检索结果-内蒙古大学图书馆

4th International conference on Electronic Information Engineering and Computer Technology, EIECT 2024

作者： Yan, Xiaoyue Mu, Dazhong School of Information Engineering Beijing Institute Of Graphic Communication Beijing China

ISBN: (纸本)9798331528850

When processing text images with traditional binarization methods, the image background noise often causes the results to become blurred or leads to the loss of edge details. To solve this problem, this paper proposes an image binarization method based on stochastic resonance theory. First, we divide the image into sub-blocks and set a binarization threshold based on the statistical properties of the pixels in each sub-block. Next, the image signal is converted into a one-dimensional time series signal using Hilbert scanning. The processed signals are input into a threshold array system, which amplifies the weak edge information in the input signals through the stochastic resonance phenomenon. Subsequently, we performed modulation and inverse scanning on the output signals of the system to generate the binary image for each sub-block. Finally, all sub-block binary images were combined to complete the binarization of the overall image. Experimental results show that the method proposed in this paper can effectively retain the detailed information of document images and significantly outperforms the traditional binarization method regarding image quality. © 2024 IEEE.

关键词： stochastic systems

来源：评论

学校读者我要写书评

暂无评论

NOISY image RESTORATION BASED ON CONDITIONAL ACCELERATION SCORE APPROXIMATION 49

NOISY IMAGE RESTORATION BASED ON CONDITIONAL ACCELERATION SC...

引用

49th IEEE International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Shi, Ziqiang Liu, Rujie Fujitsu R&D Ctr Beijing Peoples R China

ISBN: (纸本)9798350344868;9798350344851

In recent years, score-based generative models (SGM) have achieved state-of-the-art (SOTA) performance in noisy image restoration [1, 2]. But at present, most of these methods are performed in the position space, and there is a lack in modeling of the velocity and acceleration of the image on the restoration path. In this paper, we propose a new image restoration method called conditional acceleration score approximation (CASA), which introduces velocity and acceleration variables on top of the data position along the recovery path. Guided by the degraded image, CASA can effectively and dynamically control the direction and speed of motion along the diffusion path in the reverse-time stochastic differential equation. Therefore, the key to this process is how to inject the degraded image as a guidance into the third-order reverse-time process in this position-velocity-acceleration space, especially in the evolution direction of the diffusion path. We propose a strategy for approximating the conditional acceleration score by decomposing the true posterior CAS into a priori CAS and an observed acceleration score for the measurement at the current moment. Experiments on 3 different datasets and 7 kinds of restoration tasks show that CASA is better than other methods and achieves a new SOTA.

关键词： image restoration diffusion generative model Langevin dynamics stochastic differential equations conditional score estimation

来源：评论

学校读者我要写书评

暂无评论

INCREASING TRUST IN image ANALYSIS BY DETECTING TRELLIS QUANTIZATION IN JPEG imageS 31

INCREASING TRUST IN IMAGE ANALYSIS BY DETECTING TRELLIS QUAN...

引用

2024 International conference on image processing

作者： Hofer, Nora Univ Innsbruck Secur & Privacy Lab Innsbruck Austria

ISBN: (纸本)9798350349405;9798350349399

JPEG image forensics investigates the authenticity and origin of compressed images. Many established methods rely on assumptions about the statistical distribution of quantized discrete cosine transform coefficients. However, JPEG implementations that use trellis quantization, such as mozjpeg, produce images that challenge these assumptions. In this study, we demonstrate that artifacts resulting from trellis quantization can compromise the reliability of established forensic methods and cause false alarms for innocuous images. We address this issue by presenting methods to detect trellis artifacts and validating their robustness in scenarios commonly encountered in forensic analyses.

关键词： trustworthy image forensics steganalysis trellis quantization mozjpeg

来源：评论

学校读者我要写书评

暂无评论

Characterization of Early Termination for stochastic Computing image processing Circuits 14

Characterization of Early Termination for Stochastic Computi...

