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

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A Survey on Performance of Various image Compression Techniques

A Survey on Performance of Various Image Compression Techniq...

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International conference on Electrical, Electronics, Communication, Computer and Optimization Techniques (ICEECCOT)

作者： H R Gireesh L Rangaiah VTU Belagavi India RRCE Bangalore India

ISBN: (数字)9781538651308

ISBN: (纸本)9781538651315

image compression enables quite exciting solutions in many fields, such as image analysis, bio-medical image processing, wireless systems and seems to be a key application in today's digital and smart world. image compression seems to be a powerful tool in case of transmission and storage of large data images in various applications such as big data, medical etc. However due to exclusive and quality soft tissue contrast, Dynamic Magnetic Resonance Imaging (MRI) has been a field of attraction with increasing attention in recent decades. Moreover, MRI is considered as one of the most effective and strongest diagnosis system making the extensive usage of magnetic and radio waves in order to diagnose the human organs. This diagnosis is capable of generating 3D images with detailed anatomical features without any X-ray radiations. The prime purpose of this survey is to provide a comprehensive report of different image compression schemes in order to design an efficient compression scheme for dynamic MRI images. In this paper, author has surveyed different image compression schemes which are either sole implementations or hybrid of two or more algorithms. The author has also presented a comparative analysis for the surveyed compression schemes. This survey paper finally makes inroads for further researches in the domain of image compression schemes for dynamic MRI images.

关键词： image coding Magnetic resonance imaging Discrete cosine transforms Biomedical imaging Entropy Discrete wavelet transforms

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Everything is image: CNN-based Short-term Electrical Load Forecasting for Smart Grid 14

Everything is Image: CNN-based Short-term Electrical Load Fo...

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14th International Symposium on Pervasive systems, algorithms and Networks (I-SPAN) / 11th International conference on Frontier of Computer Science and Technology (FCST) / 3rd International Symposium of Creative Computing (ISCC)

作者： Li, Liangzhi Ota, Kaoru Dong, Mianxiong Muroran Inst Technol Dept Informat & Elect Engn Muroran Hokkaido Japan

ISBN: (纸本)9781538608401

Electrical load forecasting is of great significance to guarantee the system stability under large disturbances, and optimize the distribution of energy resources in the smart grid. Traditional prediction models, which are mainly based on time series analyzing, have been unable to fully meet the actual needs of the power system, due to their non-negligible prediction errors. To improve the forecasting precision, we successfully transform the numerical prediction problem into an image processing task, and, based on that, utilize the state-of-the-art deep learning methods, which have been widely used in computer image area, to perform the electrical load forecasting. A novel deep learning based short-term forecasting (DLSF) method is proposed in the paper. Our method can perform accurate clustering on the input data using a deep Convolutional Neural Network (CNN) model. And ultimately, another neural network with three hidden-layers is used to predict the electric load, considering various external influencing factors, e.g. temperature, humidity, wind speed, etc. Experimental results demonstrate that the proposed DLSF method performs well in both accuracy and efficiency.

关键词： Load forecasting Neural networks Forecasting Support vector machines Machine learning Load modeling Training

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Use of OWA Operators for Feature Aggregation in image Classification

Use of OWA Operators for Feature Aggregation in Image Classi...

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IEEE International conference on Fuzzy systems (FUZZ-IEEE)

作者： Pagola, Miguel Forcen, Juan I. Barrenechea, Edurne Lopez-Molina, Carlos Bustince, Humberto Univ Publ Navarra Inst Smart Cities Pamplona 31006 Spain

ISBN: (纸本)9781509060344

Feature aggregation is a crucial step in many methods of image classification, like the Bag-of-Words (BoW) model or the Convolutional Neural Networks (CNN). In this aggregation step, usually known as spatial pooling, the descriptors of neighbouring elements within a region of the image are combined into a local or a global feature vector. The combined vector must contain relevant information, while removing irrelevant and confusing details. Maximum and average are the most common aggregation functions used in the pooling step. To improve the aggregation of relevant information without degrading their discriminative power for classification in this work we propose the use of Ordered Weighted operators. We provide an extensive evaluation that shows that the final result of the classification using OWA aggregation is always better than average pooling and better than maximum pooling when dealing with small dictionary sizes.

关键词： Estimation Field programmable gate arrays OFDM Modulation Filtration Clocks Signal processing algorithms

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Novel segmentation for twig problem by adding prior nodes in Random-Walk algorithm 2

Novel segmentation for twig problem by adding prior nodes in...

