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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521-530 订阅

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Block-based streaming blind deconvolution for space-variant turbulence mitigation

Block-based streaming blind deconvolution for space-variant ...

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conference on Unconventional Imaging and Adaptive Optics

作者： Sheppard, David G. Iler, Amber Hunt, Bobby R. KBR Inc Houston TX 77002 USA

ISBN: (数字)9781510645110

ISBN: (纸本)9781510645110;9781510645103

A block-based, streaming, multi-frame blind deconvolution (MFBD) mitigation method is presented for restoration of imagery impacted by space-variant atmospheric turbulence. An incremental approach is taken, referred to as "streaming", which operates on each new frame as it arrives. For each new frame, an optimization is initiated to minimize the error between the new frame and forward modeling of the object and point spread function (PSF). To adapt to space-variant turbulence conditions, the fundamental units of operation are small, overlapping blocks of pixels extracted from the entire frame. The optimization for each new block is seeded using the solution from the same block area in the previous frame to produce a cumulative, improved, block solution. For each block, the algorithm implements stochastic gradient descent, and alternates between seeking a solution for the PSF and the object, while holding the other constant. To assist in regularizing the PSF solutions during the processing, the PSF estimate is projected onto a sparse dictionary representation of PSFs. The block solutions are combined using an overlap/add method to produce the object estimate after each frame. Our method can utilize either a Picard iterative process or a limited memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) algorithm with bound constraints. A comparison of results is presented using data simulated with high-fidelity to real systems and environments. While incremental methods for blind deconvolution have been reported in the literature, our method has been developed explicitly for the routine mechanics of blind deconvolution.

关键词： atmospheric turbulence blind deconvolution block-based image processing L-BFGS stochastic gradient descent

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Recognition and Classification of Pollen Grains Based on the Use of statistical, Dynamic image Characteristics, and Unique Properties of Neural Networks 11th

Recognition and Classification of Pollen Grains Based on the...

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11th World conference on Intelligent Systems for Industrial Automation, WCIS 2020

作者： Ibragimovich, Jumanov Isroil Isroilovich, Djumanov Olim Abdullayevich, Safarov Rustam Samarkand State University Samarkand Uzbekistan

ISBN: (纸本)9783030680039

Constructive approaches, principles, and methods of identification, recognition, and classification of pollen grains have been developed based on dynamic models, neural networks (NN), and mechanisms for extracting statistical, dynamic, specific information characteristics, preliminary image processing, and setting variables. Pollen recognition models are proposed, which rely on mechanisms for checking the similarity of micro-objects, highlighting the outline, dividing them into segments (classes), using specific features that differ from other classes, extracting the desired properties, characteristics, knowledge, performing image identification based on a set of numerical characteristics, database and knowledge base rules [1, 2]. The traditional computational schemes of multilayer NN, Kohonen NN, radial - basic network, which is optimized on the basis of network learning mechanisms with the variable setting, control of identification error along the boundaries of permissible values, taking into account the stationary, quasi-stationary and nonstationary behavior of the dynamics of the image contour, are investigated. Generalized algorithms for identifying images of pollen grains based on combined models and mechanisms of NN are proposed. The efficiency of the algorithms was investigated according to the criteria of the mean square error and the speed of information processing with combined dynamic and neural network identification models, linear, nonlinear, and compositional connections of neurons between network layers [3]. A software package for visualization, recognition, and classification of images of pollen grains has been developed, the implementations of which have been tested taking into account the conditions of a priori insufficiency, parametric uncertainty, and nonstationarity of processes. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Errors

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SL-MoE: A Two-Stage Mixture-of-Experts Sequence Learning Framework for Forecasting Rapid Intensification of Tropical Cyclone

SL-MoE: A Two-Stage Mixture-of-Experts Sequence Learning Fra...