引用

14th Symposium on Computer Applications & Industrial Electronics (ISCAIE)

作者： Kamisian, Izam Marsono, Muhammad Nadzir Paraman, Norlina Hamzah, Afiq Zakaria, Jamaluddin Univ Teknol Malaysia UTM Johor Baharu Malaysia

ISBN: (纸本)9798350348798;9798350348804

stochastic computing (SC) performance is significantly impacted by the length of the bitstream, which directly affects both computation time and throughput. Researchers have proposed early termination methods to reduce bitstream length, relying on stochastic bitstream patterns generated by stochastic number generators (SNGs). In this study, we investigate early termination specifically for median filter image processing applications. We analyze several SNGs, including those based on 'randperm,' linear feedback shift registers (LFSR), Streaming Accurate (SA), and counter-based approaches. These SNGs are evaluated using different image inputs. Our experimental results demonstrate that early termination can be safely employed. Notably, the use of counter-based SNGs naturally facilitates early termination in median filter applications. By leveraging this approach, we achieve bitstream length reduction without sacrificing accuracy. The extent of reduction varies based on the specific image inputs. In summary, our findings highlight the effectiveness of early termination strategies, particularly when utilizing counter-based SNGs for median filter computations.

关键词： stochastic computing early termination image processing

来源：评论

学校读者我要写书评

暂无评论

statistically Principled Deep Learning for SAR image Segmentation 38

Statistically Principled Deep Learning for SAR Image Segment...

引用

38th AAAI conference on Artificial Intelligence (AAAI) / 36th conference on Innovative Applications of Artificial Intelligence / 14th Symposium on Educational Advances in Artificial Intelligence

作者： Goldberg, Cassandra Bowdoin Coll Brunswick ME 04011 USA

ISBN: (纸本)1577358872

This paper proposes a novel approach for Synthetic Aperture Radar (SAR) image segmentation by incorporating known statistical properties of SAR into deep learning models. We generate synthetic data using the Generalized Gamma distribution, modify the U-Net architecture to encompass statistical moments, and employ stochastic distance losses for improved segmentation performance. Evaluation against traditional methods will reveal the potential of this approach to advance SAR image analysis, with broader applications in environmental monitoring and general image segmentation tasks.

关键词： Synthetic aperture radar

来源：评论

学校读者我要写书评

暂无评论

Weighted Features Based Classification of Polarimetric SAR images 13

Weighted Features Based Classification of Polarimetric SAR I...

引用

13th Iranian/3rd International Machine Vision and image processing conference (MVIP)

作者： Saneipour, Fatemeh Imani, Maryam Ghassemian, Hassan Tarbiat Modares Univ Fac Elect & Comp Engn Image Proc & Informat Anal Lab Tehran Iran

ISBN: (纸本)9798350350494;9798350350500

Today, classification of polarimetric images is an important topic where various statistical pattern recognition methods have been used to achieve the high accurate classification maps. In this work, weighting the polarimetric features according to their statistical behavior (the mean vector and variance values as the first and second statistics) is suggested to improve the PolSAR image classification. A weighted feature matrix is composed and applied to the popular classifiers such as maximum likelihood, K-nearest neighbor and support vector machine. The weighted feature matrix can be also implemented on other arbitrary classifiers to improve their discrimination ability. The experiments on the L-band AIRSAR dataset show appropriate classification results.

关键词： polarimetric synthetic aperture radar (PolSAR) weighted features classification

来源：评论

学校读者我要写书评

暂无评论

An adaptive enhancement method based on stochastic parallel gradient descent of glioma image

引用

IET image processing 2023年第14期17卷 3976-3985页

作者： Wang, Hongfei Peng, Xinhao Ma, ShiQing Wang, Shuai Xu, Chuan Yang, Ping Chinese Acad Sci Key Lab Adapt Opt Chengdu Peoples R China Chinese Acad Sci Inst Opt & Elect Chengdu Peoples R China Univ Chinese Acad Sci Sch Elect Elect & Commun Engn Beijing Peoples R China Univ Elect Sci & Technol China Sch Med Chengdu Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Univ Elect Sci & Technol China Sichuan Prov Peoples Hosp Sichuan Acad Med Sci Dept Oncol Chengdu Sichuan Peoples R China Univ Elect Sci & Technol China Sichuan Prov Peoples Hosp Canc Inst Sichuan Acad Med Sci Chengdu Sichuan Peoples R China Univ Elect Sci & Technol China Sichuan Prov Peoples Hosp Dept Lab Med Chengdu Sichuan Peoples R China Univ Elect Sci & Technol China Sichuan Prov Peoples Hosp Sichuan Prov Key Lab Human Dis Gene Study Chengdu Sichuan Peoples R China

