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

共 4729 条记录，以下是931-940 订阅

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Distributed Algorithm for Principal Component Analysis Based on Power Method and Average Consensus Algorithm

Distributed Algorithm for Principal Component Analysis Based...

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Symposium of image, Signal processing, and Artificial Vision (STSIVA)

作者： Norikazu Takahashi Mutsuki Oura Tsuyoshi Migita Graduate School of Natural Science and Technology Okayama University Okayama Japan

ISBN: (数字)9781728170862

ISBN: (纸本)9781728170879

Principal component analysis is one of the most important methods of multivariate analysis, and has been applied in a wide range of fields such as statistical analysis, machine learning, pattern recognition, signal processing, and communication. Recently, using the idea of multi-agent networks, distributed algorithms for principal component analysis have been proposed for the case where the data matrix is partitioned either row-wise or column-wise. In this paper, considering the case where the data matrix is partitioned both row-wise and column-wise, we propose a new algorithm that allows a multi-agent network to perform principal component analysis in a distributed manner. We also verify its validity by numerical experiments. The proposed algorithm is based on the power method for principal component analysis and the average consensus algorithm.

关键词： Signal processing algorithms Distributed databases Consensus algorithm Signal processing Partitioning algorithms Distributed algorithms Principal component analysis

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A Corrosion Detection Algorithm Via The Random Forest Model 17

A Corrosion Detection Algorithm Via The Random Forest Model

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17th International conference on Optical Communications and Networks (ICOCN)

作者： Liu Tingting Kang Kai Zhang Fen Ni Jialiang Wang Tianyun Dept China Satellite Maritime Tracking & Control Jiang Yin 214431 Peoples R China

ISBN: (数字)9781510627710

ISBN: (纸本)9781510627710

Corrosion is a serious issue causing damage in steel facilities. Timely inspection and repair is essential to avoid unprecedented structural failures. Employing non-destructive methods of manual inspection for large number of antennas to detect corrosion and related damages is time consuming and expensive. In addition to this, safety of inspector to climb structures possibly weakened by corrosion. In such a situation non-contact approach of automated visual inspection for corrosion and related damage detection through image processing of aerial based images is a viable option. For robust corrosion segmentation and detection, we investigate color classification based on random forest. A random forest is a statistical framework with a very high generalization accuracy and quick training times. We evaluate random forest based corrosion detection and compare it to Bayesian network, Multilayer Perceptron, SVM, Naive Bayes and RBF network. Results on a database of real images with manually annotated pixel-level ground truth show that with the IHLS colour space, the random forest approach outperforms other approaches.

关键词： corrosion detection image segmentation random forest antenna corrosion

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High resolution SAR imagery with fully flexible prior

High resolution SAR imagery with fully flexible prior

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IET International Radar conference (IET IRC 2020)

作者： Y. Xia X. Mao X. Liao C. Fang L. Yang Civil Aviation University of China Tianjin Key Laboratory for Advanced Signal Processing Tianjin China

Followed by the development of high-resolution Synthetic Aperture Radar (SAR) imagery, the statistical characteristics of the scenes or targets of interests become highly complicated and dynamically varying. It is therefore difficult to extract the elaborated features for further classification and recognition. In this paper, a fully flexible prior, named by generalized Gaussian distribution (GGD), is introduced for the feature enhancement of SAR imagery in Bayesian inference manner. Due to the flexibility of the prior model, detailed structure of the scenes or targets can be resolved in the Bayesian inference way. To accommodate for the non-conjugacy of the prior, the Bayesian inference is developed in a hierarchical manner, which guarantees closed-form solutions of the features. Considering the difficulties in solving the posterior of the target image, the variable splitting scheme of Alternating Direction Method of Multipliers (ADMM) is adopted, which is capable of separating the joint optimization into multiple sub-problems. This can simplify the computational complexity in a great extent. Finally, a proximity modified Markov Chain Monte Carlo (MCMC) sampling method is applied to solve the posterior, which ensures the computational efficiency with a satisfying level. Raw SAR data of stationary scene and moving target are used for the verification, and evaluations of Phase Transition Diagram (PTD) analysis is performed to show the superiority of the proposed algorithm.

