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

共 4722 条记录，以下是481-490 订阅

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Brain Tumor Detection using Convolutional Neural Networks (CNNs)

Brain Tumor Detection using Convolutional Neural Networks (C...

引用

Data Science, Agents & Artificial Intelligence (ICDSAAI), International conference on

作者： Sasimekala M M Siva V Shanmuga Shyam B Veeramalai Sankaradass C Selvaganesan R. Ponnusamy Department of Biomedical Engineering Chennai Institute of Technology Chennai Tamil Nadu India Dept. of Computer Science and Engineering Chennai Institute of Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Chennai Institute of Technology Chennai Department of Computer Science and Engineering Chennai Institute of Technology Chennai Tamil Nadu India

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

The brain's unchecked and fast cell development fuels a tumor. It may prove lethal if left untreated in the early stages. Accurate segmentation and classification remain difficult despite many noteworthy efforts and promising results. The differences in the tumor size, form, and proportions make brain tumor detection extremely difficult. The primary goal of this study is to provide researchers with a thorough literature review on the use of magnetic resonance (MR) imaging in diagnosing brain malignancies. This study suggested multiple methods for detecting tumors and brain cancer using statistical image processing and artificial intelligence. An assessment matrix for a certain system employing particular systems and dataset kinds is also displayed in this study. The anatomy of brain tumors, available data sets, augmentation techniques, component extraction, and classification between machine learning (ML) and deep learning (DL) models are also explained in this work. Lastly, the study gathers all pertinent information for identifying and comprehending tumors, including their advantages, disadvantages, developments, and future trends. In this study, an attempt is made to classify the tumors using CNNs.

关键词： Deep learning image segmentation Magnetic resonance Imaging Market research Data models Fuels Data mining Systematic literature review image classification

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Online Variational Filtering and Parameter Learning 35

Online Variational Filtering and Parameter Learning

引用

35th Annual conference on Neural Information processing Systems (NeurIPS)

作者： Campbell, Andrew Shi, Yuyang Rainforth, Tom Doucet, Arnaud Univ Oxford Dept Stat Oxford England

ISBN: (纸本)9781713845393

We present a variational method for online state estimation and parameter learning in state-space models (SSMs), a ubiquitous class of latent variable models for sequential data. As per standard batch variational techniques, we use stochastic gradients to simultaneously optimize a lower bound on the log evidence with respect to both model parameters and a variational approximation of the states' posterior distribution. However, unlike existing approaches, our method is able to operate in an entirely online manner, such that historic observations do not require revisitation after being incorporated and the cost of updates at each time step remains constant, despite the growing dimensionality of the joint posterior distribution of the states. This is achieved by utilizing backward decompositions of this joint posterior distribution and of its variational approximation, combined with Bellman-type recursions for the evidence lower bound and its gradients. We demonstrate the performance of this methodology across several examples, including high-dimensional SSMs and sequential Variational Auto-Encoders.

关键词： State space methods

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Low-Rank Multi-View Subspace Clustering with Context Embedding

Low-Rank Multi-View Subspace Clustering with Context Embeddi...

引用

Algorithm, image processing and Machine Vision (AIPMV), International conference on

作者： Liping Chen College of Computer and Cyber Security Fujian Normal University Fuzhou China Digital Fujian Intemet-of-Things Laboratory of Environmental Monitoring Fujian Normal University Fuzhou China

