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

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Advanced Plane Properties by Using Level image 28

Advanced Plane Properties by Using Level Image

引用

28th International conference on Radioelektronika (RADIOELEKTRONIKA)

作者： Chmelar, Pavel Beran, Ladislav Chmelarova, Natalija Rejfek, Lubos Fac Elect Engn & Informat Dept Elect Engn Pardubice Czech Republic

ISBN: (纸本)9781538624852

This paper deals with advanced plane properties in analyzed point clouds. Planes are detected by the level connected component labeling, which is our modification of the classical algorithm for 3D data. According selected detection parameters and a scanning dimension the algorithm detects individual levels in an input point cloud. A level is presented by an image expressing the points' presence at a specific level in a space, we call it level image. The pixel size in a level image is equal to the point cloud distance quantization. This connection allows us to use image processing methods to get important properties about the analyzed 3D space, which can be difficult to get by the standard mathematical solution. image processing methods offers a simple plane segmentation and visualization or area and perimeter estimation including statistical data description. The results show level image advantages.

关键词： plane detection level image level connected component labeling point cloud area and perimeter estimation

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Modified Smoothed Projected Landweber Algorithm for Adaptive Block Compressed Sensing image Reconstruction 6

Modified Smoothed Projected Landweber Algorithm for Adaptive...

引用

International conference on Audio, Language and image processing (ICALIP)

作者： Luo, Hui Zhang, Ning Wang, Yuandong East China Jiaotong Univ Sch Informat Engn Nanchang Jiangxi Peoples R China

ISBN: (纸本)9781538651957

In this article, according to the block compressed sensing (BCS) framework, a novel adaptive sampling method is proposed. First, the statistical information of image block is calculated. Then, based on the statistical information of image block and DCT coefficients, a weighted allocation factor is constructed and each block is categorized into the complex block or the simple block. And according to the different types of blocks, the sampling rate can be allocated adaptively. For validating the effectiveness of proposed sampling method and further improving the efficiency of BCS reconstruction, the modified smoothed projected Landweber (MSPL) algorithm is proposed to speed up comparatively slow rate of convergence. The results of the experiment demonstrate that the proposed method (ABCS-MSPL) significantly improves the peak-signal-to-noise ratio (PSNR) and running efficiency compared to other advanced methods for this purpose.

关键词： Block compressed sensing Adaptive sampling statistical information Modified smoothed projected Landweber

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CT image Reconstruction using Fourth-order PDE based AD Regularization

CT Image Reconstruction using Fourth-order PDE based AD Regu...

引用

International conference on image Information processing

作者： Yadunath Pathak Dheeraj Agarwal Shailendra Tiwari Manju Khurana IIIT Bhopal INDIA MANIT Bhopal INDIA TIET Patiala INDIA

One-Step-Late (OSL) statistical iterative algorithm plays an important role in Computed Tomography (CT) image reconstruction. But the fundamental problem related to OSL is the optimum initial value condition, slow convergence, and ill-posed. To resolves these problems, we present a modified OSL algorithm. The issue of optimum initial value condition and slow convergence can handle by integrating the Simultaneous Algebraic Reconstruction Technique (SART) with OSL called as modified OSL (SART+OSL). The output of modified OSL undertakes in Fourth order partial differential equation (PDE) based Anisotropic Diffusion regularization approach to deal with an ill-posed. It is an extended version of the Perona-Malik (P-M) filter. For validation of the proposed model, both simulated and real standard thorax phantoms have been used. Finally, the results were compared with the related state-of-the-art methods. It is observed that the proposed model has many desirable advantages such as noise reduction, minimize the computational cost, as well as accelerate the convergence rate.

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An English Handwriting Evaluation Algorithm Based on CNNs

An English Handwriting Evaluation Algorithm Based on CNNs

引用

International conference on Artificial Intelligence and Advanced Manufacturing (AIAM)

作者： Yingguo Gao Runze Yu Xiaohui Duan Center for Wireless Communication and Signal Processing EECS Peking University Beijing China

English handwriting evaluation is an essential part in elemental English teaching. An automatic evaluation algorithm for English handwriting quality is proposed in this paper. Generally, conventional document image processing approaches rely on hand-crafted features for capturing statistical or structural information. In contrast, we take advantage of Convolutional Neural Networks (CNNs) for extracting features from raw image pixels. The performance of this algorithm is more effective than traditional machine learning methods and the accuracy is greater than 94% in our experiment. Based on this algorithm, an intelligent English handwriting marking system is designed and it is already online.

