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

共 4715 条记录，以下是311-320 订阅

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Non-periodic stochastic Resonance Signal processing Method Based on Generation Countermeasure Network 10th

Non-periodic Stochastic Resonance Signal Processing Method B...

引用

10th International conference on Frontier Computing, FC 2020

作者： Chen, Yuqiang Zhang, Wulin Xu, Xiaoyu Mudanjiang Normal University No. 191 Wenhua Street Heilongjiang Province Mudanjiang157000 China

ISBN: (纸本)9789811601149

The purpose of this paper is to study the processing method of aperiodic stochastic resonance signal based on the generation countermeasure network. In this paper, three kinds of system models of stochastic resonance and super-resolution network based on generative countermeasure network are used as the methods, and the signal detection experiments of bi-stable and segmented hybrid models are used to better demonstrate the methods in this paper. Combined with the effectiveness analysis of speech signal of generative countermeasure network, the measurement index of on-board resonance system and the processing analysis of aperiodic square wave signal by stochastic resonance system, The results show that the PSNR of the image enhanced by the bi-stable SR system is 16.14 dB, which is significantly better than that of the fingerprint image enhanced by the traditional binary image enhancement algorithm. The PSNR of the fingerprint image is 7.68 dB, 7.72 dB and 7.51 dB, respectively, and the data exceeds nearly 50%. Compared with the classical bi-stable system, the segmented hybrid system not only has no saturation phenomenon in the input and output but also has a better effect in extracting signal under strong noise, which brings practical significance and value to the information random signal. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Signal to noise ratio

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Analysis of Accuracy in Identification of Bone Fracture using Canny Edge and Prewitt Edge Detection Approach 2

Analysis of Accuracy in Identification of Bone Fracture usin...

引用

2nd IEEE International conference on Advances in Computing, Communication and Applied Informatics, ACCAI 2023

作者： Harshavardhan, Y. Anitha, G. Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Electronics and Communication Engineering Tamilnadu Chennai602105 India

ISBN: (纸本)9798350315905

In this study, we compare the New Modified Canny edge detection method to the Prewitt edge detection method to determine whether the method is more effective at identifying bone fractures. We will accomplish this by contrasting the two approaches. methods and Materials: This study takes a ten-person sample and compares it to another ten-person sample using an innovative modified Canny edge detector (CED) and a Prewitt edge detector (PED). With the use of the g power software, we were able to compare our sample sizes using the following settings: alpha = 0.05, enrollment ratio = 0.1, 95% confidence interval = 80%, and power = 80%. The results of the study demonstrated that a customised version of the Canny edge detection method had an accuracy of 95% and a specificity of 86%. This result outperformed the Prewitt edge detection method in terms of accuracy and specificity. With an initial test statistical power of 80% in SPSS analysis and an accuracy of p = 0.006 (p 0.05) and specificity of p = 0.025 (p 0.05), it was determined that the data obtained left no room for error. The significance level was too low (p-value 0.05) to rule out this conclusion. Compared to the traditional Prewitt edge detection approach, the novel modified Canny edge detection method is significantly more accurate when diagnosing bone fractures. © 2023 IEEE.

关键词： image processing

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FEDKA: Federated Knowledge Augmentation for Multi-Center Medical image Segmentation on non-IID Data

FEDKA: Federated Knowledge Augmentation for Multi-Center Med...

引用

International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Yuhao Zhang Shaoming Duan Xinyu Zha Jinhang Su Peiyi Han Chuanyi Liu School of Computer Science Harbin Institute of Technology (Shenzhen) Shenzhen China Peng Cheng Laboratory Shenzhen China Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies Shenzhen China

Federated learning (FL) allows decentralized medical institutions to collaboratively learn a shared global model without breaching data privacy. However, in the context of medical image segmentation, data distributions across centers may vary a lot due to the diverse imaging protocols, vendors and partial annotation, which usually hampers the optimization convergence and the performance of FL. In this paper, we propose a novel approach called federated knowledge augmentation (FedKA) to address the non-IID (non-independent and identically distributed) problem in medical image segmentation within FL. FedKA first designs a pixel-wise knowledge augmentation method to preserve the knowledge of globally labeled regions for the local model during training, and augments each local feature statistical knowledge based on a mixture of Gaussian distribution. Our experiments on public datasets show the superiority of FedKA over the state-of-the-art methods in test performance.