关键词： Bayes methods Monte Carlo methods Gaussian distribution radar imaging synthetic aperture radar Markov processes sampling methods convex programming feature extraction image classification

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Learning 2D morphological network for old document image binarization 15

Learning 2D morphological network for old document image bin...

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15th IAPR International conference on Document Analysis and Recognition, ICDAR 2019

作者： Mondal, Ranjan Chakraborty, Deepayan Chanda, Bhabatosh Indian Statistical Institute Kolkata India

ISBN: (纸本)9781728128610

Document image binarization, especially old handwritten documents, is a very important yet challenging task. There are various bottlenecks for binarizing historical documents due to different types of degradation present imultaneously such as back impression, ink bleed through, faded colours, and wear and tear of the writing media. We consider these degradation as various types of noise in the document image. Here we have proposed a 2D morphological network which consists of basic morphological operation like dilation and erosion to perform our targeted task. The network also includes linear combination of output from dilation and erosion operations. The aforementioned 2D morphological network is applied for image binarization, where the structuring elements (SEs) and the weights of the linear combination layer are learned through back-propagation. The proposed network has been evaluated on DIBCO 2017 and H-DIBCO 2018 and ISI-Letter dataset. Our results show more convincing as compared to the results of other state-of-the-art methods. Though the network is developed for old handwritten documents, it may be tuned to work for image processing task. The source code can be found here https://***/ranjanZ/ICDAR-Binarization. © 2019 IEEE.

关键词： Backpropagation

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Model-based iterative reconstruction for spectral-domain optical coherence tomography 26

Model-based iterative reconstruction for spectral-domain opt...

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conference on Three-Dimensional and Multidimensional Microscopy - image Acquisition and processing XXVI

作者： Mason, Jonathan H. Reinwald, Yvonne Yang, Ying Waters, Sarah El Haj, Alicia Bagnaninchi, Pierre O. Univ Edinburgh MRC Ctr Regenerat Med 5 Little France Dr Edinburgh Midlothian Scotland Nottingham Trent Univ Coll Sci & Technol Dept Engn Nottingham England Keele Univ Inst Sci & Technol Med Keele Staffs England Univ Oxford Math Inst Oxford England Univ Birmingham Healthcare Technol Inst Birmingham W Midlands England

ISBN: (纸本)9781510624092

Spectral domain optical coherence tomography (OCT) offers high resolution multidimensional imaging, but generally suffers from defocussing, intensity falloff and shot noise, causing artifacts and image degradation along the imaging depth. In this work, we develop an iterative statistical reconstruction technique, based upon the interferometric synthetic aperture microscopy (ISAM) model with additive noise, to actively compensate for these effects. For the ISAM re-sampling, we use a non uniform FFT with Kaiser-Bessel interpolation, offering efficiency and high accuracy. We then employ an accelerated gradient descent based algorithm, to minimize the negative log-likelihood of the model, and include spatial or wavelet sparsity based penalty functions, to provide appropriate regularization for given image structures. We evaluate our approach with titanium oxide micro-bead and cucumber samples with a commercial spectral domain OCT system, under various subsampling regimes, and demonstrate superior image quality over traditional reconstruction and ISAM methods.

关键词： OCT ISAM demonstrate ISAM optical coherence tomography Demonstrate Spectral domain image quality image degradation

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18th International conference on Computer Analysis of images and Patterns, CAIP 2019

18th International Conference on Computer Analysis of Images...

