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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571-580 订阅

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JOINT statistical AND SPATIAL SPARSE REPRESENTATION FOR ROBUST image AND image-SET CLASSIFICATION

JOINT STATISTICAL AND SPATIAL SPARSE REPRESENTATION FOR ROBU...

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IEEE International conference on image processing (ICIP)

作者： Cheng, Hao Wen, Bihan Nanyang Technol Univ Sch Elect & Elect Engn Singapore Singapore

ISBN: (纸本)9781728163956

Recent image classification schemes, by learning deep features from large-scale dataset, have achieved the significantly better results comparing to classic feature-based approaches. However, there are still challenges in practice, such as classifying noisy image-set queries and training over limited-scale dataset. Instead of applying generic deep features, the model-based approaches can be more effective for robust image and image-set classification tasks, as we need various image priors to exploit the inter- and intra-set data variations while prevent over-fitting. In this work, we propose a novel joint statistical and spatial sparse representation, dubbed J3S, to model the image or image-set data, by exploiting both their local patch structures and global Gaussian distribution into Riemannian manifold. To the best of our knowledge, no work to date utilized both global statistics and local patch structures jointly via sparse representation. We propose to solve a co-regularized sparse coding problem based on the J3S model, by coupling the local and global representations using joint sparsity. The learned J3S models are used for robust image and image-set classification. Experiments show that the proposed J3S-based image classification scheme out-performs the popular or state-of-the-art competing methods.

关键词： Sparse representation Gaussian model Riemannian manifold Dictionary learning Classification

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Possible Ways of Video processing at the Quantization Stage 4

Possible Ways of Video Processing at the Quantization Stage

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4th IEEE International conference on Advanced Information and Communication Technologies, AICT 2021

作者： Havrylov, Dmytro Valerii, Yeroshenko Onyshchenko, Roman Zakomorna, Kateryna Maksym, Savchuk Oleksandr, Karaban Kharkiv National University of Radio Electronics Kharkiv Ukraine Ivan Kozhedub Kharkiv National Air Force University Kharkiv Ukraine Yaroslav Mudryi National Law University Kharkiv Ukraine Heroes of Kruty Military Institute of Telecommunications and Informatization Kyiv Ukraine

ISBN: (纸本)9781665406185

The aim of the article is to build a method of video processing with the possibility of its protection at the stage of quantization with subsequent arithmetic coding. That will allow at preservation of structural - statistical regularity to provide a necessary level of availability, reliability and confidentiality at video data transmission. Providing the required level of availability involves reducing the video volume by 30% compared to the original volume. In this case, the provision of the required level of reliability is confirmed by estimating the peak signal-to-noise ratio for the authorized user, which is PSNRaBT 20 dB. Ensuring the required level of confidentiality is confirmed by estimating the peak signal-to-noise ratio for unauthorized access, which is PSNRHC 9 dB. The scientific novelty of the obtained results lies in the following: for the first time two methods of video image processing at the quantization stage are proposed. The method of using encryption tables has greater cryptographic stability, and the method of using the key matrix has greater speed. At the same time, the use of arithmetic coding will satisfy the need for availability by reducing the initial volume. © 2021 IEEE.

关键词： Signal to noise ratio

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Improving the Efficiency of the Method of stochastic Gradient Identification of Objects in Binary and Grayscale images Using Their Preprocessing 22

Improving the Efficiency of the Method of Stochastic Gradien...

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22nd International conference on Digital Signal processing and its Applications, DSPA 2020

作者： Gilfanovich, Magdeev Radik Grigorevich, Tashlinskii Alexander Ulyanovsk State Technical University Radio Engineering Department Ulyanovsk Russia

ISBN: (纸本)9781728175287

In this paper, we consider ways to improve the stochastic gradient method efficiency of object identification for binary and grayscale images using methods of image preprocessing. Identification of an object is understood as the recognition of an object on the image with its parameters estimation. Low-pass filtering and image equalization are considered as preliminary processing. The identification parameters convergence rate is investigated. The optimal sizes of Gaussian filter mask for binary and grayscale images were found based on COIL-20 images. © 2020 IEEE.

