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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191-200 订阅

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Monte Carlo Score Matching for image Generation

Monte Carlo Score Matching for Image Generation

引用

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

作者： Nishanth Shetty Chandra Sekhar Seelamantula Department of Electrical Engineering Indian Institute of Science Bangalore India

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Score-based models are state-of-the-art generative models for image generation. We propose a novel loss namely the Monte Carlo Score Matching (MCSM) loss as an approximation of the original score matching loss. MCSM leverages a Taylor-series expansion of the score function to approximate the expensive calculation involved in computing the trace of the Jacobian of the score function. MCSM is competitive with models trained using the Sliced-Score Matching (SSM) loss. We validate the efficacy of the proposed technique in terms of negative log-likelihood and Fréchet Inception distance (FID) on MNIST and CelebA datasets, respectively. In particular, we show that FID of images generated with models trained using MCSM loss is on par with, and in some cases, better than, sliced score-matching for image generation.

关键词： Jacobian matrices Training Monte Carlo methods image synthesis Computational modeling stochastic processes Signal processing Vectors Reliability Speech processing

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2nd International conference on Science, Engineering Management and Information Technology, SEMIT 2023

2nd International Conference on Science, Engineering Managem...

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2nd International conference on Science, Engineering Management and Information Technology, SEMIT 2023

ISBN: (纸本)9783031722837

The proceedings contain 46 papers. The special focus in this conference is on Science, Engineering Management and Information Technology. The topics include: Verification and Validation of Knowledge Engineering Systems: A Life Cycle Framework;The Critical Factors of Success of Gamification in Digital Banking Services: Using Analytic Hierarchy Process (AHP) Approach;e-Shopping Sites Preference Analysis with Multi-criteria Decision-Making methods;advancing Anemia Diagnosis: Harnessing Machine Learning methods for Accurate Detection;monkeypox Detection with K-mer Using Machine Learning Algorithms;cloud Computing Model for Handling Medical Big Data: A Mobile Hospital Pervasive Healthcare Application;Harnessing Advanced AI Techniques: An In-Depth Analysis of Machine Learning Models for Improved Diabetes Prediction;image processing in Toxicology: A Systematic Review;augmented Reality Immersive World with Hologram Special Effect in Early Childhood Education;improving the Visual Ergonomics of Computerised Workplaces Through the Use of Specialised Eye-Rest Software;a Diagnosis Model Based on Federated Learning for Lung Cancer Classification;Arrhythmia Detection from ECG Traces images Using Transfer Learning Approach;enhancing Traffic Flow Prediction in Urban Areas Through Deep Learning and Probe Information: A Comparative Study;an Approach to Multi-agent Deep Q-Network Optimization of Signal Control in Multi-intersection Road Environments to Enhance Urban Traffic Flow;blockchain-Driven Smart Contracts: An Overview of Application Areas and Gap Identification in Construction Management Literature;stochastic Optimization Methodology for Production Planning with Uncertain Demand and Lead Time Based on the Digital Twin;role of Top Management Commitment and Information Technology Investment in Digital Transformation;antecedents of Mobile Banking Apps Adoption Among Consumers in Ghana.

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Performance Analysis of Color Normalization methods in Histopathology images

Performance Analysis of Color Normalization Methods in Histo...

引用

2022 International conference on Automatic Control and Intelligent Systems

作者： Yee, Wong Chung Jian, Tan Xiao Ab Rahman, Khairul Shakir Hoe, Teoh Leong Min, Lu Juei Hang, Quah Yi Ling, Teoh Chai Tunku Abdul Rahman Univ Coll TARUC Dept Elect & Elect Engn Kuala Lumpur Malaysia Tunku Abdul Rahman Univ Coll TARUC Ctr Multimodal Signal Proc Dept Elect & Elect Engn Kuala Lumpur Malaysia Hosp Tuanku Fauziah Dept Pathol Perlis Malaysia

ISBN: (纸本)9781665495813

Color normalization in histopathology is a prominent research topic in the image processing field as color in histopathology images plays a crucial role in diagnosis. As computer-aided diagnosis emerged, color normalization is much crucial as it becomes the foundation of medical image processing to assure algorithm precision and accuracy. In this paper, the main objective is to perform an analysis on three commonly used color normalization methods, namely histogram matching, histogram equalization, and stain unmixing methods. 60 breast histopathology images were used for testing purposes. Four statistical metrics were calculated to measure and determine the applicability of the color normalization methods: Structural similarity index measure (SSIM), Pearson's correlation coefficient (PCC), visual saliency-induced index (VSI), and Gradient similarity (GS). Based on the outputs, it is found that the stain unmixing method demonstrates better than that of the histogram matching and histogram equalization methods with higher values in SSIM and VSI, and comparable values in PCC and GS.

