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检索条件"任意字段=Conference on Image Processing - Algorithms and Systems VI"

共 15616 条记录，以下是4511-4520 订阅

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AN INTERIOR POINT METHOD FOR NONNEGATIVE SPARSE SIGNAL RECONSTRUCTION 25

AN INTERIOR POINT METHOD FOR NONNEGATIVE SPARSE SIGNAL RECON...

引用

25th IEEE International conference on image processing (ICIP)

作者： Huang, Xiang He, Kuan Yoo, Seunghwan Cossairt, Oliver Katsaggelos, Aggelos Ferrier, Nicola Hereld, Mark Argonne Natl Lab Math & Comp Sci Div 9700 S Cass Ave Argonne IL 60439 USA Northwestern Univ Dept Elect Engn & Comp Sci Evanston IL 60208 USA

ISBN: (纸本)9781479970612

We present a primal-dual interior point method (IPM) with a novel preconditioner to solve the l(1)-norm regularized least square problem for nonnegative sparse signal reconstruction. IPM is a second-order method that uses both gradient and Hessian information to compute effective search directions and achieve super-linear convergence rates. It therefore requires many fewer iterations than first-order methods such as iterative shrinkage/thresholding algorithms (ISTA) that only achieve sub-linear convergence rates. However, each iteration of IPM is more expensive than in ISTA because it needs to evaluate an inverse of a Hessian matrix to compute the Newton direction. We propose to approximate each Hessian matrix by a diagonal matrix plus a rank-one matrix. This approximation matrix is easily invertible using the Sherman-Morrison formula, and is used as a novel preconditioner in a preconditioned conjugate gradient method to compute a truncated Newton direction. We demonstrate the efficiency of our algorithm in compressive 3D volumetric image reconstruction. Numerical experiments show favorable results of our method in comparison with previous interior point based and iterative shrinkage/thresholding based algorithms.

关键词： l(1)-norm regularized optimization primal-dual preconditioned interior point method nonnegative sparse compressive sensing 3d volumetric image reconstruction

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Modeling and Performance Evaluation of Generative adversarial network for image denoising 1

Modeling and Performance Evaluation of Generative adversaria...

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1st International conference on Computational Techniques, Electronics and Mechanical systems (CTEMS)

作者： Nuthna, V. Chachadi, Kavita Joshi, Leah S. KLE Technol Univ Dept Elect & Elect Hubballi Karnataka India

ISBN: (纸本)9781538677094

During transmission and reception the images are degraded by noise. Also images captured using different low quality devices adversely affect the visual quality of images. The presence of the noise results in loss of visibility, gives a mottled, grainy, textured, or snowy appearance. Thus making the image visually unpleasing which drastically affects the human vision. In addition during image processing, presence of noise makes it hard for further processing (impulse, Gaussian white, salt and pepper, adversarial etc.,). Existing methods use conventional filters and Neural network models for image denoising where they compromise with the visibility of image after rigorous iterations of denoising algorithms. In this paper we implement CGANs for image denoising and evaluate the performance of CGAN with different Neural network models viz., CNN,GAN for single or multiple image denoising problem. The qualitative performance of de-noised image/images is measured using PSNR and confusion matrix.

关键词： denoising CGAN

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A Novel Code-Aided Non-Orthogonal Multiple Access Technique in Downlink MIMO System

A Novel Code-Aided Non-Orthogonal Multiple Access Technique ...

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Intelligent Applied systems on Engineering (ICIASE), IEEE International conference of

作者： Wei-Chiang Wu Department of Electrical Engineering Da-Yeh University R.O.C.

The following topics are dealt with: wireless sensor networks; learning (artificial intelligence); feature extraction; particle swarm optimisation; production engineering computing; object detection; convolutional neu... 详细信息

ISBN: (数字)9781538681398

ISBN: (纸本)9781538681404

关键词： NOMA Downlink Array signal processing Interference Clustering algorithms Silicon carbide Signal to noise ratio

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The Design and Implementation of an image processing Framework with a Graphical Programming Interface for Low-Ended FPGAs

The Design and Implementation of an Image Processing Framewo...

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International conference on image, vision and Computing (ICIVC)

作者： Nuntipat Narkthong Santitham Prom-on Department of Computer Engineering King Mongkut’s University of Technology Thonburi Bangkok Thailand

ISBN: (纸本)9781728123264

image processing and computer vision algorithms are very computationally intensive and could not be implemented on low power microcontroller used in embedded and small IoT devices. Performing computation on the cloud is not practical for always-on real-time operations and deploying a high-end microcontroller or a microprocessor consume too much power and are generally too expensive to fit into these small systems. In this paper, we propose a new programming workflow using reusable hardware modules and a graphical programming interface for implementing a complete image processing system on an FPGAs which overcomes the steep learning curve of tradition FPGAs design tool. The design can be deployed onto the low-ended FPGAs in a similar price point as a mid-range microcontroller. To demonstrate our proposed framework, we have implemented a number of image transformation operations on an FPGAs development board with Lattice iCE40 Ultra Plus FPGA and a tiny camera module. Results have shown that our designs can fit in a low-ended FPGAs while performing 5.5-345x faster and consuming 2.4-4x less power compared to current state-of-the-art microcontroller used in small embedded and IoT devices.