引用

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

作者： Jian Xu Yang Lei Guangqi Zhu Yunling Feng Bo Xiao Qifeng Qian Yajing Xu Beijing University of Posts and Telecommunications China National Meteorological Center China

Forecasting rapid intensification (RI) of tropical cyclones (TC) is an important and challenging task. However, existing RI forecast methods pay little attention to the imbalanced distribution of RI with dynamic statistical models or ma-chine learning methods. Actually, RI prediction is a class-imbalanced problem in nature. Due to its contingency, there is a great fluctuate in the class distribution between positive (RI) and negative (non-RI) samples each year. To address the above issues, we propose a novel two-stage mixture-of-experts sequence learning framework (SL-MoE) which aims to solve the class imbalanced distribution with decoupled two learning stages, thereby boosting RI forecast: (1) in the representation learning stage, the shared sequence learning backbone is trained to extract general features from class imbalanced data and the TC life flag is included to lessen the impact of the TC dying period. (2) in the mixture-of-expert learning stage, we train diverse experts with different losses and obtain weights using self-supervised weight learning to handle different test class distributions from a single training long-tailed distribution. Specially,we propose a high-resolution multi-satellite image dataset named TCHF. We perform extensive experiments, which achieve new state-of-the-art performance on the TCHF and TCIR datasets. 1

关键词： Training Representation learning Satellites Tropical cyclones Signal processing Predictive models Feature extraction

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A Novel Model for Recognising Handwritten Devanagari Numerals using Machine Learning

A Novel Model for Recognising Handwritten Devanagari Numeral...

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Pervasive Computing and Social Networking (ICPCSN), International conference on

作者： Ch. Prathima Ramprakash Reddy Arava K Sevitha G Manikanth D Vinay C Surya School of Computing Mohan Babu University (formerly Sree Vidyanikethan Engineering College) Tirupati India Department of CSE KSRM College of Engineering Department of CSSE Sree Vidyanikethan Engineering College Tirupati India

ISBN: (数字)9798350386349

ISBN: (纸本)9798350386356

This research study performs a comprehensive comparative analysis aimed at developing effective machine learning models for classifying handwritten Devanagari numerals. The research focuses on evaluating the performance of various models to determine the most accurate classification approach. Initiating with data pre-processing, including feature extraction and normalization, the data is prepared for model training and assessment. A diverse range of machine learning models, from traditional methods like Support Vector Machines (SVM) to advanced techniques such as Random Forests, K-Nearest Neighbors, and Convolutional Neural Networks, are considered for the comparative analysis, ensuring a thorough assessment of classification capabilities. Cross-validation techniques are employed during model training and testing to enhance reliability. statistical tests are utilized to assess the performance variations among models, enhancing the robustness of the analysis. Visual representations of performance metrics and comparison results offer clear insights. This research study aims to identify the most suitable machine learning model for handwritten Devanagari numeral classification, potentially advancing character recognition systems and linguistic applications.

关键词： Support vector machines Training Handwriting recognition Analytical models Machine learning algorithms image recognition Nearest neighbor methods

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Deep Learning Framework for Brain Tumor Classification through Histopathological image

Deep Learning Framework for Brain Tumor Classification throu...

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2022 International conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2022

作者： Sandhiya, B. UshaKiruthika, S. Kanaga Suba Raja, S. Shiv Nadar University Department of CSE Chennai India Easwari Engineering College Chennai India

ISBN: (数字)9781665474139

ISBN: (纸本)9781665474139

Further most oncologist varies in their conclusions around the determination utilizing histopathological-pictures, so the manual diagnosis of brain tumour is troublesome, and it too needs elevated involvement. Building a show of brain tumour classification is a fundamental assignment. CAD empowers productive and exact conclusion of this sort of imaging. In addition, it makes a difference early determination of brain tumours. Profound learning has started to advance within the final decades, so convolutional neural systems/networks are utilized to classify brain tumours. We presented the programmed brain tumour classification strategies to assist specialists in examination and treatment, which can naturally classify the histopathological pictures as generous or dangerous. This study's hypothetical premise is creating a profound learning tumour classification framework. It employs a profound learning approach that is the proposed CNN model comprises of three stages. The primary stage is pre-processing. The moment stage is the highlight extraction organize that takes input as augmented preprocessed pictures. This stage has two divisions. The primary stage comprises of three parallel CNN branches with different filter sizes to extricate distinctive include levels. We blended the three parallel branches within the moment stage, which produced a highlight combination way that diminished the time complexity and upgraded the outlined methods, performance. The third stage classifies the melded highlights as generous or dangerous. The Cancer-Genome-Atlas Glioblastoma Multiform-DB (TCGA-GBM) is utilized &implemented and contains 19,507 files of brain tumour tissue for 237 patients. The data was parted into three sections: 60%, 20%, 20% for training, validation, and testing. A few tests were performed to attain the finest demonstrative precision of 91.37 %. The results are compared to the outcomes about of different methods within the writing. The presented preferences of our m