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2nd International conference on Communication and Electronics systems, ICCES 2017

作者： Chougule, Keshav J. Subhedar, Mansi S. Department of Electronics and Telecommunication Pillai HOC College of Engineering and Technology Rasayani Raigad. Maharashtra410206 India

ISBN: (纸本)9781509050130

In object identification image Segmentation is the first step in digital image processing. It can be used to compress different segments or areas of image. A novel sub-Markov Random Walk (subRW) algorithm with label prior is proposed for seeded image segmentation. It is traditional random walker on a graph with added auxiliary nodes. The uniqueness will be nothing but adding or changing the auxiliary nodes in segmentation algorithm. We face segmentation problem in existing system if the image having very thin and elongated parts. we design a new sub RW algorithm with label prior to solve the segmentation problem of objects with thin and elongated parts (i.e Twig Problem). The experimental results on both synthetic and natural images with twigs demonstrate that the proposed subRW method outperforms previous Well Known RW algorithms for segmentation. © 2017 IEEE.

关键词： image segmentation

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image denoising and deblurring using multispectral data 23

Image denoising and deblurring using multispectral data

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23rd SPIE conference on algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral imagery

作者： Semenishchev, E. A. Voronin, V. V. Marchuk, V. I. Don State Tech Univ Dept Radioelect Syst Shevchenko 147 Shakhty 346500 Russia

ISBN: (数字)9781510608986

ISBN: (纸本)9781510608979;9781510608986

Currently decision-making systems get widespread. These systems are based on the analysis video sequences and also additional data. They are volume, change size, the behavior of one or a group of objects, temperature gradient, the presence of local areas with strong differences, and others. Security and control system are main areas of application. A noise on the images strongly influences the subsequent processing and decision making. This paper considers the problem of primary signal processing for solving the tasks of image denoising and deblurring of multispectral data. The additional information from multispectral channels can improve the efficiency of object classification. In this paper we use method of combining information about the objects obtained by the cameras in different frequency bands. We apply method based on simultaneous minimization L2 and the first order square difference sequence of estimates to denoising and restoring the blur on the edges. In case of loss of the information will be applied an approach based on the interpolation of data taken from the analysis of objects located in other areas and information obtained from multispectral camera. The effectiveness of the proposed approach is shown in a set of test images.

关键词： saliency map image edge detectors density

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Design of Smart Video Surveillance System for Indoor and Outdoor Scenes 22

Design of Smart Video Surveillance System for Indoor and Out...

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2017 22nd International conference on Digital Signal processing (DSP)

作者： Kumar, Himanshu Bhattacharya, Saumik Thomas, Sinnu Susan Gupta, Sumana Venkatesh, K. S. IIT Kanpur Dept Elect Engn Kanpur Uttar Pradesh India

ISBN: (纸本)9781538618950

Smart video surveillance of indoor and outdoor scenes is a challenging task for modern surveillance systems. Different imaging conditions like bad illumination, adverse weather, etc., makes the surveillance process difficult. Recently, researchers have proposed smart surveillance systems with additional features for more accurate monitoring of events, but not much attention is paid to improve the system such that the monitoring process consumes as minimum resources as possible. In this paper, we propose a novel surveillance system that enhances visibility in adverse weather conditions and summarizes the captured videos automatically to reduce storage space. As the summarization process is based on the events in a scene, video interpretation becomes fast and easy. We propose perceptual features that can be used for more meaningful and robust summarization of the video than the existing summarization algorithms. We test the system for both indoor and outdoor scenes and show that the system works well even with multiple moving objects and complex motions.

关键词： Surveillance System image Enhancement Video Summarization

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Digital Computation of Fractional Fourier and Linear Canonical Transforms and Sparse image Representation 9

Digital Computation of Fractional Fourier and Linear Canonic...

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9th Annual Summit and conference of the Asia-Pacific-Signal-and-Information-processing-Association (APSIPA ASC)

作者： Koc, Aykut Ozaktas, Haldun M. Bartan, Burak Gundogdu, Erhan Cukur, Tolga ASELSAN Res Ctr Ankara Turkey Bilkent Univ Dept Elect Engn Ankara Turkey Stanford Univ Dept Elect Engn Stanford CA 94305 USA Bilkent Univ Natl Magnet Resonance Res Ctr UMRAM Ankara Turkey

ISBN: (纸本)9781538615423

Fast and accurate digital computation of the fractional Fourier transform (FRT) and linear canonical transforms (LCT) are of utmost importance in order to deploy them in real world applications and systems. The algorithms in O(NlogN) to obtain the samples of the transform from the samples of the input function are presented for several different types of FRTs and ICTs, both in 1D and 2D forms. To apply them in image processing we consider the problem of obtaining sparse transform domains for images. Sparse recovery tries to reconstruct images that are sparse in a linear transform domain, from an underdetermined measurement set. The success of sparse recovery relies on the knowledge of domains in which compressible representations of the image can be obtained. In this work, we consider two- and three-dimensional images, and investigate the effects of the fractional Fourier (FRT) and linear canonical transforms (LCT) in obtaining sparser transform domains. For 2D images, we investigate direct transforming versus several patching strategies. For the 3D case, we consider biomedical images, and compare several different strategies such as taking 2D slices and optimizing for each slice and direct 3D transforming.