关键词： Color normalization histopathology histogram matching histogram equalization stain unmixing

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2nd International conference on Science, Engineering Management and Information Technology, SEMIT 2023

2nd International Conference on Science, Engineering Managem...

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2nd International conference on Science, Engineering Management and Information Technology, SEMIT 2023

ISBN: (纸本)9783031722868

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Mapping the density distribution pattern of microvasculature in whole-mouse brain 17

Mapping the density distribution pattern of microvasculature...

引用

17th International conference on Photonics and Imaging in Biology and Medicine, PIBM 2024

作者： Li, Yuxin He, Weijie Cao, Jia Li, Anan Li, Xiangning Jiang, Tao School of Computer Science and Engineering Xi'an University of Technology Xi'an710048 China HUST-Suzhou Institute for Brainsmatics Suzhou215123 China Britton Chance Center for Biomedical Photonics Wuhan National Laboratory for Optoelectronics MoE Key Laboratory for Biomedical Photonics Huazhong University of Science and Technology Wuhan430074 China

ISBN: (数字)9781510688070

ISBN: (纸本)9781510688063

Analyzing the vascular network structure of the brain is crucial for understanding brain function and preventing and treating cerebrovascular diseases. By studying indicators such as the length density and branching point density of the brain's microvascular network, we can better understand the brain's vascular network and provide a scientific basis for cerebrovascular disease research. Traditional methods rely on statistical analysis of isolated brain regions, which fail to capture the spatial distribution characteristics of micro-vessels across the entire brain and overlook spatial information from different brain regions. To address this, we propose a computational pipeline for the spatial distribution of vascular density in the brain. First, we use a deep learning model to segment brain vascular images from HD-fMOST imaging of mouse brains. Then, we perform skeletonization on the 3D vascular segmentation images and obtain vessel diameter information through distance transformation. Finally, we filter capillaries based on their diameter and calculate the feature parameters of these capillaries to acquire vascular parameter distributions across the entire brain. The entire process is enhanced by MPI parallel processing and multithreading techniques to improve image processing efficiency. Results indicate that our proposed method can capture the spatial distribution characteristics of microvascular density across the whole brain by integrating multiple datasets. This method provides a powerful tool for comprehensive research on brain vasculature. © 2025 SPIE.

关键词： image segmentation

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Weighted Features Based Classification of Polarimetric SAR images

Weighted Features Based Classification of Polarimetric SAR I...

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Iranian Machine Vision and image processing (MVIP)

作者： Fatemeh Saneipour Maryam Imani Hassan Ghassemian Image Processing and Information Analysis Lab Faculty of Electrical and Computer Engineering Tarbiat Modares University Tehran Iran

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.

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Dual-Modality Guided Artistic Style Transfer with Pre-trained Diffusion Models

Dual-Modality Guided Artistic Style Transfer with Pre-traine...

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

作者： Jiaxiong Liu Xiaolong Xiong Jun Zhou College of Computer and Information Science Southwest University China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Artistic style transfer aims to replicate an artist’s painting style in a different image. While existing pre-trained model-based methods can generate high-quality stylized images, they often lack precise control over stylistic elements. Recent approaches incorporating textual inversion offer more accurate style representations, but significant information loss occurs when transitioning between modalities. To address these issues, we propose Dual-Modality Guided Artistic Style Transfer (DMG), which makes full use of text and image information to enhance the visual effect and content consistency of stylized results. Our approach primarily consists of two key modules: Enhanced Style Encoding (ESE) and Guided Diffusion Generation (GDG). ESE processes information from both modalities to obtain an optimized and more comprehensive style representation. Subsequently, GDG employs stochastic inversion and attention control to ensure accurate delivery of content and style information. Our approach outperforms existing techniques in terms of visual quality and content consistency.