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Research on Machine Learning Natural Feature Matching Method Based on statistical Classification

Research on Machine Learning Natural Feature Matching Method...

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Information Technology and Computer Application (ITCA), International conference on

作者： Ruiyang Sun Statistics Faculty of mathematics University of Waterloo Waterloo On Canada

ISBN: (数字)9781728164946

ISBN: (纸本)9781728164953

With the development of intelligent manufacturing, machine vision has become one of the indispensable technologies in many fields. The so-called machine learning is to acquire images through image sensors, analyze and process the images by computers, and then guide mechanical mechanisms to perform motion operations. As a branch of machine vision, feature matching technology is a key step of many image processing technologies. The matching degree usually directly determines the overall performance of the system. As an important component of artificial intelligence, machine learning has gradually been applied to various industries in recent years. Its intelligent characteristics make the application process achieve substantial results. This article is based on statistical classification methods, combined with the background of natural feature matching in intelligent machine learning and the main matching methods for analysis, and aims to lay the foundation for specific applications in the future.

关键词： Machine learning Machine learning algorithms image recognition Machine vision Internet Feature extraction

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Impact damage characterization in CFRP plates using PCA and MEEMD decomposition methods in optical lock-in thermography phase images 11

Impact damage characterization in CFRP plates using PCA and ...

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conference on Optical Measurement Systems for Industrial Inspection XI held at SPIE Optical Metrology Symposium

作者： de Oliveira, Bernardo C. F. Frohlich, Herberth B. Barrera, Estiven S. Baldo, Crhistian R. Albertazzi, Armando G., Jr. Schmitt, Robert Univ Fed Santa Catarina Campus Univ UFSCPostal Box 5053 Florianopolis SC Brazil Fed Univ ABC Ave Estados 5001 Santo Andre SP Brazil Rhein Westfal TH Aachen Cluster Prod Tech Block 3A & BCampus Blvd 30 Aachen Nrw Germany

ISBN: (数字)9781510627925

ISBN: (纸本)9781510627925

Carbon fiber reinforced plastics (CFRP) are composite materials which are an interesting alternative to metal alloys in fields such as oil, aerospace, automotive, since CFRP have mechanical properties like metals but with a fraction of their weights. However, these materials have typically a highly anisotropic behavior, which may hinder the characterization of their integrity for example when subjected to an impact, because of its stochastic nature. Non-destructive testing (NDT) methods are interesting for integrity assessment, as they can evaluate the damage extension without affecting any part characteristics. Optical lock-in thermography (OLT) is an convenient NDT inspection alternative since it is a depth-wise method in which one can set different loading frequencies, leading to different scan depths. Pre-processing techniques like Principal Component Analysis (PCA) and Empirical Mode Decomposition (EMD) can be used to more accurately evaluate the damaged area. Their dimensionality reduction capability is highly desired as OLT images of CFRP laminates do not only show the defect, but also undesired information such as changes of background radiation, noise and the disposition of the fiber tissue. Traditional feature extraction methods must be highly tuned to obtain useful results. PCA and EMD methods may be considered non-supervised approaches, making them useful for a wide variety of inputs. However, PCA and EMD have their own natural limitations when being applied to images. While PCA may require high computational effort because of mathematical manipulation of matrices due to the mathematical manipulation of large matrices, problems due to mode superposition may occur if EMD original method is applied on images. In this sense, in this work it has been performed a comparison between PCA, with a new input vector architecture used to mitigate its problem with matrix dimensions, and a derivation of EMD method, called Multi-dimensional Ensemble Empirical Mode Deco

关键词： optical lock-in thermography CFRP non-destructive testing PCA MEEMD image segmentation impact damage

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On the Performance Analysis of Active Visual 3D Reconstruction in Multi-agent Networks

On the Performance Analysis of Active Visual 3D Reconstructi...

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International conference on Wireless Communications and Signal processing (WCSP)

作者： Qier An Yuan Shen Tsinghua University Beijing National Research Center for Information Science and Technology Beijing China

Visual 3D reconstruction builds the 3D map of the environment from images and is essential in a wide range of applications such as robotics, augmented reality and relic preservation. In this paper, we integrate the visual 3D reconstruction with the mobility of mobile platforms to address the active visual 3D reconstruction problem in multi-agent networks. We first establish a statistical model of the visual 3D reconstruction problem in multi-agent networks. Then we propose a next-best-view selection scheme to find the best camera configuration in active reconstruction. Moreover, we propose a statistical evaluation criterion to substitute traditional laser-scanned-model-based methods to measure the reconstruction quality under certain camera configuration. Numerical results verify the effectiveness of our methods.

