

2024 International conference on Optics, Electronics, and Communication Engineering, OECE 2024

作者： Chen, Xu Zhang, Jihong Du, Yufei Sha, Zihuan Qiu, Rongfei Xu, Zezhong School of Computer Science and Information Engineering Changzhou Institute of Technology Jiangsu Changzhou213032 China

ISBN: (纸本)9781510685437

In order to overcome defects resulted from replacing the photo background color, an improved method is proposed for background replacement. The α-values in the alpha matte are transformed to enhance the details in the transition area. The new photo is composited by comprehensively considering the foreground portrait, new background, old background and the alpha matte. The experimental results show that the foreground portrait appears clearer and more complete, and the transition area between the foreground and background is more harmonious and more natural. © 2024 SPIE.

关键词： Color Opacity Computer science Engineering image processing image processing algorithms and systems image processing software image segmentation Information science

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Automatic Licence Plate Detection and Recognition System using image processing Techniques 1

Automatic Licence Plate Detection and Recognition System usi...

引用

1st International conference on Cognitive, Green and Ubiquitous Computing, IC-CGU 2024

作者： Ganesh, Peddi Shyam Srilekha, B. Sravanthi, Vukanti Siddhartha Institute of Technology & Sciences Department of ECE Telangana India

ISBN: (纸本)9798350361780

The recent exponential surge in the number of vehicles on our roadways has made congestion and violations important problems. By automating traffic management using an ALPR system, we can improve access control systems, streamline traffic flow, and save time. Here, we look at how well algorithms for edge processing and morphological processing perform. The parameters of the neural network, including the regularisation parameter, the count of hidden layer units, and the count of iterations, are optimised with great care. With a focus on real-time implementation and control via a graphical user interface, this case presents a parking security system suitable for usage in office buildings, institutions, malls, etc. Operating on Raspberry Pi 2B and Matlab, the system accomplishes a 97% efficiency rate through the use of image processing techniques and machine learning algorithms. © 2024 IEEE.

关键词： Traffic congestion

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Research on the Design of Financial Institution Customer Identity Verification systems Based on Artificial Intelligence and Machine Recognition 2

Research on the Design of Financial Institution Customer Ide...

引用

2nd IEEE International conference on image processing and Computer Applications, ICIPCA 2024

作者： Shi, Jiayi Co. Ltd. Shanghai200127 China

ISBN: (纸本)9798350360240

Over the recent years, fraudulent activities in financial institutions have increased in China;according to criminals, there are several approaches to deceiving their users. This article focuses on applying Artificial Intelligence (AI) and Machine Learning in customer identity verification, a move driven towards recognizing and addressing the increasing bank fraud cases. The research introduces novel machine learning systems and models designed to determine fraudulent banking transactions. Various methods for enhancing detection accuracy are described. An artificial neural network-based model is proposed to enhance the system's efficiency and accuracy in identifying unauthorized transactions. The outcomes of variedly designed algorithms are, in this case, presented, typifying the impact of the logistic regression algorithm in enhancing the performance of the system, while stacked generalization demonstrates a higher AUC at 0.954. Recognizing bank fraud using artificial intelligence algorithms remains a significant concern in today's digital society. © 2024 IEEE.

关键词： Sales

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Copy-Move image Forgery Detection using Deep Learning Approaches 1

Copy-Move Image Forgery Detection using Deep Learning Approa...

引用

1st International conference on Software, systems and Information Technology, SSITCON 2024

作者： Ibrahim, Zainab Shawkat Hasan, Taha Mohammed University of Diyala College of Science Department of Computer Science Iraq

ISBN: (纸本)9798350352931

The advancement of digital image processing software has reached a stage where it is effortless to manufacture forgeries by using numerous manipulating approaches on authentic photos. Occupations such as law, healthcare, and education are susceptible to manipulated imagery. Therefore, it is crucial to ascertain the authenticity of an image. Single type of image manipulation is identified as 'copy move,' when significant item or objects are concealed by duplicating and inserting them into the original image. It is crucial to determine whether an image is genuine. The current techniques employed to identify image forgeries relied on conventional feature extraction algorithms, such as key point and block-based algorithms. These conventional approaches yield a subpar outcome. Deep learning approaches have established superior performance in image processing applications. This paper presents a highly effective method for identifying instances of copy-move forgery in images using a hybrid approach that combines deep learning (DL) such as (VGG19 and ResNet50) for feature extraction and Machine Learning (ML) algorithms including (Random Forests (RF) and Support Vector Machine (SVM)) to classify digital images. The proposed models (VGG19-RF), (VGG19-SVM), (ResNet50-RF), and (ResNet50-SVM) have been compared using two standard datasets. The results indicate that ((VGG19-RF) achieves higher recall, accuracy, precision and F1-score of 97% on MICC-F220 dataset, On the other hand (VGG19-RF) and (ResNet50-RF) achieve higher recall, precision, accuracy and F1-score of 97% on the MICC-F2000 dataset. © 2024 IEEE.