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18th International conference on Computer Analysis of images and Patterns, CAIP 2019

ISBN: (纸本)9783030298906

The proceedings contain 106 papers. The special focus in this conference is on Computer Analysis of images and Patterns. The topics include: DeepNautilus: A Deep Learning Based System for Nautical Engines’ Live Vibration processing;binary Code for the Compact Palmprint Representation Using Texture Features;handwriting Analysis to Support Alzheimer’s Disease Diagnosis: A Preliminary Study;geometrical and statistical Properties of the Rational Order Pyramid Transform and Dilation Filtering;Personal Identity Verification by EEG-Based Network Representation on a Portable Device;a System for Controlling How Carefully Surgeons Are Cleaning Their Hands;class-Conditional Data Augmentation Applied to image Classification;Fabric Classification and Matching Using CNN and Siamese Network for E-commerce;real-Time Style Transfer with Strength Control;analysis of Skill Improvement Process Based on Movement of Gaze and Hand in Assembly Task;faster Visual-Based Localization with Mobile-PoseNet;unsupervised Effectiveness Estimation Through Intersection of Ranking References;Joint Correlation Measurements for PRNU-Based Source Identification;detecting Sub-image Replicas: Retrieval and Localization of Zoomed-In images;homogeneity Index as Stopping Criterion for Anisotropic Diffusion Filter;adaptive image Binarization Based on Multi-layered Stack of Regions;Evaluating Impacts of Motion Correction on Deep Learning Approaches for Breast DCE-MRI Segmentation and Classification;a Two-Step System Based on Deep Transfer Learning for Writer Identification in Medieval Books;restoration of Colour images Using Backward stochastic Differential Equations with Reflection;sound Transformation: Applying image Neural Style Transfer Networks to Audio Spectograms;hybrid Function Sparse Representation Towards image Super Resolution;timber Tracing with Multimodal Encoder-Decoder Networks;a Challenging Voice Dataset for Robotic Applications in Noisy Environments.

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Automatic Detection of Transmission Line on UAV Inspection images with the Statistics Approach in the DCT Domain

Automatic Detection of Transmission Line on UAV Inspection I...

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Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD), International conference on

作者： Min Zhang Abubakar Abubakar Khalid Yifan Li Yu Chen School of Mathmatics Northwest University Xi’an China School of Electrical Engineering Xi’an Jiaotong University Xi’an China

ISBN: (数字)9781728192772

ISBN: (纸本)9781728192789

Power transmission lines are significant aspect for uninterrupted electric energy supply. The aerial camera captured image processing methods use for inspecting the condition of this infrastructure involve algorithms that explicitly find line segments in the image and classify them according to width, length and angle. These methods are erroneous with extreme false detection outcome rate. It's obvious that image may contain a complex background with a line like structures. For this purpose, an alternative approach that visualizes the statistical model of local DCT coefficients is proposed to estimate the existence of power line. The proposed algorithm tackles the issue of power transmission line detection by exploiting low-level images characteristic to mimic human visual acuity and detect the existence of power line by mapping the extracted coefficient to Generalized Gaussian Distributions (GGDs) from the DCT coefficients. Then percentile pooling is applied on the fitted coefficient to obtain values representing the local and global distribution. Emphasis is laid on the orientation feature to capture the transmission line varying position and location in separate images. These features are feed as input to SVM for classification. The proposed algorithm was tested on the TEIAS visible light database containing 4000 undistorted images, and a local dataset containing 1039 distorted and undistorted images. Experimental results show the proposed method can detect power lines with reasonable performance.

关键词： Discrete cosine transforms Support vector machines Training Power cables Feature extraction Distortion image color analysis

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Hierarchical Sparse Reconstruction Based Multi-feature Saliency for Target Detection in SAR images 6

Hierarchical Sparse Reconstruction Based Multi-feature Salie...