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image Encryption Design Based on Henon Mapping

Image Encryption Design Based on Henon Mapping

引用

image processing, Electronics and Computers (IPEC), Asia-Pacific conference on

作者： Wenyi Su Mingjie Zhou Jiaqing Bao Lanjing Yang Liping Zhu Guodong Li School of Mathematics and Computing Science Guilin University of Electronic Technology Guilin China School of Mathematics and Data Science Changji University Changji China

ISBN: (数字)9798350374407

ISBN: (纸本)9798350374414

image data, compared to textual information, possesses visual characteristics that require novel encryption methods to ensure its security. Therefore, it is necessary to explore new encryption techniques tailored specifically for image data. By applying the Henon chaotic map, two random sequences are generated. These sequences, along with a given key, are used to produce four equally-sized random matrices through specific formulas. Two of these matrices are employed in the forward and backward diffusion modules, while the remaining two are utilized in the permutation module. Through two rounds of diffusion operations and one permutation operation, the four random matrices are utilized to encrypt the image, resulting in a secure, encrypted image. The security performance of this encryption algorithm is analyzed theoretically, considering encryption and decryption speeds, histogram statistical characteristics, correlations between adjacent pixels, and information entropy. Additionally, simulation experiments demonstrate the effectiveness of this digital image encryption scheme, showcasing excellent encryption results and security performance.

关键词： Military communication Visualization Chaotic communication Military computing Digital images Encryption Performance analysis Information entropy Optimization Random sequences

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TextureWGAN: Texture Preserving WGAN with MLE Regularizer for Inverse Problems

TextureWGAN: Texture Preserving WGAN with MLE Regularizer fo...

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conference on Medical Imaging - image processing

作者： Ikuta, Masaki Zhang, Jun Univ Wisconsin Dept Elect Engn & Comp Sci Milwaukee WI 53211 USA

ISBN: (纸本)9781510640221

Many algorithms and methods have been proposed for inverse problems particularly with the recent surge of interest in machine learning and deep learning methods. Among all proposed methods, the most popular and effective method is the convolutional neural network (CNN) with mean square error (MSE). This method has been proven effective in super-resolution, image de-noising, and image reconstruction. However, this method is known to over-smooth images due to the nature of MSE. MSE based methods minimize Euclidean distance for all pixels between a baseline image and a generated image by CNN and ignore the spatial information of the pixels such as image texture. In this paper, we proposed a new method based on Wasserstein GAN (WGAN) for inverse problems. We showed that the WGAN-based method was effective to preserve image texture. It also used a maximum likelihood estimation (MLE) regularizer to preserve pixel fidelity. Maintaining image texture and pixel fidelity is the most important requirement for medical imaging. We used Peak Signal to Noise Ratio (PSNR) and Structure Similarity (SSIM) to evaluate the proposed method quantitatively. We also conducted the first-order and the second-order statistical image texture analysis to assess image texture.

关键词： Generative Adversarial Networks (GANs) Wasserstein distance inverse problems Convolutional Neural Network (CNN) Maximum Likelihood Estimation (MLE) statistical texture analysis

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Longitudinal Structure Analysis and Segmentation Algorithm of Dongba Document 1

引用

3rd International conference on 3D Imaging Technologies - Multidimensional Signal processing and Deep Learning (3DIT-MSP and DL)

作者： Yang, Yuting Kang, Houliang Suzhou Vocat Univ Sch Comp Engn Suzhou 215000 Jiangsu Peoples R China Suzhou Vocat Univ Sports Dept Suzhou 215000 Jiangsu Peoples R China

