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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131-140 订阅

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Novel Retinex-type images Enhancement Method based on Sampling Representations 9

Novel Retinex-type Images Enhancement Method based on Sampli...

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9th IEEE International conference on Information Technology and Nanotechnology, ITNT 2023

作者： Antsiperov, Viacheslav Kotelnikov Institute of Radioengineering and Electronics Russian Academy of Sciences Moscow Russia

ISBN: (纸本)9798350397338

The work is devoted to the issues of statistical processing of images based on the modeling of some mechanisms of perception on the periphery of the human visual system (the retina). First of all, we mean the processes of coding (compression) of information, registered by receptors for its compact transmission through the visual channel, i.e. the optic nerve. So the synthesis of the proposed coding is based on the known mechanisms of neural layered processing of registered radiation. One of the features of such a coding is the enhancement of the illumination/reflectivity in the field of view. The main idea of such enhancement is formalized in the frames of the so-called Retinex model. Second, the classical approach to the coding synthesis is the Rate-Distortion theory. However, it has been established over the past two decades, that an optimal ratio of rate and distortion does not guarantee high perceptual image quality. It is because the coding also needs to have some predictive aspects. In this regard, the information bottleneck principle, proposed around 2000, proved to be very promising for solving the Rate-Distortion-Perception tradeoff. In this work we offer some insights for implementing this principal into Retinex-type images enhancement. Namely, we propose a generative model of an image encoder that provides an optimal compromise between the degree of data compression and the predictive (perceptual) quality of the code. The generative model of such an encoder opens up the way to the synthesis of image encoding methods based on the known principles of statistical (machine) learning, such as: multiresolution analysis, nonlinear (adaptive) filtering, neural networks, etc. For an adequate construction of a generative model of image / neural coding, a number of formalized descriptions are used in the paper. They are, firstly, the description of the recorded data through a special representation of images by a controlled size samples of counts (sampling represent

关键词： image enhancement

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Comprehensive Electric load forecasting using ensemble machine learning methods 29

Comprehensive Electric load forecasting using ensemble machi...

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29th International conference on Systems, Signals and image processing (IWSSIP)

作者： Bhatnagar, Mansi Dwivedi, Vivek Singh, Divyanshu Rozinaj, Gregor Slovak Univ Technol FEI Dept Multimedia & Telecommun Bratislava Slovakia Blue Bricks Pvt Ltd AI ML Developer Lucknow India

ISBN: (数字)9781665495783

ISBN: (纸本)9781665495783

The accuracy of electric load forecasting is crucial when working on applications in power grid decision-making and operation. Due to the non-linear and stochastic behaviour of customers, the electric load profile is a complicated signal. In this paper, authors propose machine learning based automated system for electricity load forecasting, taking into consideration various variable factors that have an impact on the future electricity load demand. Three machine learning algorithms are used for evaluation of the proposed framework. The algorithms are evaluated on electricity load data collected from eastern region of Ontario, integrated with the weather and population data of the region. The Light GBM algorithm comparatively performs best with mean absolute error of 0.156. The developed system can be used for more accurate and efficient load forecasting applications.

关键词： Time series Machine learning ensemble learning Electricity load prediction

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Optimizing image Quality and Diagnostic Accuracy in Impedance Tomography with Gastric Phantom Model 5

Optimizing Image Quality and Diagnostic Accuracy in Impedanc...

引用

5th International conference on Biomedical Engineering, IBIOMED 2024

作者： Wicaksono, Ridwan Chenaniah Rohman, Muhammad Fathur Nafisman, Prima Fahlesi, Habib Fabian Keriswasiat, Dheandy Universitas Gadjah Mada Dept. of Electrical Engineering and Information Technology Yogyakarta Indonesia Universitas Gadjah Mada Faculty of Medicine Public Health and Nursing Dept. of Medicine Yogyakarta Indonesia