关键词： Object recognition

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SALIENCY-BASED image CONTRAST ENHANCEMENT WITH REVERSIBLE DATA HIDING

SALIENCY-BASED IMAGE CONTRAST ENHANCEMENT WITH REVERSIBLE DA...

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

作者： Yang, Shilong South China Univ Technol Sch Comp Sci & Engn Guangzhou Peoples R China

ISBN: (纸本)9781509066315

Reversible data hiding (RDH) has become a hot research area in the recent years due to its wide applications such as authentication. Among all the RDH methods proposed, contrast enhancement based reversible data hiding is one that was recently proposed. However, most of the existing schemes proposed focus on image itself without taking human visual system (HVS) into account. Consequently, the full range of the redundancy from HVS cannot be fully exploited, and the resulting image may be visually unpleasing. In this paper, a new approach is proposed by introducing the visual saliency into the process of reversible data hiding. Specifically, a saliency fixation map is generated by using an existing saliency prediction model, followed by a saliency guided segmentation to better enhance the salient regions in the input image. The evaluation results show that the proposed method yields better visual results and outperforms the existing methods in statistical metrics.

关键词： Reversible data hiding Visual saliency Contrast enhancement Human visual system image quality

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OPTIMAL MEASUREMENT BUDGET ALLOCATION FOR PARTICLE FILTERING

OPTIMAL MEASUREMENT BUDGET ALLOCATION FOR PARTICLE FILTERING

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IEEE International conference on image processing (ICIP)

作者： Aspeel, Antoine Gouverneur, Amaury Jungers, Raphael M. Macq, Benoit UCLouvain ICTEAM Inst Ave Georges Lemaitre 4-6 Louvain La Neuve Belgium

ISBN: (纸本)9781728163956

Particle filtering is a powerful tool for target tracking. When the budget for observations is restricted, it is necessary to reduce the measurements to a limited amount of samples carefully selected. A discrete stochastic nonlinear dynamical system is studied over a finite time horizon. The problem of selecting the optimal measurement times for particle filtering is formalized as a combinatorial optimization problem. We propose an approximated solution based on the nesting of a genetic algorithm, a Monte Carlo algorithm and a particle filter. Firstly, an example demonstrates that the genetic algorithm outperforms a random trial optimization. Then, the interest of non-regular measurements versus measurements performed at regular time intervals is illustrated and the efficiency of our proposed solution is quantified: better filtering performances are obtained in 87.5% of the cases and on average, the relative improvement is 27.7%.

关键词： Optimal measurement times Particle filtering Sequential Monte Carlo methods Sparse measurements Genetic algorithm

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statistical texture analysis of forest areas from very high spatial resolution satellite images

Statistical texture analysis of forest areas from very high ...

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2021 All-Russian conference with International Participation "Spatial Data processing for Monitoring of Natural and Anthropogenic Processes", SDM 2021

作者： Dmitriev, Egor V. Kondranin, Timofei V. Melnik, Petr G. Donskoy, Sergey A. Marchuk Institute of Numerical Mathematics of the Russian Academy of Sciences Moscow Russia Moscow Region Dolgoprudny Russia Mytischi Branch of Bauman Moscow State Technical University Moscow Region Mytischi Russia Federal Forestry Agency ROSLESINFORG Moscow Russia

