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检索条件"任意字段=Conference on Statistical and Stochastic Methods for Image Processing"

共 4717 条记录，以下是371-380 订阅

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A Method of Infrared image Mosaic Based on Keyframe Matching

A Method of Infrared Image Mosaic Based on Keyframe Matching

引用

Machine Vision, image processing and Imaging Technology (MVIPIT), International conference on

作者： Jianming Shao Ya Yu Zheng Xu Chen Ye Dachang Jia Minghao Yan Jiangsu Electric Power Company Wuxi Power Supply Company Wuxi China

ISBN: (数字)9798350306545

ISBN: (纸本)9798350306552

Infrared images usually have a narrow field of view, requiring splicing of multiple image sequences to meet the application requirements of wide field of view and high resolution. However, due to the large overlap between adjacent frame images in infrared image stitching, it can lead to accumulated stitching errors and increased computational costs. An infrared image mosaic method based on keyframe matching is proposed. Firstly, key frames are selected as the stitching object based on the similarity of image statistical vectors. Then, the SURF algorithm is used for feature extraction and image registration. Finally, three typical image fusion methods are used to complete panoramic image stitching. The experiments results based on on infrared image datasets show that this method can significantly reduce computational complexity while ensuring the quality of stitching.

关键词： image registration image resolution Splicing Machine vision Imaging Feature extraction Vectors

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The image Encryption Method Based on Deep Learning and Compressed Sensing

The Image Encryption Method Based on Deep Learning and Compr...

引用

Frontiers Technology of Information and Computer (ICFTIC), IEEE International conference on

作者： Xinjun Zhang Yueheng Zhang Huanhuan Xia School of Electronic and Information Engineering Liaoning Technical University Huludao Liaoning China Hainan Yangpu Development and Construction Holdings Co. Ltd Danzhou Hainan China

ISBN: (数字)9798331541750

ISBN: (纸本)9798331541767

image encryption utilizes the two-dimensional matrix characteristics of digital images to convert recognizable, information-rich images into indistinguishable, noise-like images by applying reversible transformation rules in the spatial domain. Traditional image encryption techniques include chaotic encryption, optical encryption, spatial domain encryption, and DNA encryption. However, some traditional methods face limitations in decryption quality and security, making them susceptible to brute-force attacks or statistical analysis. To address these issues, this paper proposes an image encryption method based on deep learning and compressed sensing. The main innovations lie in three aspects. Firstly, chaotic sequences generated by iterating the initial values of a six-dimensional cellular neural network system are used as a dataset, which is then divided into training and testing sets and input into a convolutional neural network for processing. Secondly, two measurement matrices are used for two-dimensional compressed sensing of the image, one derived from the Logistic chaotic system and the other being a random measurement matrix. Thirdly, during the encryption process, the processed prediction results from the test set are used to scramble the image. The scrambled image is then converted into a matrix and XORed with a newly processed measurement matrix generated by the Logistic chaotic system to produce a new matrix. This new matrix is then used to XOR encrypt the image, generating the encrypted output.

关键词： Deep learning Training Technological innovation statistical analysis Encryption Optical sensors Matrix converters Compressed sensing Logistics Testing

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Automated total micro-IBA using Advanced image processing and Machine Learning 25

Automated total micro-IBA using Advanced Image Processing an...

引用

25th International conference on Ion Beam Analysis and 17th International conference on Particle Induce X-Ray Emission, IBA-PIXE 2021 and 23rd International conference on Secondary Ion Mass Spectrometry, SIMS 2021

作者： Solis-Lerma, D. Khodja, H. Université Paris-Saclay CEA CNRS NIMBE LEEL Gif-sur-Yvette91191 France

We have developed a Python code that aims at automatizing the analysis of generic micro-IBA data by associating statistical methods and machine learning algorithms. The code is organized in two parts: hyperspectral image analysis and composition prediction. In the first stage, main phases and local anomalies are detected separately using PCA and DWEST methods, respectively. In the prediction stage, we use the model generated by a trained artificial neural network. The network is fed with simulated particle and x-ray spectra generated from the SIMNRA and Gulys software codes. For particle spectra, we paid particular attention to the cross section selection that goes beyond already implemented SIMNRA functionalities. To limit the impact of the simulation time on the overall code performance, we make use of data augmentation. When using simulated data as input, we found that the trained neural network predicts stoichiometries and thicknesses with an excellent agreement, even for complex targets composed of several elements and layers. Regarding realistic experimental data, we still get reasonable predictions but remain dependant of cross section quality. The code can combine data from RBS, NRA, ERDA and PIXE and should pave the way for fully automatized micro-ion beam analysis. © Published under licence by IOP Publishing Ltd.