ISBN: (数字)9798350390254

ISBN: (纸本)9798350390261

Multi-view subspace clustering plays an important role in different AI tasks. However the iterative processes of most of multi-view subspace clustering are often too complex, which costs a lot of time. The outstanding Facilitated Low-rank Multi-view Subspace Clustering method maintain the low-rank structure within each view in a efficient way. It regarded the matrix of a specific view as the multiplication of an orthogonal dictionary and a latent representation. However, it obtained the coefficient matrix from each view by simple addition. Since the data from different view originate from different devices and apply different methods for regularization, they often have different probability distribution structures. Low-rank Multi-view Subspace Clustering with Context Embedding (CLMSC) is proposed to exploiting the low-rank consistency across multiple views maintaining context side information. At the same time the CLMSC algorithm learns the original data of different view from statistical perspective, and uses the IQR function to measure the discrimination relationship between the data. Then CLMSC provides a universally effective probabilistic weighting scheme for different views. In the process of multi-view feature fusion, the weight of each feature view is considered. Specifically, the structural similarity matrix of each view is learned according to the probability characteristics, and the participation degree of the phase moderation matrix of each perspective is constrained by reasonable weights. So that the proposed method encourage the potential consistency across different view and retain the ideal structural information. The experimental results show that the proposed method can maintain the effectiveness and improve the accuracy greatly.

关键词： Dictionaries Costs Machine vision Clustering methods Clustering algorithms Probabilistic logic Time measurement Probability distribution Iterative algorithms Periodic structures

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stochastic window transformer for image restoration 22

Stochastic window transformer for image restoration

引用

Proceedings of the 36th International conference on Neural Information processing Systems

作者： Jie Xiao Xueyang Fu Feng Wu Zheng-Jun Zha University of Science and Technology of China Hefei China

ISBN: (纸本)9781713871088

Thanks to the powerful representation capabilities, transformers have made impressive progress in image restoration. However, existing transformers-based methods do not carefully consider the particularities of image restoration. In general, image restoration requires that an ideal approach should be translation-invariant to the degradation, i.e., the undesirable degradation should be removed irrespective of its position within the image. Furthermore, the local relationships also play a vital role, which should be faithfully exploited for recovering clean images. Nevertheless, most transformers either adopt local attention with the fixed local window strategy or global attention, which unfortunately breaks the translation invariance and causes huge loss of local relationships. To address these issues, we propose an elegant stochastic window strategy for transformers. Specifically, we first introduce the window partition with stochastic shift to replace the original fixed window partition for training. Then, we design a new layer expectation propagation algorithm to efficiently approximate the expectation of the induced stochastic transformer for testing. Our stochastic window transformer not only enjoys powerful representation but also maintains the desired property of translation invariance and locality. Experiments validate the stochastic window strategy consistently improves performance on various image restoration tasks (deraining, denoising and deblurring) by significant margins.

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Cosmic Density Field Reconstruction with a Sparsity Prior Using images of Distant Galaxies 9th

Cosmic Density Field Reconstruction with a Sparsity Prior Us...

引用

9th International conference on Big Data Analytics (BDA)

作者： Yoshida, Naoki Li, Xiangchong Univ Tokyo Kavli IPMU WPI Kashiwa Chiba 2778552 Japan

ISBN: (纸本)9783030966003;9783030965990

Future astronomical observations are expected to deliver multi- peta byte images of stars, galaxies, and supernovae. processing the sheer volume of imaging and spectroscopic data is technically challenging, and producing scientific outputs from the big data will remain a key task in the next decade. We develop novel methods based on modern machine learning and deep learning to analyze data from Subaru Hyper Suprime-Cam. In this contribution, we focus on reconstruction of cosmic density field. We use the observation of gravitational lensing effect that causes slight deformation of shapes of galaxies. The collective effect can be used to reconstruct the large-scale density distribution. Our novel technique assuming a sparsity prior allows to reconstruct the density field in full three dimensions. statistical analysis of cosmic structure enables accurate determination of a few fundamental quantities called cosmological parameters that describe the contents and the evolution of the Universe.

关键词： Sparse model image analysis Bayesian inference

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Detecting Fake Political News on Social Media by Support Vector Machine Compared to Naive Bayes

Detecting Fake Political News on Social Media by Support Vec...