Brain tumour diagnosis is significant for both physicians and patients, but the low contrast and the artefacts of MRI glioma images always affect the diagnostic accuracy. The existing mainstream image enhancement methods are insufficient in improving contrast and suppressing artefacts simultaneously. To enrich the field of glioma image enhancement, this research proposed a glioma image enhancement method based on histogram modification and total variational using stochastic parallel gradient descent (SPGD) algorithm. Firstly, this method modifies the cumulative distribution function on the image histogram and performs gamma correction on the image according to the modified histogram to obtain a contrast-enhanced image. Then, the method suppresses the artefacts of glioma images by total variational and wavelet denoising algorithm. To get better enhancement images, the optimal parameters in the proposed method are searched by the SPGD algorithm. The statistical studies performed on 580 real glioma images demonstrate that the authors' approach can outperform the existing mainstream image enhancement methods. The results show that the proposed method increases the discrete entropy of the image by 8.9% and the contrast by 2.8% compared to original images. The enhanced images are produced by the proposed method with a natural appearance, appealing contrast, less degradation, and reasonable detail preservation. To enrich the field of glioma image enhancement, this paper proposed a glioma image enhancement framework based on histogram modification and total variational using stochastic parallel gradient descent (SPGD) algorithm. The results show that the proposed framework has superior performance compared to several existing glioma image enhancement methods. image

关键词： image denoising image enhancement parallel algorithms artefacts suppression contrast improvement histogram modification stochastic parallel gradient descent

来源：评论

学校读者我要写书评

暂无评论

Enhancing Multi-view Mammography image Classification: By using Breast Region Extraction Method and statistical Features 13

Enhancing Multi-view Mammography Image Classification: By us...

引用

13th Iranian/3rd International Machine Vision and image processing conference (MVIP)

作者： Bidabadi, Neda Shirany Mahmoudzadeh, Elham Isfahan Univ Technol Dept Elect & Comp Engn Esfahan *** Iran

ISBN: (纸本)9798350350494;9798350350500

Breast cancer is one of the most dangerous diseases among women. Different methods are used to diagnose this cancer that among these, imaging and computer-aided systems are more common. In these systems, one of the most important step is preprocessing and removing unnecessary areas of the images, as well as extracting the chest area. In this paper, we present a method that consists of preprocessing, feature extraction, and using a machine learning classifier. In the preprocessing step, we propose a method to extract the region of interest in both angles of mammography images. The proposed novel method includes applying gamma correction thresholding to the images and obtaining two binary images based on the proposed threshold using the Otsu method. Results show the proposed method successfully removes the chest muscle with 98% accuracy. In the next, for feature extraction phase, we utilize three different methods for extracting features. Finally, by employing an Extra tree model classifier, we classify mammography images into normal and abnormal. By incorporating the block-based feature extraction method, we achieve 98% accuracy in classification. Overall, our approach demonstrates the effectiveness of preprocessing and feature extraction for diagnosing breast cancer using mammography images.