关键词： Forecasting

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Joint estimation of image rotation angle and scaling factor

Joint estimation of image rotation angle and scaling factor

引用

Asia-Pacific-Signal-and-Information-processing-Association Annual Summit and conference (APSIPA ASC)

作者： Yu, Kun Yang, Rongsong Zeng, Hui Peng, Anjie Southwest Univ Sci & Technol Sch Comp Sci & Technol Mianyang Sichuan Peoples R China

ISBN: (纸本)9789881476890

Scaling and rotation are often involved in image tampering, and thus have attracted wide attention in image forensics. However, existing studies usually focus on detection or parameter estimation of a single operation, e. g., scaling or rotation. When a probe image has been both scaled and rotated, the statistical characteristics on which previous methods are based become much complicated and the parameter estimation task becomes much challenging. By analyzing the interaction between the features of both operations, a joint parameter estimation method is proposed in this paper. Firstly, some candidate estimations of the rotation angle are derived from the cyclic spectrum of the questioned image. Then, by taking the dependence between the peaks caused by rotation and those caused by scaling-then-rotation into consideration, these candidates can be further checked. Finally, both the scaling factor and the rotation angle can be derived with the proposed method. The proposed method is verified by conducting experiments on a subset of the BOSSbase dataset, achieves accuracy of 84.96% in rotation angle estimation, 78.88% in scaling factor estimation, and 77.13% in the estimation of both parameters.

关键词： image tampering scaling factor estimation rotation angle estimation cyclic spectrum joint parameter estimation

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Enhancing CNN Classification with Lamarckian Memetic Algorithms and Local Search

Enhancing CNN Classification with Lamarckian Memetic Algorit...

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Signal processing and Advance Research in Computing (SPARC), International conference on

作者： Akhilbaran Ghosh Rama Sai Adithya Kalidindi Tata Consultancy Services Limited Mumbai India Shiv Nadar University Dadri Uttar Pradesh India

ISBN: (数字)9798350385205

ISBN: (纸本)9798350385212

Optimization is critical for achieving optimal performance in deep neural networks (DNNs). Traditional gradient-based methods, such as stochastic gradient descent (SGD) and ADAM, often face challenges like local minima entrapment and slow convergence rates. This paper explores population-based metaheuristic optimization algorithms for image classification networks. We propose a novel approach integrating a two-stage training technique with population-based optimization algorithms and local search capabilities to enhance solution quality and convergence speed. In the first stage, a global search is conducted using a population-based algorithm to explore the solution space extensively, allowing the network to escape local minima and find promising regions. In the second stage, a local search fine-tunes the solutions from the global search, ensuring improved accuracy and stability. Our experiments show that the proposed method outperforms state-of-the-art gradient-based techniques like ADAM in accuracy and computational efficiency, particularly in scenarios with high computational complexity and numerous trainable parameters. The results suggest that our approach offers a robust and efficient alternative for weight optimization in convolutional neural networks (CNNs). Future work will explore integrating adaptive mechanisms for parameter tuning and extending the method to other types of neural networks and real-time applications.

关键词： Training Accuracy Signal processing algorithms Feature extraction Classification algorithms Convolutional neural networks Optimization Genetic algorithms Convergence image classification

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Owner Identification from Postal Correspondence Using Machine Learning

Owner Identification from Postal Correspondence Using Machin...

引用

Delhi Section conference (DELCON), IEEE

作者： Lipika Sharma B. S. Ashwath Babu Sumit Goswami Guru Tegh Bahadur 4th Centenary Engineering College (Affiliated to GGSIPU) New Delhi India Bennett University Greater Noida India Defence Research and Development Organisation (DRDO) New Delhi India

ISBN: (数字)9798331518592

ISBN: (纸本)9798331518608

The postal service is a crucial part of information transmission in today's world. Heavily relying on the delivery to the correct owner, it faces the problem of distinguishing the current and past owners. Our research seeks to identify the current and past recipients of postal letters by examining address frequencies and patterns. It incorporates various machine learning models such as classification models and clustering analysis. Using statistical and machine learning methods, ownership is determined by parsing postal envelope contents and extracting addresses and names by image processing. This approach provides a comprehensive understanding of change in ownership over time. The methodology used in the research integrates Named Entity Recognition (NER), Optical Character Recognition (OCR), and Address Frequency Analysis for the purpose. The machine learning models accurately identify the owner from postal correspondence with 75% accuracy.

关键词： Analytical models image processing Optical character recognition Noise Machine learning Named entity recognition Information processing Data mining Faces Postal services

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An Intelligent image processing-based Approach to Optimize Vehicle Headlamp Aiming 18

An Intelligent Image Processing-based Approach to Optimize V...

引用

18th International Multi-conference on Systems, Signals & Devices (SSD)

作者： Navaei, Javad Babakmehr, Mohammad Kalamdani, Rajeev Liu, Yu-Ning Qiu, Shanshan Farhan, J. Blackful, Jasper Ford Motor Co Global Data Insight & Analyt Dearborn MI 48121 USA Ford Motor Co Global Adv Mfg Dearborn MI 48121 USA

ISBN: (纸本)9781665414937

In this paper, a novel image processing-based approach is proposed to optimize vehicle headlamps aiming. Currently, most aiming devices rely on numerical derivative oriented methods to find the essential focal features, also called reference points, to perform the aiming process. However, these approaches are not robust, and minor changes in isocurves' smoothness may result in finding inaccurate aiming focal features. To address the associated robustness issue, a statistical signal processing-based approach named penalized contrast for changepoint detection is proposed. Experimental results indicate a high accuracy level of the proposed method concerning the master-tuned ground truth case studies while suggesting a robust mathematical process. Furthermore, it provides an extra level of flexibility for defining alternative focal features by calculating several points along isocurves, each of which can be considered as an axillary reference point for enhancing the aiming process.