Aerospace images with a spatial resolution of less than 1 m are actively used by regional services to obtain and update information about various environmental objects. Considerable efforts are being devoted to the development of remote sensing methods for forest areas. The structure of the forest canopy depends on various parameters, most of which are determined by ground-based methods during forest management works. Remote sensing methods for assessing the structural parameters of forest stands are based on texture analysis of panchromatic and multispectral images. A statistical approach is often used to extract texture features. The basis of this approach is the description of the distributions characterizing the mutual arrangement of image pixels in grayscale. This paper compares the effectiveness of matrix based statistical methods for extracting textural features for solving the problem of classifying various natural and manmade objects, as well as structures of the forest canopy. We consider statistics of various orders based on estimates of the distributions of gray levels, as well as the mutual occurrence, frequency, difference and structuring of gray levels. The results of assessing the informativeness of statistical textural characteristics in determining various structures of the forest canopy are presented. Dependences of the classification results on the choice of distribution parameters are determined. For the quantitative validation of the results obtained, data from ground surveys and expert visual classification of very high resolution WorldView-2 images of the territories of Savvatyevkoe and Bronnitskoe forestries are used. © 2021 Copyright for this paper by its authors.

关键词： Pattern recognition Remote sensing Soil-vegetation cover Texture analysis Very high resolution images

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image Salt and Pepper Noise Adaptive Based on Fuzzy Median Filtering 12

Image Salt and Pepper Noise Adaptive Based on Fuzzy Median F...

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12th International conference on Digital image processing (ICDIP)

作者： Qu, Jingkun Xu, Jinxiang Beijing Inst Technol Sch Mechatron Engn Beijing 100081 Peoples R China

ISBN: (纸本)9781510638464

The advantage of the adaptive filter is that it can automatically adjust the parameters in the filtering process when the statistical characteristics of the input signal are unknown. Therefore, it can achieve better results when applied to image processing than traditional filtering methods. For pepper and salt noise, the algorithm in this paper is based on judging the difference of image gray level and using the fuzzy function to process the image. Through the comparison of normalized mean square error (NMSE) and peak signal to noise ratio (PSNR), the advantages of the proposed algorithm over the traditional adaptive filtering algorithm in preserving image details are verified.

关键词： image processing Adaptive filtering Salt and pepper noise fuzzy theory

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Measurements of Clothing Circumference Using Vision methods 43rd

Measurements of Clothing Circumference Using Vision Methods

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43rd IBIMA conference on Artificial intelligence and Machine Learning, IBIMA-AI 2024

作者： Kohut, Piotr Miekina, Lucjan Manka, Michal Buratowski, Tomasz Klorek, Tomasz Bartkowiak, Bartosz Department of Robotics and Mechatronics AGH University of Krakow Krakow Poland VISBROKER Sp. z o.o. Poznan Poland

ISBN: (纸本)9783031774928

Vision methods allow for accelerating the measurements of critical parameters of the figure and clothing and making the results free of errors usually made during manual measurements. However, it is necessary to develop appropriate algorithms for estimating spatial parameters, such as circumferences in selected cross-sections, based on 2-dimensional images. The literature is dominated by reports on the use of an ellipse for estimating circumferences, methods involving the concatenation of two ellipses are also described, and examples of the use of a superellipse are discussed but without an in-depth analysis of the selection of its parameters and the results achieved. This article discusses these essential properties of the superellipse as a model of the figure/clothing circumference. The article presents the developed methods of image processing and analysis for measuring circumferences and selected clothing measurement parameters. The paper discusses classic circuit approximation methods using ellipse equations and the proposed superellipse (Lame) method. A vision-based algorithm was presented, which uses spatial markers to measure circumferences and selected clothing parameters. The proposed method was verified on a test stand during experiments and compared with manual measurements. The results of measurements of the circumference, length, and width of selected clothing items were obtained. statistical estimates characterizing the developed vision measurement system were determined based on the results. The obtained findings indicate that, compared to the classical ellipse approach, in the proposed method to measure the circumference, based on Lame curves, the average absolute error does not exceed 0.55 mm. The average measurement accuracy of the developed vision system for all conducted measurements was below the assumed value of 5 mm. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Anthropometry

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Experimental Study on Assistive System Using statistical Signal processing Technology for Visually Impaired People 10th

Experimental Study on Assistive System Using Statistical Sig...