关键词： image processing Graphical programming FPGAs graphical programming image processing Field programmable gate arrays Microcontrollers REAL TIME OPERATION hardware module embedding

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Real time image Recognition Based on low cost processing Platform

Real time Image Recognition Based on low cost Processing Pla...

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International conference on Telecommunications in Modern Satellite, Cable and Broadcasting Service (TELSIKS)

作者： Diana Baicu Mihai Crăciunescu Ştefan Mocanu Maria Cîrciumaru Faculty of Automatic Control and Computer Science University Politehnica of Bucharest Bucharest Romania

ISBN: (数字)9781728108780

ISBN: (纸本)9781728108797

In this paper, the traffic sign recognition module of a small-scale autonomous car prototype will be presented. The process undergoing the choice of an appropriate algorithm, as well as the factors taken into consideration will be presented in the form of a case study. The current literature presents various ways of achieving the recognition of traffic signs, but most of them are computational expensive, or have difficulty in offering consistent results in conditions that are different from the prerecorded ones. Since the processing on the car is carried on an embedded platform from Nvidia (Jetson TX2), this study is based on the same board, the stream being captured with a low-cost webcam. Classical algorithms like SURF (Speeded Up Robust Features), SIFT (Scale Invariant Feature Transform) or ORB (Oriented fast and Rotated Brief) offer reliable results when the lighting condition between the reference image and the image obtained from the camera are similar. In our setup, the algorithms mentioned above start to behave badly in low light conditions. Therefore, this paper discusses the possibility of using Haar like features alongside a classifier for detecting traffic signs.

关键词： Feature extraction Cameras image color analysis Automobiles Machine learning algorithms Object recognition Detectors

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Signal processing for Optical Communication System Assisted by Computer vision Techniques 10

Signal Processing for Optical Communication System Assisted ...

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10th International conference on Advanced Infocomm Technology (ICAIT)

作者： Bo, Tianwai Chan, Calvin Chun-Kit Kim, Hoon Korea Adv Inst Sci & Technol Sch Elect Engn 291 Daehak Ro Daejeon 34141 South Korea Chinese Univ Hong Kong Dept Informat Engn Hong Kong Peoples R China

ISBN: (纸本)9781538679364

We review the recent progress on the application of image processing techniques to optical communication systems. The focus is placed mainly on the implementation complexity and performance of the techniques for optical performance monitoring and the compensation of common phase error. We also briefly introduce several applications where machine learning algorithms could be beneficial to fiber-optic transmission system.

关键词： digital signal processing image processing optical communications

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Novel Stop Sign Detection Algorithm Based on Vehicle Speed Profile

Novel Stop Sign Detection Algorithm Based on Vehicle Speed P...

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American Control conference

作者： Soodeh Dadras Homayoun Jamshidi Sara Dadras Thomas Edward Pilutti Utah State University Logan UT 84322 USA Ford Motor Company

ISBN: (纸本)9781538679012;9781538679265

Automated driving technology involves various modern in-vehicle systems that are designed to increase road traffic safety by helping drivers gain a better awareness of the road and its potential hazards as well as other drivers around them. Stop sign detection module is an integral part of these systems as it has great utility in automated or assisted driving applications. Although promising results have been achieved in the areas of stop sign detection and classification, these methods are heavily dependent on images and image processing algorithms and detection problem in the real world remains a challenging issue. In this paper, we propose a method to detect the stop sign, based on statistical analysis of data obtained by drive history, a project conducted by Ford Motor Company. Our detection algorithm is based on the speed profile where speed is zero or close to zero. Then, we apply clustering to our data set to extract the points with this common feature. In the end, results demonstrate that our algorithm can improve the stop sign detection efficiency and afford high precision where other algorithms are prone to failure.

关键词： driver information systems image classification intelligent transportation systems object detection pattern clustering road safety road traffic road vehicles statistical analysis parking signs driver information systems Road traffic Statistical methods Statistical analysis Intelligent transportation systems road vehicles Object detection Road safety image classification Cluster Analysis Detection algorithms Land transportation

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Technique for Acceleration of Classical Ward's Method for Clustering of image Pixels

Technique for Acceleration of Classical Ward's Method for Cl...

