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

共 4708 条记录，以下是41-50 订阅

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Blind image Separation Using Composite Criteria based on statistical Information and Sparse Representation 12

Blind Image Separation Using Composite Criteria based on Sta...

引用

12th International conference on image processing Theory, Tools and Applications, IPTA 2023

作者： Boudjenouia, Fouad Abed-Meraim, Karim Chetouani, Aladine Jennane, Rachid Polytechnic Military School Algiers Algeria University of Orléans Prisme Laboratory Orléans France University of Orléans Idp Institute UMR-CNRS 7013 Orléans France

ISBN: (纸本)9798350325416

This paper deals with blind image separation by exploiting the statistical characteristics of the mixtures (information related to the sources independence) with the sparsity of the signals. More precisely, we investigate and compare the gain that can be reached by considering two different signal properties, namely the statistical independence and the sparse representation. These properties are considered in a 'dual' as well as in a 'hybrid' mode. Both approaches are compared, simulation and experimental results demonstrate the efficiency of the proposed methods. © 2023 IEEE.

关键词： Blind source separation

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Algorithm for finding reference brightness correction coefficients 3

Algorithm for finding reference brightness correction coeffi...

引用

3rd International conference on Optics, Computer Applications, and Materials Science, CMSD 2023

作者： Abdullayeva, Ozoda Beknazarova, Saida Namangan Engeneering-Construction Institute Namangan Uzbekistan Tashkent University of Information Technologies Named after Muhammad Al-Khwarizmi Tashkent Uzbekistan

ISBN: (纸本)9781510674486

This article presents an algorithm for determining reference brightness correction coefficients to improve image quality. The algorithm utilizes a combination of statistical analysis and image processing techniques to identify and correct brightness discrepancies in digital images. By establishing a robust and efficient method for calculating these coefficients, the algorithm aims to enhance the overall visual fidelity of images across various applications, such as photography, medical imaging, and remote sensing. The proposed algorithm demonstrates its effectiveness through experimental results and comparisons with existing methods, highlighting its potential for practical implementation in image processing workflows. In the world, the filtering of digital images by the convolution method with a pulse characteristic in the spectral region scientific research is being conducted to improve the quality level of digital television images, methods for modeling filtration processes and highly efficient control systems in a number of priority areas, including: on the formation of mathematical models of filtration processes, improving the methods of wavelet, Fourier, Haar, Walsh-Hadamard, Karhunen-Loev in increasing the clarity and brightness of images based on linear and nonlinear differential equations;creation of methods for eliminating additive, pulsed and adaptive-Gaussian types of noise in images using additive and adaptive filtering;methods of algorithms and software for introducing intra-frame and interframe image transformations;methods of adaptive brightness system control using the Chebyshev matrix series;methods of gradient, static and Laplace methods for image segmentation and dividing it into contours;formation of criteria and conditions for evaluating image quality. Conducting scientific research in the above research areas confirms the relevance of the topic of this article. © 2024 SPIE. All rights reserved.

关键词： image enhancement

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An Approach to Automatic Leaf Recognition of Bangladeshi Plants Using image processing Techniques and Random Forests 2

An Approach to Automatic Leaf Recognition of Bangladeshi Pla...

引用

2nd International conference on Power, Electrical, Electronics and Industrial Applications, PEEIACON 2024

作者： Hassan, Reyad Akter, Sameya Rahman, Md Obaidur Faridpur Engineering College Dept. of Computer Science and Engineering Faridpur Bangladesh

ISBN: (纸本)9798331517984

This study proposes a novel approach for identi-fying plant leaves using a combination of handcrafted visual leaf image features(shape, color, texture, and preliminary vein properties), their extraction strategies (based on image processing and statistical measures), and classification methods. To do so, a 5-step approach is presented comprising image acquisition, image pre-processing, feature extraction, classification steps, comparison, and evaluation. The presented approach is tested on publicly available standard leaf image datasets such as Flavia, Swedish, and our self-collected BDLeaf20 dataset comprising 20 Bangladeshi local plant species which include fruit, flower, and medicinal plants bearing a total of 1072 leaf images. The plant species included in the BDleaf20 dataset vary in terms of their rarity and popularity within the region. To investigate the effectiveness of the proposed approach, Random Forests classifier is used achieving accuracy scores of 97.21 %,99.11 %, and 93.17% respectively on the Flavia, Swedish, and BDLeaf20 datasets. © 2024 IEEE.

关键词： Random forests

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Feature extraction for pneumonia diagnosis using conventional methods 1

Feature extraction for pneumonia diagnosis using conventiona...