引用

International conference on Computational and Characterization Techniques in Engineering & Sciences (CCTES)

作者： M. Sireesha S. Tamilselvan Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Department of Cloud Computing and Image Processing Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350364699

ISBN: (纸本)9798350364705

The primary objective is to enhance the precision of the Support Vector Machine and evaluate it in comparison to the Naive Bayes algorithm in terms of its ability to forecast fake political news on social media networks. This research work focuses on two groups: Support Vector Machine and Naive Bayes Algorithm, where each group is executed for 10 times. The ClinCalc software was used in calculating samples in which the alpha value is considered as 0.05, and the confidence interval of 95%. Result: There is a statistically significant difference between the algorithm used by Improved Support Vector Machine and Naive Baye with P value as 0.001 (Independent Sample t-Test P<0.05) in SPSS statistical analysis. The accuracy of the Improved method Support Vector Machine is 98.12%, which appears to be superior to Naive Baye of 95.03% correspondingly. Conclusion: According to the results, the Support vector machine has an accuracy of 98.12%, which is higher than the Naive Bayes accuracy of 95.03%.

关键词： Support vector machines Cloud computing Accuracy Social networking (online) statistical analysis Distributed databases Prediction algorithms Vectors Naive Bayes methods Fake news

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Comparison of Traditional Tool Combination and Deep Learning methods for Automatic Classification of Steel Surface Defects

Comparison of Traditional Tool Combination and Deep Learning...

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Embedded Systems and Artificial Intelligence (ESAI), International conference on

作者： Zoheir Mentouri Abdelmalek Bouguettaya Hakim Doghmane Kaddour Gherfi Salim Bendjoudi Research Center in Industrial Technologies Cheraga Algiers Algeria Laboratory of Inverse Problems Université 8 Mai 1945 Guelma Algeria Université 8 Mai 1945 Guelma Algeria

ISBN: (数字)9798331522056

ISBN: (纸本)9798331522063

In industry, the automatic recognition of surface defects of flat steel products still represents a real challenge. Indeed, in addition to constraints such as the image noise or blur, there is neither an agreed standard of these defects, which can be of different sizes, shapes, orientations, and locations, nor a standard defect identification method, whether based on traditional tools such as statistical-based, spatial/frequency filtering based or model-based or on newer techniques such as deep learning. Hence, the complexity of the algorithm that deals with this matter always depends on the specific needs of the application. This paper focuses on the comparison of deep learning approaches with a new combination of traditional tools, namely the Gabor wavelets (GW) and the Local Phase Quantization, respectively known for image abrupt change representation and for the invariance to image blur. Both approaches are assessed on the North University (NEU) and a Research Center (RC_DDB) datasets, which include surface defect images of hot rolled steel product. Interesting correct-prediction rates and processing times are achieved for both approaches, with slightly higher scores for the Deep Learning approaches in terms of accuracy, but better computation times for the proposed combination.

关键词： Deep learning Accuracy Quantization (signal) Surface waves Shape Process control Quality control Steel Surface treatment Standards

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Comparative Analysis of image Filtering for Dental Caries image Denoising

Comparative Analysis of Image Filtering for Dental Caries Im...

引用

Science and Information Technology in Smart Administration (ICSINTESA), International conference of

作者： Masayu Alya Nur ‘Aini Siti Nurul Mohd Aqmariah Kanafiah Yessi Jusman Sartika Puspita Faculty of Electrical Engineering Technology Universiti Malaysia Perlis Perlis Malaysia Department of Electrical Engineering Faculty of Engineering Universitas Muhammadiyah Yogyakarta Yogyakarta Indonesia Department of Oral Biology Faculty of Dentistry Universitas Muhammadiyah Yogyakarta Yogyakarta Indonesia