关键词： statistical tests

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The statistical Learning methods in image processing and Facial Recognition 21

The Statistical Learning Methods in image processing and Fac...

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7th International conference on Engineering and MIS, ICEMIS 2021

作者： Mohammed, Tareq Abed Rafeeq, Sarbaz Omar Bayat, Oguz College of Information Technology Imam ja'Afar Al-Sadiq University Kirkuk Iraq College of Engineering Altinbas University Istanbul Istanbul Turkey

ISBN: (纸本)9781450390446

The aim of this paper is to develop a new approach for The statistical Learning methods in image processing and Facial Recognition using the deep learning techniques in python. In the recent years there have been significant advances in face recognition by using deep neural networks. One of the potential next steps is to develop optimized 3D facial recognition. Shifting from 2D to 3D increases complexity of the problem by adding an- other dimension to data, making possible solutions more resource hungry. We will investigate different depth camera based facial recognition techniques and test their performance by deploying them on an embedded processor. We focus on applications for embedded systems and use a small low-resolution time of flight (ToF) camera with our system to keep overall system portable and compact. All faces images are then projected on the feature space ("face space") to find the corresponding coordinators. The face space is composed of "Eigenfaces"or "Fisherfaces"which are actually eigenvectors found after doing a matrix composition - Eigen decomposition. At the heart of Eigenface method is the Principal Component Analysis (PCA) - one of the most popular unsupervised learning algorithms - while Fisherface is a better version of the previous one which makes use of both Principal Component Analysis and Linear Discrimination Analysis (LDA) to get more reliable results. The algorithms were realized by Python in Anaconda. Given initial images in the database, the program can detect and recognize the human faces in the provided pictures before saving them in the database to improve the calculation accuracy in the future. After evaluation, the recognition general results are exported on the screen with details included in the text files. © 2021 ACM.

关键词： Python

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Gaussian mixture solvers for diffusion models 23

Gaussian mixture solvers for diffusion models

引用

Proceedings of the 37th International conference on Neural Information processing Systems

作者： Hanzhong Guo Cheng Lu Fan Bao Tianyu Pang Shuicheng Yan Chao Du Chongxuan Li Gaoling School of Artificial Intelligence Renmin University of China and Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China Tsinghua University Sea AI Lab Singapore

Recently, diffusion models have achieved great success in generative tasks. Sampling from diffusion models is equivalent to solving the reverse diffusion stochastic differential equations (SDEs) or the corresponding probability flow ordinary differential equations (ODEs). In comparison, SDE-based solvers can generate samples of higher quality and are suited for image translation tasks like stroke-based synthesis. During inference, however, existing SDE-based solvers are severely constrained by the efficiency-effectiveness dilemma. Our investigation suggests that this is because the Gaussian assumption in the reverse transition kernel is frequently violated (even in the case of simple mixture data) given a limited number of discretization steps. To overcome this limitation, we introduce a novel class of SDE-based solvers called Gaussian Mixture Solvers (QMS) for diffusion models. Our solver estimates the first three-order moments and optimizes the parameters of a Gaussian mixture transition kernel using generalized methods of moments in each step during sampling. Empirically, our solver outperforms numerous SDE-based solvers in terms of sample quality in image generation and stroke-based synthesis in various diffusion models, which validates the motivation and effectiveness of GMS. Our code is available at https://***/Guohanzhong/GMS.