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6th Asia-Pacific conference on Synthetic Aperture Radar, APSAR 2019

作者： Li, Lu Du, Lan Wang, Zhaocheng National Laboratory of Radar Signal Processing Xidian University Xi'an China

ISBN: (纸本)9781728129129

The utilization of clutter information is useful for target detection. However, the clutter statistical modeling, used in conventional constant false alarm rate (CFAR) methods, is usually difficult for a real synthetic aperture radar (SAR) image. And though intensity feature is basic for target detection, only using intensity feature is insufficient considering the speckle noise. To address these problems, we propose a novel hierarchical sparse reconstruction based multi-feature saliency method to detect targets in SAR images. The proposed method hierarchically generates saliency maps using sparse reconstruction based on clutter templates on instance feature and structural feature separately. The clutter templates sampled from the pure clutter SAR images contains the clutter information. Though the sparse reconstruction based on clutter templates, we can fully utilize clutter information without clutter statistical modeling. Moreover, the proposed method jointly uses the instance feature and the structure feature, making the saliency detection more robust to speckle noise. We evaluate the proposed method on the miniSAR real data. The experimental results demonstrate that the proposed method outperforms conventional SAR target detection methods in terms of the precision, recall and F1-score. © 2019 IEEE.

关键词： Synthetic aperture radar

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An adaptive threshold for change detection methods using a windowed entropy-based criterion - Application to fault-tolerant fusion in collaborative mobile robotics 4

An adaptive threshold for change detection methods using a w...

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4th conference on Control and Fault Tolerant Systems (SysTol)

作者： Daass, Bilal Pomorski, Denis Haddadi, Kamel Univ Lille CNRS Cent LilleUMR 9189 CRIStAL Ctr Rech Informat Signal & Automat Lille F-59000 Lille France Univ Valenciennes Univ Lille CNRS IEMNCent LilleISENUMR 8520 F-59000 Lille France

ISBN: (纸本)9781728103808

Nowadays, process supervision occupies an important place in quality control, cooperative localization in mobile robotics, video and image processing, and intelligent system design, to name a few. Indeed, any failure of such processes can reduce performance and have serious consequences. The development of statistical methods, capable of detecting and locating anomalies in these dynamic systems as quickly as possible, is of real interest. In this context, we have proposed in a previous study a reformulation of the change detection strategy using an entropy-based criterion. Our approach allowed the calculation of an adaptive threshold, unlike the Bayes criterion. In this paper, we propose an improvement of this study by introducing the use of an optimal window of observations. We validate the proposed approach to the Exponentially Weighted Moving Average (EWMA) control charts, which is a commonly used change detection technique. Our strategy is illustrated on a well-known example of the literature. Finally, this windowed entropy-based criterion allows one to design a fault-tolerant fusion methodology, which is experimentally validated from an extended Kalman filter (EKF) in collaborative mobile robotics.

关键词： Microsoft Windows Robots Control charts Change detection algorithms Entropy Fault tolerant systems Collaboration

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Date Palm Disease Identification Using Features Extraction and Deep Learning Approach

Date Palm Disease Identification Using Features Extraction a...

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IEEE International conference on Multi Topic

作者： Aurangzeb Magsi Javed Ahmed Mahar Mirza Abdur Razzaq Sajid Habib Gill Shah Abdul Latif University Khairpur Mir's Pakistan

ISBN: (数字)9781728198934

ISBN: (纸本)9781728198941

The world is advancing in various fields of life, computers are intervening in every task progressively. In this context, biotechnology software applications are on the peak to solve intricate problems in many fields including agriculture. This paper deals with such problems and their time-series severity in the field of date palm diseases. Date fruit, being the richest one in proteins, is also economically important crop for Pakistan. Due to different highly harmful diseases, the crop does not reach the required level of amount and quality as well. Hence, an attempt to identify a common and rather dangerous disease (Sudden Decline Syndrome) is made through image processing techniques. This paper presents a mechanism to identify the mentioned disease at different stages infecting the date palms. The disease is categorically identified so that proper process may also begin to prevent the losses in the yields. The deep learning technique is used to identify the diseases on the basis of texture and color extraction methods. The dataset of 1200 date palm disease images is developed to experiment the proposed mechanism of disease identification and cumulatively achieved the accuracy of 89.4%. Later, statistical analysis is carried out of outcomes to further process through Convolutional Neural Network. This application will benefit at both macro as well as micro level harvesters and stakeholders across the country and globe.

关键词： Diseases Feature extraction image color analysis statistical analysis Agriculture Histograms Task analysis

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