关键词： Support vector machines

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High-Dimensional Data processing Using Quantum-Inspired Evolutionary algorithms for Homeland Security Imaging systems

High-Dimensional Data Processing Using Quantum-Inspired Evol...

引用

2024 International conference on Communication, Computer Sciences and Engineering, IC3SE 2024

作者： Alekhya, V. Reddy, N. V. Uma Singh, Jay Boddu, Bhasker Sobti, Rajeev Hameed, Ali Abduhussien Institute Of Aeronautical Engineering Dundigal Hyderabad India New Horizon College Of Engineering Department Of Artificial Intelligence And Machine Learning Bangalore India Gl Bajaj Institute Of Technology & Management U.P. Greater Noida India Mlr Institute Of Technology Department Of Information Technology Telangana Hyderabad India Lovely Professional University Phagwara India College Of Medical Technology The Islamic University Radiology Techniques Department Najaf Iraq

ISBN: (纸本)9798350366846

This research study explores the emerging area of quantum-inspired evolutionary algorithms (QIEAs) applied to high-dimensional data processing, with a focus on homeland security imaging systems. This work attempts to close the paradigm gap in image processing methodologies caused by the growing complexity of security threats. By combining the adaptive processes of evolutionary algorithms with the probabilistic reasoning inherent in quantum computing, the proposed technique creates a potent tool for managing the inherent difficulties of high-dimensional data. The main contribution of this study is the creation of a brand-new QIEA framework that is specifically designed for image analysis in security contexts and exhibits higher accuracy and efficiency than traditional techniques. The quantum bit representation and quantum gate operations, which have been cleverly tailored to the evolutionary algorithm structure, provide the methodological basis, and improve the search capacity in multidimensional data fields. An unparalleled degree of clarity and detail in security photography is made possible by this fusion, which is essential for threat identification and prevention. Experiments conducted on a variety of difficult datasets show that the suggested method is reliable for identifying important characteristics in complicated photos, which is a crucial component of homeland security applications. Beyond only addressing short-term security issues, this study establishes a standard for next investigations into quantum-inspired computing for image processing. © 2024 IEEE.

关键词： Imaging systems

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Segmentation-based low-resolution spatial tracker for video 34

Segmentation-based low-resolution spatial tracker for video

引用

conference on Automatic Target Recognition XXXIV

作者： Benyamin, Minas Goldman, Geoffrey H. DEVCOM Army Res Lab 2800 Powder Mill Rd Adelphi MD 20783 USA

ISBN: (纸本)9781510673977;9781510673960

Low-resolution image object recognition and tracking is often required for battlefield reconnaissance. For high-cost military systems, standard signal processing techniques can be used by tracking systems, however, low-cost systems require simpler approaches. We developed a fast detector-agnostic tracker for improving situational awareness using electro-optical video data. Our approach uses low computational techniques such as YOLO, match filters, and shape transforms to segment objects of interest in an image. From two or more successive detections, we initialize an alphabeta filter that predicts the location of the target of interest in the image. Next, we segment subsequent frames to a search area around the predicted region. This increases the sensitivity of the detector by improving the average signal-to-noise ratio and it also decreases the false alarm rate. The reduction in the size of the processing area can improve the detection speed per frame by an order of magnitude relative to a full-sized frame. By using algorithms with input from variablesize object a such as YOLO, this algorithm can be adapted to track virtually any object captured in a video.

关键词： Tracking Detection YOLO Hough Transform image processing Electro-optical Alpha-Beta Filter Low Resolution Classification

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A study on advancing Edge Detection and Geometric Analysis through image processing 24

A study on advancing Edge Detection and Geometric Analysis t...