ISBN: (数字)9789811924484

ISBN: (纸本)9789811924484;9789811924477

With the rapid development of information technology, Dongba hieroglyphs and Dongba culture have also been rapidly spread through information technology and the Internet. However, most of the Dongba documents still exist in the form of images at present, and it is impossible to extract and retrieve the text. Therefore, combined with the structural characteristics of the Dongba document, first we normalize the size of the document image;then, combined with the vertical projection algorithm to perform preliminary segmentation of the document, we extract the structural characteristics of the vertical column from it. Finally, we use statistical methods to analyze the attributes and effectiveness of different segmentation columns, and by removing abnormal columns, we achieve effective vertical segmentation of documents and text column extraction. The algorithm is intuitive, vivid, simple to implement, and lays a foundation for detailed analysis and digitization of documents.

关键词： Analysis of Dongba document structure Vertical segmentation statistical analysis Vertical projection split

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Study on depressive symptom trajectories based on heterogeneous statistical learning 24

Study on depressive symptom trajectories based on heterogene...

引用

9th International conference on Biomedical Signal and image processing, ICBIP 2024

作者： He, Yan Li, Likang Ma, Chuoxin Guangdong Provincial Key Laboratory IRADS BNU-HKBU United International College Zhuhai519087 China

ISBN: (纸本)9798400717970

Depression, a critical and widespread mood disorder, poses a major challenge to international health. It significantly disrupts personal, professional, and academic pursuits beyond ordinary sadness, affecting vast numbers globally and emerging as a primary driver of disability, per the World Health Organization. Its association with serious physical conditions such as heart disease, diabetes, and heightened suicide risk further exacerbates its impact on personal well-being, familial stability, and economic health, underscoring its extensive burden on society. This study aims to explore the relationship between educational levels and depressive symptoms among the elderly in China, particularly through the application of heterogeneous mediation effect analysis to identify potential mediating variables between the two. Utilizing the longitudinal dataset from the China Health and Retirement Longitudinal Study (CHARLS), the study employs structural equation modeling (SEM) to screen potential mediating variables and explores the heterogeneity of mediation effects among different groups through grouping methods. The results indicate that subjects can be divided into four groups, each with significantly different levels and trends of depressive symptoms. Further analysis reveals variations in the paths of mediation effects between different groups, highlighting the complexity of the relationship between education level and depressive symptoms. The significance of this study lies in its first application of heterogeneous mediation effect analysis to the CHARLS dataset, offering a new perspective on understanding how education impacts the mental health of the elderly through various mediating paths. We recommend that future research further investigate the confounding factors in the mediating relationships between education, economic status, and depressive symptoms to more comprehensively understand the interactions among these variables. © 2024 Copyright held by the owner/au

关键词： Contrastive Learning

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MMFA-Net: Multi-Modal Feature Learning and Multi-Head Fuse Attention for image Manipulation Detection and Localization

MMFA-Net: Multi-Modal Feature Learning and Multi-Head Fuse A...

引用

image processing, Computer Vision and Machine Learning (ICICML), International conference on

作者： Chengming Liu Wenshuai Yao School of Cyber Science and Engineering Zhengzhou University ZhengZhou China

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

With the advent of various advanced image processing techniques and the increasingly low manipulation threshold, image Manipulation Detection and Localization (IMDL) has become a complex and challenging task. Previous studies have shown the importance of manipulation artifacts in the IMDL problem. However, existing methods often struggle to capture effective artifacts when addressing with refined forgery (post-processed forgery). In this paper, we introduce MMFA-Net, a novel approach for IMDL. We extract RGB features, Laplacian high-frequency features, Bayar statistical anomaly features, and manipulation imprint from the image through the Multi-modal Feature Extractor module, concatenating them as multi-modal feature inputs to capture manipulation edge artifacts, which are increasingly difficult to detect in image manipulation. In addition, we propose an innovative Multi-head Fuse Attention (MFA) module. Building on the backbone network's ability to model inconsistencies between original and tampered regions at coarse-to-fine granularities, the MFA module can more effectively capture contextual dependencies in the spatial dimensions and inter-channel correlations for more precise localization. The performance on several public datasets demonstrates that the proposed network outperforms existing SOTA models and remains robust against refined forgery.