关键词： Two dimensional displays image coding Fourier transforms Matrix decomposition Chirp image representation

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A Fast image Match Method Based on Water Wave Optimization and Gray Relational Analysis 9

A Fast Image Match Method Based on Water Wave Optimization a...

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9th IEEE International conference on Intelligent Data Acquisition and Advanced Computing systems - Technology and Applications (IDAACS)

作者： Xu, Wei Ye, Zhiwei Hou, Yuqian Hubei Univ Sch Comp Sci Wuhan Hubei Peoples R China

ISBN: (纸本)9781538606971

image match has been widely used in computer vision, pattern recognition and image processing. The matching efficiency is a focus topic in the field and some methods have been presented, such as simplification of similarity measure, application of optimization algorithms. Particle swarm optimization algorithm (PSO) has been utilized successfully for image match. However, it is easy to fall into the local optimum and the accuracy isn't good enough. The Water Wave Optimization (WWO) algorithm is a new evolutionary algorithm, which has been proved to be superior to many leading heuristic optimization algorithms on some benchmarking problems and engineering practical problems. In the paper, gray correlation analysis is used to simplify the calculation of similarity measure, and then WWO is employed to obtain the best matching position fast. Experimental results demonstrate that the proposed approach has higher efficiency and matching accuracy than GPSO and GABCA, meanwhile, it has good anti-noise performance.

关键词： image Match Water Wave Optimization Gray Relational Analysis

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image Reconstruction Based on combination of Modified CoSaMP and Orthogonal Wavelets

Image Reconstruction Based on combination of Modified CoSaMP...

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International conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT)

作者： Kanjalkar, Pramod M. Patil, Pooja T. Rao, M. Venu Gopal KL Univ Dept ECE Vaddeswaram 522 502 Andhra Pradesh India Vishwakarma Inst Technol Dept I&C Pune Maharashtra India

ISBN: (纸本)9781509061068

Compressive sensing is a signal processing technique for efficiently acquiring and reconstructing the signals by finding solutions to underdetermined linear systems. Non- linear optimization algorithms are used to solve underdetermined linear system. Greedy algorithms are widely used due to their low complexity than that of non-linear optimization algorithms. The problem with the Greedy algorithms is that some algorithms reduces reconstruction time but quality of reconstruction is affected while some require more reconstruction time with high quality of reconstruction. This is serious issue for the large size images. In this paper we have worked on CoSaMP algorithm and by modifying it we have reduced reconstruction time to reconstruct the sparse image with high quality of reconstruction which solves the above mention problem. Experimentation is performed on Modified CoSaMP using orthogonal filters like db4 , Haar and coif3 for different size of the images and results in terms of PSNR (peak signal to noise ratio), SSIM (Structural similarity index measurement) and runtime have been calculated and compared with OMP, ROMP, OLS and CoSaMP.

关键词： Compressive Sensing (CS) OMP (Orthogonal Matching Pursuit) ROMP (Regularized Orthogonal Matching Pursuit) OLS (Orthogonal Least Square) CoSaMP (Compressive Sampling Matching Pursuit) PSNR SSIM

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Outlier processing via L1-principal subspaces 30

Outlier processing via L1-principal subspaces

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30th International Florida Artificial Intelligence Research Society conference, FLAIRS 2017

作者： Chamadia, Shubham Pados, Dimitris A. Department of Electrical Engineering State University of New York BuffaloNY14260 United States

ISBN: (纸本)9781577357872

With the advent of big data, there is a growing demand for smart algorithms that can extract relevant information from high-dimensional large data sets, potentially corrupted by faulty measurements (outliers). In this context, we present a novel line of research that utilizes the robust nature of L1-norm subspaces for data dimensionality reduction and outlier processing. Specifically, (i) we use the euclidean-distance between original and L1-norm-subspace projected samples as a metric to assign weight to each sample point, (ii) perform (K=2)-means clustering over the one-dimensional weights discarding samples corresponding to the outlier cluster, and (iii) compute the robust L1-norm principal subspaces over the reduced "clean" data set for further applications. Numerical studies included in this paper from the fields of (i) data dimesnionality reduction, (ii) direction-of-arrival estimation, (iii) image fusion, and (iv) video foreground extarction demonstrate the efficacy of the proposed outlier processing algorithm in designing robust low-dimensional subspaces from faulty high-dimensional data. Copyright © 2017, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： image fusion

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