ISBN: (纸本)9798350389265

The phantom model imaging system enables real-time data processing and visualization through Electrical Impedance Tomography (EIT). In gastric impedance tomography, a 16 of electrodes placed around the phantom measure electrical impedance, thereby monitoring the distribution and motility of gastric contents. image reconstruction aims to obtain an optimal range of image values to ensure superior image quality. The process involves reconstructing the mesh area for analysis and determining computational speed in image reconstruction, with a mesh value of 5,5× 10-5 pixels chosen as the basis for determining the resolution level in the hyperparameters lambVal (Λ) and p Val (p). The hyperparameter p is utilized to find the optimal value for the EIT image shape that approximates the boundary ground truth with the smallest pixel error, while Λ is optimized for detail and accuracy of the image scale factor. The diagnostic system's performance was evaluated in terms of accuracy, response time, and measurement reliability, using a stable testing frequency of 50,000. A frequency of 50,000 was selected due to the decrease and stabilization of the relative permittivity value, leading to an error of 0.36555. Validation of image reconstruction results is conducted using both qualitative and quantitative methods. The qualitative approach involves comparing the real image boundary with its convergence range, producing a convergence area within the ρ range of 0.36 to 0.48 and the Λ range of 0.63 to 0.84. The quantitative approach employs statistical techniques and numerical measurements for more objective validation which yields a convergence area with a p of 0.45 and a Λ of 0.7 at a frequency of 50,000 Hz. Additionally, real-time processing enhances the system's capability to monitor and adapt to rapid changes in the gastric environment, further improving diagnostic accuracy and the effectiveness of the imaging system. © 2024 IEEE.

关键词： Phantoms

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A Framework for Video Based Sign Language Interpretation Using Machine Learning and statistical methods 4

A Framework for Video Based Sign Language Interpretation Usi...

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4th International conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Swathi, D. Susmetha, K. Sreeji, S. Mangairkarasi, S. B.E Computer Science and Engineering with Data Science Sathyabama Institute of Science and Technology Semmancheri TamilNadu Chennai600119 India Sathyabama Institute of Science and Technology Semmancheri Department of Computer Science and Engineering TamilNadu Chennai6001109 India

ISBN: (纸本)9798331523923

The Sign Language Recognition System has been designed to capture video input, process it to detect hand gestures, and translate these gestures into readable text. The project consists of several key components and steps: Video processing: Using OpenCV, the system captures frames from the video input. MediaPipe processes these frames to detect and track hand landmarks in real time. OpenCV capabilities allow for efficient frame extraction and basic image processing tasks such as resizing and normalization. Hand Detection and Tracking: MediaPipe pre-trained models identify and track hand movements within the video frames. The accurate detection and tracking of the hand movements are critical for the subsequent recognition of the sign language gestures. Sign Language Recognition: The core system is the deep learning model, trained using the TensorFlow and Keras on a dataset of sign language gestures. The model learns to classify the detected hand movements into corresponding sign language characters or words. Convolutional Neural Networks (CNNs) are typically used for task due to their effectiveness in image recognition tasks. Text Display: Once the system recognizes the signs, it converts them into text and displays the output. This can be done through a console output or a graphical user interface (GUI) built with Tkinter. The GUI provides a user friendly experience, allowing users to see the translated text in real-time. © 2025 IEEE.

关键词： Convolutional neural networks

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Retinotopic image Encoding by Samples of Counts 10th

Retinotopic Image Encoding by Samples of Counts

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11th International conference on Pattern Recognition Applications and methods (ICPRAM)

作者： Antsiperov, Viacheslav Kershner, Vladislav RAS Kotelnikov Inst Radioengn & Elect Mokhovaya 11-7 Moscow 125009 Russia

ISBN: (纸本)9783031245374;9783031245381

The article is devoted to the synthesis of image encoding methods based on the images data themselves. The proposed approach is based on a previously developed special representation of images by samples of counts (sampling representations). Since the sampling representations are essentially random constructions, the synthesis of encoding methods is carried out strictly within the framework of the generative paradigm. In essence, the approach proposed treats the image coding within generative model as a special case of the classical statistical problem of probability distribution density estimation. In the paper we restrict ourselves to the class of parametric estimation procedures, which imply some parametric family of probability distributions. Namely, we propose to use the model of a parametric mixture of simple distribution components. Accordingly, a set of component weights estimates calculated from a sampling representation, considering as input data, is interpreted as an encoded image - output data. In this context, optimal coding is synthesized with the maximum likelihood method. For the algorithmic implementation of the coding procedure the mixture model is equipped with the structure of receptive fields, that is a well-known organizing principal for receptors in the human eye retina. On this basis, we synthesize a relatively simple recurrent coding algorithm, which turned out to be close to the popular in machine learning EM algorithm. The paper presents interpretation of several features of the algorithm from the point of view of well-known facts about the image processing in the periphery of the visual system, discusses options for the algorithm implementation, and presents the results of numerical simulation of its operation.