关键词： Headlamp image processing Reference Point Contour Changepoint Detection

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Data processing in CPSS Platform Based on Data Mining and Cloud Edge Computing

Data Processing in CPSS Platform Based on Data Mining and Cl...

引用

2022 International conference on 3D Immersion, Interaction and Multi-Sensory Experiences, ICDIIME 2022

作者： Sun, Haimin Sun, Mengcan Zhao, Lu Luxun Aacademy of Fine Arts Liaoning Shenyang China

ISBN: (数字)9781665490092

ISBN: (纸本)9781665490092

CPSS, a better social-physical information system, can better achieve the management and control goals of security and efficiency of complex systems. The large amount of data information in this CPSS platform needs to be processed reasonably and quickly. The purpose of this article is to solve the problem of processing a large amount of data information in the CPSS platform. In combination with traditional statistical analysis technology and data mining technology(DMT), a detailed analysis and research on how to process the data in the CPSS platform is more detailed., Theoretical analysis method, case analysis method, logical analysis method and other methods. By understanding the current status of domestic and foreign related research and DMT, it analyzes the main problems existing in the data processing and management of CPSS platform in China, and innovates DMT It is applied to the data processing of social physical information system, and the feasibility of data processing technology in CPSS platform is discussed systematically. The results show that the data processing method in CPSS platform based on data mining can enable CPSS platform to efficiently extract useful information when dealing with issues such as security of complex systems, thereby making the processing of massive data easier, more convenient, and more efficient. © 2022 IEEE.

关键词： Data mining

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WebAuthML: A Web-Based Approach for Banknote Authentication Using Machine Learning and image processing

WebAuthML: A Web-Based Approach for Banknote Authentication ...

引用

IEEE India conference (INDICON)

作者： Srinivas Arukonda SriLakshmi Voddelli Department of Computer Science and Engineering SRM UNIVERSITY AP Amaravathi AP India School of Computer Science and Engineering VIT-AP University Amaravathi AP India

ISBN: (数字)9798350391282

ISBN: (纸本)9798350391299

Counterfeit detection in banknotes remains a significant challenge, given the advanced techniques employed by counterfeits. Many existing solutions are either in accessible to the general public or lack the robustness required for reliable authentication. To overcome these limitations, this study proposes a web-based system for bank note verification, integrating machine learning and image processing. The system allows users to upload images of banknotes through a user-friendly interface designed with responsive web technologies, while backend operations are managed using Django. image preprocessing methods, including Gaussian blurring, normalization, and Sobel edge detection, are applied to enhance visual quality and extract essential statistical features such as entropy, variance, skewness, and kurtosis. These features serve as inputs to a logistic regression model that classifies banknotes as authentic or counterfeit. Experimental results reveal that the proposed system achieves high accuracy on a balanced dataset. Additionally, comparative analysis with other machine learning classifiers shows that the system out performs existing state-of-the-art models, offering are liable solution for practical use.

关键词： Logistic regression Visualization Accuracy image edge detection Computational modeling Authentication Machine learning Feature extraction Robustness Real-time systems

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Towards No Shadow: Region-based Shadow Compensation on Low-altitude Urban Aerial images

Towards No Shadow: Region-based Shadow Compensation on Low-a...

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IEEE International conference on Signal and image processing Applications (ICSIPA)

作者： Shahbe Mat-Desa Wan-Noorshahida Mohd-Isa Petra Gomez-Krämer Roslee M. Noramiza Hashim Junaidi Abdullah Aziah Ali Zarina Che-Embi Amalina Ibrahim Faculty of Computing and Informatics Multimedia University Cyberjaya Malaysia L3i Laboratory La Rochelle University La Rochelle France Faculty of Engineering Multimedia University Cyberjaya Malaysia

ISBN: (数字)9798350352368

ISBN: (纸本)9798350352375

Low-altitude aerial images contain multiple shadowed regions, and a shadowed region frequently covers several non-homogenous regions of objects and surfaces. Using the information of neighboring shadow-free regions, a shadowed region can be efficiently compensated with reliable output. In this paper, using the transfer of statistical color properties, a new region-based shadow compensation approach is proposed. The proposed method demonstrates a significant improvement in the overall color mean of shadowed regions, increasing from 76.49 to 141.67, nearing the value of 172.51 observed in shadow-free regions. Additionally, the overall standard deviation of the shadowed region also improves from 31.01 to 43.29, approximately 93.68% matching the 46.21 standard deviation observed in the shadow-free region. The visual results are comparable to those achieved by state-of-the-art deep-learning methodologies and are reasonably acceptable for challenging aerial urban environments.

关键词： image quality Visualization Smoothing methods image color analysis Urban areas Reliability Standards

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