关键词： Three-dimensional displays Cameras Visualization image reconstruction Solid modeling Robot vision systems

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Selection of Training Options for Deep Learning Neural Network Using Genetic Algorithm

Selection of Training Options for Deep Learning Neural Netwo...

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International conference on methods and Models in Automation and Robotics (MMAR)

作者： Piotr Szymak Institute of Electrical Enginnering and Automatics Polish Naval Academy Gdynia Poland

Recently, a growing usage and consequently a developing level of autonomy of Autonomous Underwater Vehicles (AUVs) can be seen. These vehicles are power supplied and controlled from the sources located on their boards. One of the most often used sensors of the AUV is a video camera. This sensor in connection with the video images processing software can increase the level of autonomy of the AUV. One of the most popular applications using video camera is an image recognition, e.g. for the obstacle detection. One of the newest methods used for this application is the Deep Learning Neural Network (DLNN). The goal of the paper is to examine the genetic algorithm optimization method for the selection of training options for DLNN used for the underwater images recognition. In the research, the pretrained AlexNet DLNN and the stochastic Gradient Descent with Momentum (SGDM) training method have been used. It is planned to implement examined DLNN on board of the Biomimetic Underwater Vehicles (BUV).

关键词： Training Sensors Cameras image recognition Neural networks Genetic algorithms Reactive power

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Word-Wise Handwriting Based Gender Identification Using Multi-Gabor Response Fusion 4th

Word-Wise Handwriting Based Gender Identification Using Mult...

引用

4th Workshop on Document Analysis and Recognition, DAR 2018, held in Conjunction with the 11th Indian conference on Vision, Graphics, and image processing, ICVGIP 2018

作者： Asadzadeh Kaljahi, Maryam Vidya Varshini, P.V. Shivakumara, Palaiahnakote Pal, Umapada Lu, Tong Guru, D.S. Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia Vellore Institute of Technology VelloreTamil Nadu India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India National Key Lab for Novel Software Technology Nanjing University Nanjing China Department of Studies in Computer Science Manasagangotri University of Mysuru Mysore India

ISBN: (纸本)9789811393600

Handwriting based gender identification at the word level is challenging due to free style writing, use of different scripts, and inadequate information. This paper presents a new method based on Multi-Gabor Response (MGR) fusion for gender identification at the word level. It first explores weighted-gradient features for word segmentation from text line images. For each word, the proposed method obtains eight Gabor response images. Then it performs sliding window operation over MGR images to smooth the values. For each smoothed MGR images, we perform fusion operation that chooses the Gabor response value which contributes to the highest peak in the histogram. This process results in a feature matrix, which is fed to CNN for gender identification. Experimental results on our dataset (multi scripts) apart from English, and benchmark databases, namely, IAM, KHATT, and QUWI, which contain handwritten English and Arabic text, show that the proposed method outperforms the existing methods. © Springer Nature Singapore Pte Ltd. 2019.

关键词： image segmentation

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BLIND image QUALITY ASSESSMENT BASED ON VISUO-SPATIAL SERIES STATISTICS

BLIND IMAGE QUALITY ASSESSMENT BASED ON VISUO-SPATIAL SERIES...

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

作者： Zhou, Ziheng Lu, Wen Yang, Jiachen He, Lihuo Gao, Xinbo Xidian Univ Sch Elect Engn Xian Shaanxi Peoples R China Tianjin Univ Sch Elect Informat Engn Tianjin Peoples R China

ISBN: (纸本)9781538646588

Existing blind image quality assessment (BIQA) methods based on statistics attach limited attention to the relative position of pixels. Features in these BIQA methods are too flimsy to characterize quite a few distortions with strong locality or complexity. However, psychological studies have shown that according to the relative position within visual field, the cognitive system generates visuo-spatial serial memory used for cognitive tasks, e. g., subjective image quality assessment. Inspired by the visuo-spatial series generated by human visual system (HVS), we propose a BIQA method based on imitation Visuo-spatial Series Statistics (VSS). The proposed method simulates visual system to construct visuo-spatial series based on the relative position of pixels, and use statistical features of visuo-spatial series to predict image quality. Extensive experiments demonstrate the proposed method has a superior performance compared to the state-of-the-art BIQA methods.

关键词： Blind image quality assessment pixel relation visuo-spatial series statistics

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