关键词： Perceptual image coding Generative models Machine learning

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New Indicators and Standards for Measuring of the End Mill's Helical Groove by image processing 10

New Indicators and Standards for Measuring of the End Mill's...

引用

conference on Optical Metrology and Inspection for Industrial Applications X

作者： Pivkin, Petr M. Ershov, Artem A. Grechishnikov, Vladimir A. Kuznetsov, Vladimir A. Nazarenko, Ekaterina S. Malysheva, Elena Yu. Nadykto, Alexey B. Moscow State Univ Technol STANKIN Lab Micromachining Technol Moscow 127055 Russia Moscow State Univ Technol STANKIN Dept Cutting Tools & Machining Technol Moscow 127055 Russia

ISBN: (纸本)9781510667877;9781510667884

The studies will be carried out using optical metrology methods on a Walter Helicheck inspection machine in reflected light and a number of images were stored to form a statistical sample. Established new indicators and criteria for grinding efficiency based on image processing of the helical groove of the end mill. As a result, recommendations for the selection of optical control techniques were made for the first time at the intermediate stage of technological preparation for production, in real time, and after processing. In this work, for the first time, we prove the possibility of determining the camera displacement pith distance during continuous scanning of the profile of a helical surface in a radial section, the measurement accuracy and recreating a three-dimensional model of the object. As a result of the work of the new algorithm using the Haar-wavelet with new indicators, it was established that the actual one is located inside the focal zone, which proves the possibility of applied application of the method of monitoring the shape of helical flute of end mills using computer vision. The measurement accuracy of the helical flute increased from 4 to 12% along its profile.

关键词： image processing standards for measuring cutting tools optical metrology new indicators helical groove Haar-like feature

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PLUG-AND-PLAY image RECONSTRUCTION MEETS stochastic VARIANCE-REDUCED GRADIENT methods

PLUG-AND-PLAY IMAGE RECONSTRUCTION MEETS STOCHASTIC VARIANCE...

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IEEE International conference on image processing (ICIP)

作者： Monardo, Vincent Iyer, Abhiram Donegan, Sean De Graef, Marc Chi, Yuejie Carnegie Mellon Univ Dept Elect & Comp Engn Pittsburgh PA 15213 USA Air Force Res Lab Mat & Mfg Directorate Wright Patterson AFB OH USA Carnegie Mellon Univ Dept Mat Sci & Engn Pittsburgh PA 15213 USA

ISBN: (纸本)9781665441155

Plug-and-play (PnP) methods have recently emerged as a powerful framework for image reconstruction that can flexibly combine different physics-based observation models with data-driven image priors in the form of denoisers, and achieve state-of-the-art image reconstruction quality in many applications. In this paper, we aim to further improve the computational efficacy of PnP methods by designing a new algorithm that makes use of stochastic variance-reduced gradients (SVRG), a nascent idea to accelerate runtime in stochastic optimization. Compared with existing PnP methods using batch gradients or stochastic gradients, the new algorithm, called PnP-SVRG, achieves comparable or better accuracy of image reconstruction at a much faster computational speed. Extensive numerical experiments are provided to demonstrate the benefits of the proposed algorithm through the application of compressive imaging using partial Fourier measurements in conjunction with a wide variety of popular image denoisers.