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Overview of SAR image Feature Extraction and Target Recognition 1

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2nd International conference on 3D Imaging Technologies-Multidimensional Signal processing and Deep Learning, 3DIT-MSPandDL 2020

作者： Wang, Zhixu Xin, Zhihui Huang, Xiaoqiao Sun, Yu Xuan, Jiayu Yunnan Key Lab of Opto-Electronic Information Technology Yunnan Normal University Kunming China

ISBN: (数字)9789811633911

ISBN: (纸本)9789811633904

Currently, synthetic aperture radar (SAR) image target recognition is one of the research focus and hot spot all over the world. This paper briefly introduces the process of SAR image target recognition and summarizes the methods of SAR image feature extraction from four aspects: optical or visual characteristics, statistical analysis or mathematical transformation, scattering mechanism and depth learning, and then simulates the typical methods. Finally, the problems of SAR image feature extraction and target recognition technology are analyzed, and the future development direction is prospected. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Feature extraction

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Effective Classification of Plant Disease Using image processing and Machine Learning 3

Effective Classification of Plant Disease Using Image Proces...

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3rd IEEE International Virtual conference on Innovations in Power and Advanced Computing Technologies, i-PACT 2021

作者： Chanda, Pramit Brata Sarkar, Subir Kumar Jadavpur University Electronics and Tele Communication Engineering Kolkata India

ISBN: (纸本)9781665426916

In agricultural domain early identify leaf disease from plant leaf primarily is very crucial for Rapid growth of plants. So prevention of losses of crops are very much depended on appropriate plant disease identification and prevention. So uses of capturing the photos for predicting plant diseases effectively. Here SVM based machine learning approach are applied on area of affected region and classify properly from disease affected image. The work is sub divided into some sections as image acquisition, image pre-processing, feature extraction, segmentation and classification. In pre-process parts are transferred into rgb image with filtering is applied to reduction of noise and after that feature are extracted from images. Next the segmentation is applied for detecting the region of clusters with kmeans approach and other methods as threshold based segmentation or region growing is used to efficiently detect disease affected area. Then Classifier are used for detecting healthy and un healthy images. Here MKSVM based classifier is used for classifying the diseases as Cercospora Leaf Spot, Alternaria Alternata, Anthracnose, Bacterial Blight etc. So some of the statistical parameters are like mean, sd, rms, entropy are measured that are significantly gives good performance for identify disease. Here the results obtained using this approach gives more than 95% of accuracy rates comparing to other approaches of detecting plant diseases efficiently. © 2021 IEEE.

关键词： Median filters

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Leukocytes Segmentation and Classification in Digital Microscopic images 4

Leukocytes Segmentation and Classification in Digital Micros...

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4th IEEE International conference on Computing and Information Sciences, ICCIS 2021

作者： Hussain, Muhammad Abbas Ahmad, Ibtihaj Shaukat, Arslan Islam, Zain UI College of EME Department of Computer Engineering Pakistan Department of Electrical Engineering Pakistan Pakistan

ISBN: (纸本)9781665494410

image processing and machine learning have recently gained positive contributions to various medical procedures. One of the diagnostic processes' essential requirements in many diseases is laboratory tests, such as the Complete Blood Count (CBC) test. In CBC, various leukocytes, also known as White Blood Cells (WBC), are segmented, classified, and counted by a lab technician in microscopic slides. This process is very tiresome and requires a human technician with specialized skill sets. This research proposes a fully automatic algorithm for the segmentation and classification of white blood cells. The proposed method applies pre-processing techniques to digital microscopic images. White blood cells are then segmented based on color pallets. Hybrid features are extracted from the segmented images based on the fusion of local binary patterns and statistical features. Then various classifiers are used for the classification of WBC. Results suggest that the Support Vector Machine (SVM) and Artificial Neural Networks (ANN) outclass other classifiers. It was observed that the proposed methodology outperformed existing methods in terms of classification accuracy (97.5%). © 2021 IEEE.

关键词： image classification

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