引用

1st International conference on Electrical, Computer, Telecommunication and Energy Technologies

作者： Beghoura, Imane Benssalah, Mustapha Sbargoud, Fazia Ecole Mil Polytech Signal Proc Lab Algiers Algeria Ecole Mil Polytech Comlplexe Syst Control Lab Algiers Algeria

ISBN: (纸本)9798350388497;9798350388480

Medical image processing plays a crucial role in pneumonia diagnosis and classification using chest X-ray images. This research investigates key phases of medical image processing, focusing on Preprocessing, Feature Extraction, and Classification. Texture analysis methods, notably Local Binary Patterns (LBP) and Gray-Level Co-occurrence Matrix (GLCM), have been widely employed in medical imaging due to the inherent importance of textures in medical images. The significance of robust feature extraction methods lies in their ability to identify complex patterns indicative of various medical issues. By conducting experiments on a public chest X-ray dataset, this research employs established machine-learning classifiers to evaluate the efficacy of LBP and GLCM. The findings highlight the superior performance of GLCM in capturing nuanced textural features, showcasing its potential as a principal contributor to enhancing pneumonia classification accuracy. However, the combination of both techniques produces even superior outcomes. LBP, known for its proficiency in capturing local texture patterns, complements GLCM, which excels in extracting global statistical textural information. The combination of these methods enhances the feature space, incorporating both local variations and long-range dependencies, thereby enriching the disease-discriminating patterns, particularly in pneumonia cases. From a wider perspective, texture analysis methods play a crucial role in enhancing the comprehension of medical images, thereby improving the accuracy and reliability of diagnoses.

关键词： GLCM LBP Feature extraction Pneumonia

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High-Speed Compilation of Large-Scale stochastic Circuits 8

High-Speed Compilation of Large-Scale Stochastic Circuits

引用

8th IEEE International conference on Rebooting Computing, ICRC 2023

作者： Ilov, Alex Paler, Alexandru Aalto University Espoo Finland

ISBN: (纸本)9798350382044

stochastic computing (SC) is executed on bitstreams which encode probabilities into the ratio between the number of one bits and the length of the stream. SC is successfully applied, for example, to image processing and beliefpropagation (BP) of low-density parity-check (LDPC) codes. With increased interest into SC, there is a gap with respect to the available scalable design automation tools needed for the fast prototyping of SC circuits. We bridge this gap by starting from the *** library, and implement data structures and methods to achieve orders of magnitude speed-ups for the compilation of SC circuits. We demonstrate the speedups by compiling BP decoders for LDPC codes. Our improvements enable the compilation of circuits which were previously out of reach for the tools. Our methods pave the way towards scalable design automation of SC. © 2023 IEEE.

关键词： image processing

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stochastic Super-Resolution For Gaussian Textures 48

Stochastic Super-Resolution For Gaussian Textures

引用

48th IEEE International conference on Acoustics, Speech and Signal processing, ICASSP 2023

作者： Pierret, Émile Galerne, Bruno Université de Tours Cnrs Institut Denis Poisson Université d'Orléans France France

ISBN: (纸本)9781728163277

Super-resolution (SR) is an ill-posed inverse problem which consists in proposing high-resolution images consistent with a given low-resolution one. While most SR algorithms are deterministic, stochastic SR deals with designing a stochastic sampler generating any realistic SR solution. The goal of this paper is to show that stochastic SR is a well-posed and solvable problem when restricting to Gaussian stationary textures. Using Gaussian conditional sampling and exploiting the stationarity assumption, we propose an efficient algorithm based on fast Fourier transform. We also demonstrate the practical relevance of the approach for SR with a reference image. Although limited to stationary microtextures, our approach compares favorably in terms of speed and visual quality to some state of the art methods designed for a larger class of images. © 2023 IEEE.

关键词： Kriging

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Robustly overfitting latents for flexible neural image compression 38

Robustly overfitting latents for flexible neural image compr...

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38th conference on Neural Information processing Systems, NeurIPS 2024

作者： Perugachi-Diaz, Yura Gansekoele, Arwin Bhulai, Sandjai Vrije Universiteit Amsterdam Netherlands Centrum Wiskunde & Informatica Netherlands

Neural image compression has made a great deal of progress. State-of-the-art models are based on variational autoencoders and are outperforming classical models. Neural compression models learn to encode an image into a quantized latent representation that can be efficiently sent to the decoder, which decodes the quantized latent into a reconstructed image. While these models have proven successful in practice, they lead to sub-optimal results due to imperfect optimization and limitations in the encoder and decoder capacity. Recent work shows how to use stochastic Gumbel annealing (SGA) to refine the latents of pre-trained neural image compression models. We extend this idea by introducing SGA+, which contains three different methods that build upon SGA. We show how our method improves the overall compression performance in terms of the R-D trade-off, compared to its predecessors. Additionally, we show how refinement of the latents with our best-performing method improves the compression performance on both the Tecnick and CLIC dataset. Our method is deployed for a pre-trained hyperprior and for a more flexible model. Further, we give a detailed analysis of our proposed methods and show that they are less sensitive to hyperparameter choices. Finally, we show how each method can be extended to three- instead of two-class rounding. © 2024 Neural information processing systems foundation. All rights reserved.