关键词： Accuracy image coding stochastic processes Signal processing Benchmark testing Visual effects Diffusion models Encoding Speech processing Painting

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Truncated proposals for scalable and hassle-free simulation-based inference 36

Truncated proposals for scalable and hassle-free simulation-...

引用

36th conference on Neural Information processing Systems (NeurIPS)

作者： Deistler, Michael Goncalves, Pedro J. Macke, Jakob H. Univ Tubingen Tubingen Germany Max Planck Inst Intelligent Syst Stuttgart Germany

ISBN: (纸本)9781713871088

Simulation-based inference (SBI) solves statistical inverse problems by repeatedly running a stochastic simulator and inferring posterior distributions from model-simulations. To improve simulation efficiency, several inference methods take a sequential approach and iteratively adapt the proposal distributions from which model simulations are generated. However, many of these sequential methods are difficult to use in practice, both because the resulting optimisation problems can be challenging and efficient diagnostic tools are lacking. To overcome these issues, we present Truncated Sequential Neural Posterior Estimation (TSNPE). TSNPE performs sequential inference with truncated proposals, sidestepping the optimisation issues of alternative approaches. In addition, TSNPE allows to efficiently perform coverage tests that can scale to complex models with many parameters. We demonstrate that TSNPE performs on par with previous methods on established benchmark tasks. We then apply TSNPE to two challenging problems from neuroscience and show that TSNPE can successfully obtain the posterior distributions, whereas previous methods fail. Overall, our results demonstrate that TSNPE is an efficient, accurate, and robust inference method that can scale to challenging scientific models.

关键词： Iterative methods

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Frequency-Domain Adaptive Filtering Algorithms for Nonstationary Environments

Frequency-Domain Adaptive Filtering Algorithms for Nonstatio...

引用

IEEE International conference on Signal and image processing (ICSIP)

作者： Yishu Peng Sheng Zhang Zhengchun Zhou School of Information Science and Technology Southwest Jiaotong University Chengdu Sichuan P. R. China

ISBN: (数字)9798350350920

ISBN: (纸本)9798350350937

In nonstationary environments, existing frequency-domain adaptive filtering algorithms would exhibit poor tracking performance. To solve this issue, this paper focuses on developing new frequency-domain adaptive filtering algorithms based on single data. Using the circular matrix of the regression vector, we first establish a model and cost function suitable for a nonstationary system. Next, with resort to the stochastic gradient descent and power normalized methods, the frequency-domain least mean-square algorithm based on single data (SFDLMS) and its normalized version (named SFDNLMS) are derived. Even in the presence of correlated input signals, the proposed SFDNLMS algorithm can provide fast tracking/convergence performance. The transient and steady-state behavior is also studied. Finally, experiment results illustrate the advantages of the proposed algorithms and the reliability of the theoretical analysis.

关键词： Frequency-domain analysis image processing stochastic processes Filtering algorithms Reliability theory Cost function Vectors

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GLCM Based Feature Extraction and Medical X-ray image Classification Using Machine Learning Techniques 1st

GLCM Based Feature Extraction and Medical X-ray Image Classi...

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1st EAI International conference on Intelligent Systems and Machine Learning, ICISML 2022

作者： Rout, Jyotiranjan Das, Swagat Kumar Mohalik, Priyabrata Mohanty, Subhashree Mohanty, Chandan Kumar Behera, Susil Kumar Department of Computer Science and Engineering Balasore College of Engineering and Technology Balasore India

ISBN: (纸本)9783031350771

The automated system is now created with excellent accuracy to detect abnormalities in X-ray images. To enhance the appearance of medical photographs, image pre-processing methods are applied, so that high accuracy can be achieved with constrained means. images are often classified based on their textural properties, which are measured using the Gray Level Co-occurrence Matrix (GLCM). The grey level correlation matrix provides statistical information of the second order on the grey levels of neighboring pixels in a picture (GLCM). In this proposed paper, medical X-ray images are classified and their features are extracted using an ensemble learning model. By extracting image features using the GLCM feature extraction method, this proposed model is able to distinguish between healthy and sick images (Gray level co-occurrence matrix).to improve the efficiency of the Ensemble learning classification method, it is compared against various algorithms using performance indicators, including Logistic regression, Gaussian Naive Bayes, as well as Random Forest. When this approach is compared to existing methods, the proposed ensemble model has an accuracy rate of 97% in classifying normal and diseased images. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： image enhancement

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