关键词： Mammography image Region of Interest Extraction Breast Cancer statistical Feature Extraction

来源：评论

学校读者我要写书评

暂无评论

Brain tumour detection in magnetic resonance imaging using Levenberg-Marquardt backpropagation neural network

引用

IET image processing 2023年第1期17卷 88-103页

作者： Ghahramani, Marzieh Shiri, Nabiollah Islamic Azad Univ Shiraz Branch Dept Elect Engn Shiraz *** Iran

Magnetic resonance imaging (MRI) is a high-quality medical image that is used to detect brain tumours in a complex and time-consuming manner. In this study, a back propagation neural network (BPNN) along with the Levenberg-Marquardt algorithm (LMA) is proposed to classify MRIs and diagnose brain tumours in a simple and fast process. The BPNN has 10 neurons in the hidden layer, and the default function of the feedforward feeds is mean squared error (MSE). The LMA is optimized as a multivariable adaptive approach and considerably decreases the MSE of the BPNN, so the errors of the tumour classification are diminished. The proposed method follows four steps including preprocessing, skull removal, feature extraction, and classification. The input MRIs are converted to greyscale, resized, and thresholding is performed in the preprocessing step and followed by skull removal. Morphological operations of closing, opening, and dilation are used to segment abnormal areas in the MRIs, and the opening operator recognizes the tumour more accurately. Using statistical analysis and a grey-level co-occurrence matrix (GLCM) 12 features are extracted from the MRIs and used as the inputs of the BPNN. To evaluate the proposed method, 670 normal and 670 abnormal brain MRIs are used as input data, and the classification is performed in 0.494 s. The accuracy, sensitivity, specificity, precision, dice, recall, and MSE are 98.7%, 97.61%, 99.7%, 97.61%, 98.6%, 97.61%, and 0.005, respectively. The approach is accurate and fast for medical images classification.

关键词： medical image classification image segmentation feature extraction tumour classification default function Biology and medical computing input MRIs high-quality medical image image classification biomedical MRI Medical magnetic resonance imaging and spectroscopy multivariable adaptive approach brain tumour detection Biophysics of neurophysiological processes brain tumour diagnosis Neural nets backpropagation mean squared error opening operator greyscale Computer vision and image processing techniques Biomedical magnetic resonance imaging and spectroscopy statistical analysis grey-level cooccurrence matrix neural nets skull removal hidden layer preprocessing step image recognition MSE preprocessing removal Levenberg-Marquardt algorithm magnetic resonance imaging image thresholding abnormal brain MRIs BPNN morphological operations brain LMA Other topics in statistics Patient diagnostic methods and instrumentation tumours Levenberg-Marquardt backpropagation neural network medical image processing Probability theory, stochastic processes, and statistics

来源：评论

学校读者我要写书评

暂无评论

Stable Multilevel Deep Neural Networks for Option Pricing and xVAs Using Forward-Backward stochastic Differential Equations 24

Stable Multilevel Deep Neural Networks for Option Pricing an...

引用

5th International conference on AI in Finance

作者： Naarayan, Aadhithya Ashok Parpas, Panos Imperial Coll Dept Comp London England Imperial Coll London Dept Comp London England

ISBN: (纸本)9798400710810

Deep learning techniques have significantly impacted fields such as image processing, computer vision, and natural language processing. However, their influence on quantitative finance, particularly in option pricing, hedging, and portfolio management, has been limited. Traditional financial applications rely on rigorous mathematical models and established numerical methods like Monte Carlo and finite difference methods. However, these methods struggle with high-dimensional problems. Recent works propose using Deep Neural Networks (DNNs) to solve high-dimensional Partial Differential Equations (PDEs) in finance, potentially overcoming the limitations of conventional techniques. Despite the recent progress, DNN methods face high computational costs, stability issues, and generally fail to meet the high accuracy requirements of the financial industry. This paper addresses these challenges. To address the high computational cost of training deep neural networks for financial applications, we propose a multilevel architecture inspired by multilevel Monte Carlo methods. To enhance stability, we adopt a dynamical systems perspective and utilize the NAIS-Net architecture, which ensures global asymptotic stability. For improving accuracy, we leverage the forward and backward systems of stochastic differential equations (FBSDEs) for option pricing problems. We also provide a theoretical argument that suggests that the proposed approach is superior to Physics Informed Neural Networks. Finally, the proposed methodology is implemented on several option pricing and xVA problems and is shown to achieve higher efficiency and accuracy, notably improving the pricing of options and xVAs by an order of magnitude over state-of-the-art methods.

关键词： Forward-Backward stochastic Differential Equations Deep Neural Networks Partial Differential Equations Option Pricing

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：