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Model of stochastic Traffic Flow through an Intersection with Representation of Cluster Interaction

Model of Stochastic Traffic Flow through an Intersection wit...

引用

2024 Systems of Signal Synchronization, Generating and processing in Telecommunications, SYNCHROINFO 2024

作者： Valuev, A.M. Mechanical Engineering Research Institute Named after A.A. Blagonravov of Ras Laboratory of Dynamics of Man-Machine Systems Moscow Russia

ISBN: (纸本)9798350373448

A method is being developed for computer simulation of traffic at a controlled intersection of urban roads, considered as a random process. The proposed model explicitly takes into account the spatio-temporal structure of traffic flow, systematically considering the interdependencies between vehicle passages through conflict points of crossing and merging as well as on exit roads. The statistical dependencies used in the model for the time interval between successive vehicles on the route and the difference in their speeds are determined. methods for obtaining such dependencies from monitoring data are considered. The proposed approach can be used to analyze the current passage of flows and to select a method for structural- parametric control of an intersection. The organization of calculations for solving such problems develops what was previously proposed and tested using the example of a T- junction. © 2024 IEEE.

关键词： stochastic systems

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image Segmentation and Pre-processing for Lung Cancer Detection in Humans Based on Deep-Learning

Image Segmentation and Pre-Processing for Lung Cancer Detect...

引用

2023 International conference on Circuit Power and Computing Technologies, ICCPCT 2023

作者： Singh, Drishti Singh, Jaspreet School of Engineering and Sciences GD Goenka University Gurugram India

ISBN: (纸本)9798350333244

When it comes to cancer and its linked disorders, lung cancer is consistently ranked among the top causes of mortality. The primary method for making the diagnosis is to do a scan analysis of the patient's lungs. It's possible that an MRI, CT scan, or X-ray will be analyzed in this scan analysis. Due to the wide variety of imaging techniques that can be used to a patient's lungs, one of the challenging jobs that must be completed is the automated classification of lung cancer. methods involving machine learning, deep learning and image processing have demonstrated a significant amount of promise for the classification and identification of lung cancer. In this research, we demonstrate a successful strategy for detecting and classifying malignant and benign lung cancer-related CT scan pictures. This approach was developed as part of our research for this paper. The proposed method begins by classifying the photos after they have been processed with image processing techniques. After that, the supervised learning algorithms are utilized to further process the images. In this section, we have extracted statistical features as well as textural characteristics, and then we have fed several extracted features to multiple classifiers. We have utilized a total of seven distinct classifiers, which are referred to as the KNN classifier, SVM classifier, multinomial naive Bayes classifier, decision tree, SGD(stochastic gradient descent), MLP (multi-layer perceptron) and random forest. When training and testing these classifiers, we employed a dataset that included both benign and malignant lung cancer-related images. In the findings that were collected, it was discovered that the accuracy is highest which is approx. 88 percent(for MLP classifier), when compared to other classifiers. © 2023 IEEE.

关键词： image segmentation

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Deep Learning Approaches for Signal and image processing: State-of-the-Art and Future Directions 7

Deep Learning Approaches for Signal and Image Processing: St...

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7th International conference on Contemporary Computing and Informatics, IC3I 2024

作者： Deepan, S. Gera, Jaideep Pareek, Anupam Chinchmalatpure, Sheela Nayak, Gouri Sankar Bodapati, Jagadeesh Sr University School of Computer Science and Artificial Intelligence Telangana Warangal India Guntur522212 India Poornima University Faculty of Management of Commerce Ramchandrapura P.O. Vidhani Vatika Sitapura Extension Rajasthan Jaipur303905 India Vishwakarma Institute of Technology Maharashtra Pune India Vignan's Institute Of Information Technology Duvvada Andhra Pradesh Visakhapatnam530049 India Rajamundry India

ISBN: (纸本)9798350350067

Deep learning has revolutionized signal and image processing by enabling the creation of complex algorithms with many applications. This study examines deep learning signal and image processing optimization and hardware acceleration strategies. Experimentally evaluating stochastic gradient descent (SGD) and Adam optimization algorithms determines their convergence speed and effectiveness. We also examine how GPUs may accelerate model execution and deep learning inference. Our study reveals that deep learning optimization methodologies and platforms provide several practical challenges and trade-offs. This work may help signal and image processing researchers and practitioners design scalable and efficient solutions. Additionally, their methods illuminate deep learning. © 2024 IEEE.

关键词： Deep learning

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