关键词： Location awareness Representation learning Computer vision image forensics Correlation Laplace equations Fuses image edge detection Feature extraction Forgery

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Research on Face image Denoising Method Based on Adaptive Bidimensional Empirical Mode Decomposition and stochastic Resonance Method

Research on Face Image Denoising Method Based on Adaptive Bi...

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Electronics and Electrical Engineering Technology (EEET), International conference on

作者： Ximin Sun Jiangkai Jia Bin Zhang Yi Hao Bin Zheng Bo Sun Zihao Li Yong Li State Gride Ecommerce Technology CO. LTD State Gride Ecommerce Technology CO. LTD Tian’jin China

ISBN: (数字)9798350395594

ISBN: (纸本)9798350395600

Face recognition technology has a wide range of application prospects, and important application value in content-based retrieval, digital video processing and visual detection. However, the noise in the process of image acquisition will directly affect the recognition ability of face image, and the stochastic resonance theory can convert the specific intensity of noise signal energy into useful signals. Therefore, this paper proposes an image processed method based on adaptive bidimensional empirical mode decomposition (BEMD) and stochastic resonance (SR) theory is used to improve the quality of noised face images. The noised face image is decomposed by adaptive BEMD algorithm, and multiple Intrinsic mode function (IMF) components and residual are obtained. The three IMF components containing the least feature information are selected and deleted by threshold parameters. After the remaining IMFs and the residual is reconstructed, the noise energy is transferred to the image features by SR. Meanwhile, the optimal parameters of the two-dimensional stochastic resonance system are automatically obtained by taking the Peak signal to noise ratio (PSNR) as the detection index, so as to realize the quality of the face image. Four different methods are used to improve 50 face images with glasses and 50 face images without glasses. Compared with the BEMD algorithm, median filter and mean filter, the face image data set improved by the method proposed in this paper has the highest PSNR, which improves the image quality.

关键词： Empirical mode decomposition Adaptive systems PSNR Face recognition stochastic resonance Noise Glass

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Evaluating the Impact of Point Cloud Colorization on Semantic Segmentation Accuracy

Evaluating the Impact of Point Cloud Colorization on Semanti...

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International conference on Vision, image and Signal processing (ICVISP)

作者： Qinfeng Zhu Jiaze Cao Yuanzhi Cai Lei Fan Department of Civil Engineering Xi'an Jiaotong-Liverpool University Suzhou China CSIRO Mineral Resources CSIRO Kensington Australia

ISBN: (数字)9798331541460

ISBN: (纸本)9798331541477

Point cloud semantic segmentation, the process of classifying each point into predefined categories, is essential for 3D scene understanding. While image-based segmentation is widely adopted due to its maturity, methods relying solely on RGB information often suffer from degraded performance due to color inaccuracies. Recent advancements have incorporated additional features such as intensity and geometric information, yet RGB channels continue to negatively impact segmentation accuracy when errors in colorization occur. Despite this, previous studies have not rigorously quantified the effects of erroneous colorization on segmentation performance. In this paper, we propose a novel statistical approach to evaluate the impact of inaccurate RGB information on image-based point cloud segmentation. We categorize RGB inaccuracies into two types: incorrect color information and similar color information. Our results demonstrate that both types of color inaccuracies significantly degrade segmentation accuracy, with similar color errors particularly affecting the extraction of geometric features. These findings highlight the critical need to reassess the role of RGB information in point cloud segmentation and its implications for future algorithm design.

关键词： Point cloud compression Accuracy Three-dimensional displays image color analysis Semantic segmentation image edge detection Signal processing algorithms Signal processing Feature extraction

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