引用

8th International conference on Graphics and Signal processing

作者： Hamza, Syed Amir Jesser, Alexander Heilbronn Univ Heilbronn Germany Heilbronn Univ Professorship Embedded Syst & Commun Engn Heilbronn Germany

ISBN: (纸本)9798400717024

In the realm of Printed Circuit Board (PCB) manufacturing, the alignment process is pivotal for ensuring the functional integrity of the final product. Traditional image measurement techniques, while foundational, often fall short of achieving the high degree of accuracy and precision necessary for today's complex PCB designs. This research presents a novel approach that significantly enhances measurement accuracy through the application of optimized image processing techniques. By leveraging advanced algorithms within the OpenCV library, we introduce a methodology that accurately transforms pixel coordinates into real-world measurements, crucial for precise PCB alignment. Our technique employs edge detection algorithms such as Canny, Sobel, and Prewit filters, combined with a machine learning model that adapts to variations in real-time imaging conditions. The study delineates the development of a user-friendly graphical interface that streamlines the measurement process, making it accessible for practical industrial application. Results from experimental validations indicate a substantial improvement in measurement precision, with a demonstrable reduction in alignment errors compared to conventional methods. This leap forward not only promises to elevate the standards of PCB manufacturing but also opens avenues for similar advancements in other domains where image measurement is essential. The implications of this work are far-reaching, with the potential to significantly boost the efficiency and reliability of electronic manufacturing processes globally.

关键词： image measurement techniques microns edge detection algorithms real-world coordinate transformation machine learning in imaging graphical user interface (GUI) Printed Circuit Board (PCB)

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Bayesian Optimization for Auto-tuning Convolution Neural Network on GPU 23rd

Bayesian Optimization for Auto-tuning Convolution Neural Net...

引用

23rd International conference on algorithms and Architectures for Parallel processing (ICA3PP)

作者： Zhu, Huming Liu, Chendi Zhang, Lingyun Dong, Ximiao Xidian Univ Key Lab Intelligent Percept & Image Understanding Minist Educ Xian 710071 Peoples R China

ISBN: (纸本)9789819708109;9789819708116

GPU as a hardware processor plays an important role in the training of deep neural networks. However, when using GPUs for computation on convolutional neural network models, different combinations of GPU kernel configuration parameters have different performance. Therefore, this paper proposes BAGF, a bayesian auto-tuning framework for GPU kernels, which parameterizes the factors affecting the performance of GPU programs and uses bayesian optimization methods to search for the best parameters in the search space consisting of the parameters. Compared with other optimization algorithms, BAGF obtains excellent configuration parameters with fewer iterations. This paper analyzes the performance of BAGF on four benchmarks and compares with other common optimization algorithms. In addition, the performance improvement of each parameter configuration is analyzed. Finally, the BAGF was tested with the convolution layer of Alexnet, and the results of the Roofline model were analyzed. Compared with the original parameter configuration, the speed of BAGF was increased by 50.09%.

关键词： CNN Parallel computing GPU Auto-tuning

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Exploring the Potential of Deep Learning algorithms in Medical image processing: A Comprehensive Analysis 5th

Exploring the Potential of Deep Learning Algorithms in Medic...

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5th International conference on Communication and Intelligent systems, ICCIS 2023

作者： Pal, Ganesh Prasad Pal, Raju Department of Computer Science and Engineering Jaypee Institute of Information Technology Noida India Department of Computer Science and Engineering School of Information and Communication Technology Gautam Buddha University Greater Noida India

ISBN: (纸本)9789819720781

In this study, we comprehensively examine the potential of deep learning algorithms in the domain of medical image processing. Through a systematic analysis of existing literature, we explore the applications, methodologies, and outcomes of these algorithms across diverse medical specialties. Our analysis underscores the transformative impact of convolutional neural networks and transfer learning techniques in enhancing diagnostic accuracy, image segmentation, and disease detection. We discuss challenges such as data variability, ethics, and interpretability while emphasizing the importance of interdisciplinary collaboration. By synthesizing findings and identifying future directions, we highlight the promising role of deep learning in revolutionizing healthcare diagnostics and treatment planning. After conducting a thorough analysis of various literature and empirical studies, we have evaluated the capabilities and drawbacks of deep learning models in managing different medical imaging modalities such as X-rays, MRIs, and CT scans. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： COviD-19

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