ISBN: (数字)9798350376111

ISBN: (纸本)9798350376128

A pre-processing algorithm is crucial in enhancing diagnostic capabilities in various domains, including medical diagnostics. In particular, dental X-ray imaging is a primary tool for interpreting diseases and making accurate pathology decisions in clinical settings. However, the presence of noise in dental X-ray images poses a significant challenge, affecting diagnostic accuracy. This paper aims to compare and evaluate different image filtering methods for denoising dental caries images to improve diagnostic accuracy. Our This study evaluates evaluated the effectiveness of standard filters commonly used applied in medical imaging, including encompassing the Gaussian Filter, Median Filter, Average Filter, Guided Filter, Bilateral Filter, and Wiener Filterfilters. We Tsystematically compare these filters were systematically compared to identify the most suitable one for denoising dental X-ray images under various noise scenarios, including: Gaussian, Salt and Pepper, and Poisson noise. The results demonstrated that the Guided filter is was particularly effective in removing Gaussian and Poisson noise, while the Median filter is was well-suited for removing Salt and Pepper noise. Both filters outperformed others in qualitative and quantitative analyses, highlighting their efficacy in enhancing the visualization of dental structures and improving diagnostic accuracy in dental imaging.

关键词： Salt Visualization Filters Accuracy Wiener filters statistical analysis Noise image filtering Dentistry X-ray imaging

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Breast image classification based on ResNet and Random forest multilayer classifier model

Breast image classification based on ResNet and Random fores...

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International conference on Bio-engineering for Smart Technologies (BioSMART)

作者： Tang Rui Wang Tianyi Xu Yifan Sun Hongji Teoh Teik Toe Mechanical Manufacturing and Automation Fuzhou University Fuzhou China Digital Media Technology Fuzhou University Fuzhou China NTU Business AI Lab Nanyang Technological University Singapore

In this paper,a more optimal breast cancer detection model based on ResNet and random forest models is developed based on the combination of diagnostic methods of breast cancer pathology slice images and breast cancer detection studies. The proposed model is based on ResNet to classify breast MRI images, and random forest to select some samples and their features to compensate for the overfitting of ResNet in small sample data, and random forest to compensate for the overfitting of ResNet in small sample *** random forest compensates the disadvantage that ResNet generates a lot of redundancy when the sample features are of high *** the two models complement each other,the efficiency of image processing and the accuracy of image classification of the model are ***,a statistical analysis of the test results of the breast cancer detection model in this paper concluded that this model is more accurate and efficient in detecting classified breast cancer than a single machine learning model.

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MicroPoreNet: ComplexandMultilevels Microporosity Characterization of Carbonate Rocks through Semisupervised CNN

MicroPoreNet: ComplexandMultilevels Microporosity Characteri...

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IEEE International conference on Imaging Systems and Techniques (IST)

作者： Sattarin, Shariar Muther, Temoor Dahaghi, Amirmasoud Kalantari Negahban, Shahin Univ Kansas Dept Chem & Petr Engn Lawrence KS 66045 USA

ISBN: (纸本)9781728173719

Characterizing the rock properties of the carbonate core sample has always been challenging due to the complex nature of the sample images obtained during micro-CT *** traditional approaches utilize pore network modeling for predicting porosity and micro-porosities from those images. However, manual and time-consuming segmentation, biasness, inconsistency in image interpretation, and weakness of currently applied statistical methods make the image processing of complex carbonate cores challenging and produce results with significant error. This paper presents a novel workflow of characterizing porosity and micro porosities at different levels while training the processed images with semi-supervised CNN. This study, firstly, processes the 1100 slices of images obtained from a micro-CT scanner with 700 nm resolution. Secondly, the processed images are used to train the semi-supervised CNN for prediction. Lastly, the results are post-processed to create segmented *** segmented i mages are then analyzed and quantified based on their colo rcoding for the porosity and three levelsofmicro-porositypredictions. The results reveal that the workflow cani dentify hidden features in the core sample which cannot be previously decrypted, such as multiple levels of microporosity and complex geological features such as the Crinoid. Also, the predicted porosities fall with thel aboratory range of the sample, i.e., 6to14%.

关键词： image processing Semisupervised CNN micro-CTscan porosity micro-porosity complex carbonate rock

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