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International Russian Automation conference (RusAutoCon)

作者： Igor G. Khanykov Laboratory of Technology of Big Data and Socialcyberphysical Systems Institute for Informatics and Automation of the Russian Academy of Sciences Saint Peterbsurg Russia

The segmentation task refers to the preliminary stage of image preprocessing. Further object detection, feature recognition, scene analysis and prediction of the situations depends on its results. Modern segmentation algorithms require: the refusal to use of a priori information, the availability of a quality functional for the result assessment, the variable number of segments in the partition of the original image, the linear computational complexity and the adequacy of the segmentation results. Among the methods of the cluster analysis that satisfy most of the requirements listed, the Ward's method is appropriate. But, its high computational complexity prevents its direct application. The purpose of this study is to overcome the excessively high computational complexity of the classical Ward's method. The classical methods of cluster analysis that meet the actual requirements for modern image segmentation algorithms are compared. The choice of the classical Ward's method is justified as well as its advantages and disadvantages are presented. The application of the idea of the reversible computing in image processing is described. The modifications of the computational process of the Ward's method are described. A model flowchart of a sequence of algorithms allowing to bypass the problem of the computational complexity peculiar to Ward's method is proposed. The experimental results of the quality improvement of the conventional segmentation are presented. The proposed model flowchart allows one to bypath the problem of the computational complexity by dividing the process into separate sequential three stages. The model flowchart is suitable for the quality improvement of any traditional segmentation.

关键词： image segmentation Computational complexity Computational modeling Clustering algorithms Flowcharts Merging

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Energy Efficient ApproxSIFT Implementation for image Mosaic with Approximate Computing Technologies 61

Energy Efficient ApproxSIFT Implementation for Image Mosaic ...

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61st IEEE International Midwest Symposium on Circuits and systems (MWSCAS)

作者： Liu, Zheyu Ren, Erxiang Zhang, Yanming Qiao, Fei Wei, Qi Luo, Li Yang, Huazhong Tsinghua Univ Dept Elect Engn Beijing Peoples R China Beijing Jiaotong Univ Sch Elect & Informat Engn Beijing Peoples R China

ISBN: (纸本)9781538673928

image mosaic could generate wide-angle images by stitching regional images with their overlapped portions, which is widely used technology in image signal processing. During the procedure, SIFT-feature based approaches are proven to perform best in such methods. However, traditional implementations of SIFT algorithm would consume the most of the processing time and energy. By balancing the processing quality and the design specifications of such applications of image mosaic, approximate computing hardware implementations have been adopted in this work to improve the energy-efficiency with ignorable quality degradations. Moreover, the approximate adders of LOA have been used to replace the exact adder to carry out the Gaussian convolution process of the algorithms, which further boost the SIFT operation performance, so-called ApproxSIFT. Simulation results of logic level show that, compared with the traditional method of exact computing, the ApproxSIFT hardware implementation with approximate adders significantly improve the image mosaic system, which achieves 2.4X and 3.5X in speed and power consumption, respectively.

关键词： image mosaic SIFT Approximate computing Low-Part-OR Adder (LOA)

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Deep learning based guidewire segmentation in x-ray images

Deep learning based guidewire segmentation in x-ray images

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conference on Medical Imaging - Physics of Medical Imaging

作者： Wagner, Martin G. Laeseke, Paul Speidel, Michael A. Univ Wisconsin Dept Med Phys 1111 Highland Ave Madison WI 53705 USA Univ Wisconsin Dept Radiol 600 Highland Ave Madison WI 53792 USA Univ Wisconsin Dept Med 1685 Highland Ave Madison WI 53705 USA

ISBN: (纸本)9781510625440

X-ray fluoroscopy is commonly used during liver embolization procedures to guide intravascular devices (e.g. guidewire and catheter) to the branches of the hepatic arteries feeding tumors. A vascular roadmap can be created to provide a reference for the device position. Recently, techniques have been developed to create dynamic vessel masks to compensate for respiratory motion. In order to superimpose the intravascular guidewire onto the vessel mask, robust segmentation is required. Commonly used techniques often use mask subtraction to isolate the device in x-ray images. However, this is not suitable due to the motion in liver applications. The proposed method uses a deep convolutional neural network to segment the guidewire in native (unsubtracted) x-ray images. The neural network uses an encoder / decoder structure, which is based on the VGG-16 network. To create a large dataset of annotated images, simulated images were created based on 3D digital subtraction angiography acquisitions of hepatic arteries in porcine studies. Random guidewire shapes were generated within the vascular volume and superimposed on the original non-contrast projection images. The network was trained using a set of 56,768 images created from 10 acquisitions. The segmentation results of the trained network were compared to a mask-subtraction-based algorithm for an independent validation data set. The deep learning algorithm (Dice = 58.1%, false negative rate (FNR) = 9.6%) outperformed the subtraction technique (Dice = 23.7%, FNR = 40.8%). This study shows that the deep learning approach is suitable for robust segmentation of curvilinear structures such as guidewires and could be used to superimpose the segmented device on dynamic roadmaps.

关键词： image segmentation X-rays X-ray imaging 3D acquisition Arteries Computer programming image processing algorithms and systems Neural networks Liver Tumors

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