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10th International conference on Health Informatics and Medical Systems, HIMS 2024 and 10th International conference on Biomedical Engineering, BIOENG 2024, held as part of the World Congress in Computer Science, Computer Engineering and Applied Computing, CSCE 2024

作者： Shimizu, Shunji Hori, Masaya Inoue, Hiroaki Miwakeichi, Fumikazu Hirai, Nobuhide Kikuchi, Senichiro Kato, Satoshi Department of Applied Information Engineering Suwa University of Science Chino Japan Department of Engineering and Management Suwa University of Science Chino Japan Shimizu Research Laboratory Suwa University of Science Chino Japan Department of Interdisciplinary Statistical Mathematics The Institute of Statistical Mathematics Tachikawa Japan Health Administration Center Tokyo Medical and Dental University Bunkyo Japan Department of Rehabilitation Sciences Gunma University Maebashi Japan Oyama Fujimidai Hospital Shimotsuke Japan

ISBN: (纸本)9783031859076

In this study, we will investigate a new image signal processing method in order to develop a highly useful walking support device for visually impaired people. Visually impaired people walk independently using assistive devices. Currently, there are many support devices that use object detection and object recognition using deep learning, but there are still no highly useful devices. The smallest unit constituting an image signal is a pixel. Adjacent pixels are correlated with each other. The positional relationship of pixels expresses the features of objects, and scenery in the image. Autoregressive (AR) model is a regression analysis method used to analyze time-series signals. AR models are one of the basic methods for signal processing for speech recognition and control. Furthermore, in image signal processing, it can be used as a signal processing method when data has some kind of order, such as texture feature extraction, contour processing, and video processing. In this experiment, we converted the pixel array of the image signal into a one-dimensional time-series signal and process the signal. We tried to identify objects or regions within images. The method proposed in this study may be used to extract "Point of Interest" (POI) and "Point of Reference" (POR), which are important information for visually impaired people. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： image texture

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Characterisation of Landuse / Landcover Changes and its Comparison in Vijayawada City using Artificial Neural Networks with Minimum Distance to Mean

Characterisation of Landuse / Landcover Changes and its Comp...

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Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF), International conference on

作者： K.Pavan Venkat Vidhya Lakshmi Sivakumar Department of Civil Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India

Aim: The main objective of this paper is to find the change detection of a Vijayawada city, Andhra Pradesh. Digital image processing is done by performing image classification, the regions separated into 6 different major classes based on its use and by the cover it is found for a period of 20 years from 2001 to 2020 and comparing the algorithms Artificial Neural Networks (ANN) with Minimum Distance to Mean (MDM) classification to predict which gives higher accuracy. Materials and methods: The images of the city are collected from the satellites landsat 7ETM+ (Enhanced Thematic Mapper plus) &landsat 8 for novel supervised classification from the United States Geological Survey (USGS) earth explorer. Those images are preprocessed for image classification and used for novel supervised classification with a total sample size of N= 6 with 3 samples from 2 groups with different years. The pretest power is to be determined with 80% with an alpha value of 0.05 and CI of 95%. Results and discussion: Based on the results from the SPSS (statistical analysis software) we got p=0.04 for overall accuracy which is p<0.05 for single tailed test, it is evident that there is significant difference between the two groups by the independent sample t test. The novel supervised classification is done for the samples and we have achieved the percentage of land cover, those are taken and we also got key outputs as Overall accuracy and kappa coefficient. We have achieved 97.1067±1.61593 as mean and SD for ANN, 88.8733±6.68356 as mean and SD for MDM for overall accuracy. The kappa coefficients are as 0.9399±0.0550 as mean and SD for ANN, 0.7757±0.1447 as mean and SD for MDM. Conclusion: According to the current research findings, even though there is no significant difference between groups we can say that Artificial Neural Networks provide more accurate classified output and we can say that ANN is better among those two algorithms.

关键词： Earth Artificial satellites Change detection algorithms statistical analysis Urban areas Artificial neural networks Software

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