关键词： plug-and-play stochastic optimization variance reduction image reconstruction

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Diffusion Posterior Sampling for Nonlinear CT Reconstruction

Diffusion Posterior Sampling for Nonlinear CT Reconstruction

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conference on Medical Imaging - Physics of Medical Imaging

作者： Li, Shudong Tivnan, Matthew Stayman, J. Webster Johns Hopkins Univ Biomed Engn Baltimore MD 21218 USA Harvard Med Sch Radiol Boston MA USA Massachusetts Gen Hosp Boston MA USA

ISBN: (纸本)9781510671553;9781510671546

Diffusion models have been demonstrated as powerful deep learning tools for image generation in CT reconstruction and restoration. Recently, diffusion posterior sampling, where a score-based diffusion prior is combined with a likelihood model, has been used to produce high quality CT images given low-quality measurements. This technique is attractive since it permits a one-time, unsupervised training of a CT prior;which can then be incorporated with an arbitrary data model. However, current methods only rely on a linear model of x-ray CT physics to reconstruct or restore images. While it is common to linearize the transmission tomography reconstruction problem, this is an approximation to the true and inherently nonlinear forward model. We propose a new method that solves the inverse problem of nonlinear CT image reconstruction via diffusion posterior sampling. We implement a traditional unconditional diffusion model by training a prior score function estimator, and apply Bayes rule to combine this prior with a measurement likelihood score function derived from the nonlinear physical model to arrive at a posterior score function that can be used to sample the reverse-time diffusion process. This plug-and-play method allows incorporation of a diffusion-based prior with generalized nonlinear CT image reconstruction into multiple CT system designs with different forward models, without the need for any additional training. We demonstrate the technique in both fully sampled low dose data and sparse-view geometries using a single unsupervised training of the prior.

关键词： Diffusion Double positive medium Education and training CT reconstruction Data modeling Reverse modeling Computed tomography statistical modeling stochastic processes Systems modeling

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Texture image Retrieval Based on statistical Modeling of All Subbands in the Complex Transform Domain 13

Texture Image Retrieval Based on Statistical Modeling of All...

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13th International conference on Graphics and image processing (ICGIP)

作者： Qu, Huaijing Xu, Jia Wang, Hengbin Wang, Jiwei Wei, Yanan Zhang, Zhisheng Shandong Jianzhu Univ Sch Informat & Elect Engn Jinan 250101 Peoples R China

ISBN: (数字)9781510650435

ISBN: (纸本)9781510650435;9781510650428

Aiming at the problem of imperfect utilization of subband statistical information in multi-scale texture image retrieval, a texture image retrieval method based on statistical modeling of all subband coefficients in the dual-tree complex wavelet transform domain is proposed. Firstly, the low-frequency subband coefficients, the high- frequency subband amplitude coefficients, and the high-frequency subband relative phase coefficients are modeled as mixed Gaussian distribution, Weibull distribution, and wrapped Cauchy distribution, respectively. Then, the model parameters are estimated by the expectation-maximization algorithm and the maximum likelihood method respectively, and the closed-form Cauchy-Schwarz divergence and Kullback-Leibler divergence are used as similarity measures for retrieval. Finally, the simulation experiment was carried out in two standard texture image libraries of Vistex and Brodatz. Experimental results show that the method proposed in this paper has better retrieval performance than existing methods.

关键词： Texture image retrieval dual-tree complex wavelet transform statistical modeling parameter estimation similarity measurement

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Face Super-Resolution Using stochastic Differential Equations 35

Face Super-Resolution Using Stochastic Differential Equation...

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35th SIBGRAPI conference on Graphics, Patterns and images (SIBGRAPI)

作者： dos Santos, Marcelo Laroca, Rayson Ribeiro, Rafael O. Neves, Joao Proenca, Hugo Menotti, David Univ Fed Parana Dept Informat Curitiba Brazil Brazilian Fed Police Natl Inst Criminalist Brasilia Brazil Univ Beira Interior Inst Telecomunicacoes Covilha Portugal

ISBN: (数字)9781665453851

ISBN: (纸本)9781665453851

Diffusion models have proven effective for various applications such as images, audio and graph generation. Other important applications are image super-resolution and the solution of inverse problems. More recently, some works have used stochastic differential equations (SDEs) to generalize diffusion models to continuous time. In this work, we introduce SDEs to generate super-resolution face images. To the best of our knowledge, this is the first time SDEs have been used for such an application. The proposed method provides an improved peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and consistency than the existing super-resolution methods based on diffusion models. In particular, we also assess the potential application of this method for the face recognition task. A generic facial feature extractor is used to compare the super-resolution images with the ground truth, and superior results were obtained compared with other methods. Our code is publicly available at https://***/marcelowds/sr-sde.

关键词： PSNR Inverse problems Face recognition Superresolution stochastic processes Differential equations Particle measurements

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