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International symposium on Intelligent signal processing and Communication Systems (ISPACS)

作者： Tajima, Shinya Ogawa, Tomomi Matsumoto, Hiroki Maebashi Inst Technol Maebashi Gunma Japan

ISBN: (纸本)9781479961207

There is the over-sampling method which is one of the methods for realizing the blind identification. In the conventional method, the unknown system is usually assumed by FIR system and the estimation filter is also used by FIR filter. However, if a target system has long impulse length in many real environment, we may have to assume IIR system for the unknown system. If we use FIR filters to estimate such systems, the identification performance is lower. Therefore, it is necessary to research the blind identification method for the unknown system as IIR system. Then, we propose the blind identification based on over-sampling method using orthogonal filter for IIR system and we evaluate the identification performance by computer simulations.

关键词： IIR filters blind source separation signal sampling IIR system blind identification method computer simulations identification performance evaluation orthogonal filter over-sampling method Communication systems computational modeling Computer simulation Estimation Finite impulse response filters Vectors

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Comments on "Joint Detection and Estimation of Multiple Objects From image Observations"

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ieee TRANSACTIONS ON signal processing 2012年第3期60卷 1539-1540页

作者： Davey, Samuel J. Def Sci & Technol Org Intelligence Surveillance & Reconnaissance Div Edinburgh SA 5111 Australia Univ Adelaide Sch Elect & Elect Engn Adelaide SA 5005 Australia

The above article [1] introduced an algorithm for multitarget track-before-detect based on a multi-Bernoulli random finite set model (MB-TBD). This new algorithm was compared with the Histogram Probabilistic Multi-Hypothesis Tracker (H-PMHT) on simulated data examples containing multiple targets with non-linear dynamics. The authors reported poor performance from H-PMHT and described several deficiencies of the algorithm. This note highlights unnecessary assumptions made in the assessment of H-PMHT and repeats two of the simulation examples after relaxing them. We demonstrate a substantial improvement in performance compared with the originally published results. The simulation example is also shown to be a relatively high signal to noise problem and good performance is obtained from a conventional detect-then-track algorithm. [ABSTRACT FROM AUTHOR]

关键词： Approximation algorithms Australia computational modeling Estimation Heuristic algorithms Materials Target tracking

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Curvelet Transform Based Super-Resolution image processing

Curvelet Transform Based Super-Resolution Image Processing

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12th ieee International symposium on signal processing and Information Technology (ISSPIT)

作者： Thuong Le-Tien Luc Nguyen-Tan Giang Le-Thach Cao Bui-Thu Hochiminh City Univ Technol Dept Elect & Elect Engn 268 Ly Thuong KietDist 10 Ho Chi Minh City Vietnam Ind Univ Dept Elect Technol Ho Chi Minh City Vietnam

ISBN: (纸本)9781467356046

Super-Resolution (SR) processing is a technique that produces a High - Resolution (HR) image from a range of Low - Resolution (LR) images. There are many methods for implementing the SR signal processing. In this paper, we introduce a specific transformation that were developed in the recent years called Curvelet Transform (CT), and then apply it in SR processing to obtain a quality improvement of SR images. We discuss two separate algorithms using the Discrete Curvelet Transform (DCT) and then compare them based on the results of HR images. We find out that the first algorithm, named the iterative algorithm can produce better HR images than the second one, the Interpolation algorithm. However, the iterative algorithm also take more computational cost than that of the Interpolation Algorithm. It is a reasonable trade-off between images quality and processing speed. We also made a comparison between our algorithms and previous works such as the Projection onto Convex Sets (POCS) and the Nearest Neighbourhood algorithms to show the quality improvement.

关键词： Curvelet Transform Superresolution image processing Iterative Algorithm Interpolation Algorithm POCS algorithm Nearest Neighbourhood Algorithm

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Applying FOSSE Approach to image Data for Evaluating Game Situations

Applying FOSSE Approach to Image Data for Evaluating Game Si...

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ieee symposium Series on computational intelligence (SSCI)

作者： Suzuki, Yudai Nakashima, Tomoharu Osaka Prefecture Univ Grad Sch Humanit & Sustainable Syst Sci Sakai Osaka Japan

ISBN: (纸本)9781728124858

In this paper, we propose to apply a FOrward Simulation for Situation Evaluation (FOSSE) approach to image data. A FOSSE approach was proposed for evaluating situation of a soccer game in our previous work. It consists of two parts: Forward simulation part and evaluate situation part. At first, the FOSSE approach simulates the future situation by a simulation model before evaluating the current situation. Secondly, the approach evaluates the current situation by a evaluation model with simulated future information. In the FOSSE approach, the future game situations are generated by what is called forward simulation. The first FOSSE approach used numerical information such as the position of the ball and the players. Applying the FOSSE approach to image data is a difficult task because forward simulation of image data is not straight forward. Therefore, in this paper, we focus on the forward simulation of the image data and investigate the suitable image simulation model for the FOSSE approach. As the evaluation model, we employ a 3D Convolutional Neural Network. First, we show that the evaluation performance can be increased by using successive frames of field situations through a series of preliminary computational experiments. Especially, the effectiveness of using future frame information rather than using past frame information is shown. Then, we present FOSSE approach with image data where both the current and the future frame information of game situations are used to evaluate the current game situation. computational experiments are conducted to investigate the effective forward simulation model for our FOSSE approach.

关键词： Situation Evaluation Forward Simulation 3D Convolutional Neural Network Convolutional LSTM RoboCup Soccer Time Series Data Soccer Simulation image processing

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Removal of Fixed Valued Impulse Noise by Improved Trimmed Mean Median Filter 5

Removal of Fixed Valued Impulse Noise by Improved Trimmed Me...

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5th ieee International Conference on computational intelligence and Computing Research (ieee ICCIC)

作者： Sharma, Shachi Yadav, Pranay Samrat Ashok Technol Inst Dept Informat Technol Vidisha India Laxmipati Inst Sci & Technol Elect & Commun Bhopal India

ISBN: (纸本)9781479939756

Impulse noise removal is considered one of the most burning topic in digital image processing (DIP). When an image is formed, factors like lighting (source, and intensity) and camera characteristics like the sensor response, lenses and also atmospheric condition affect the presence of the image. It hides the important fine points and information of images. In order to enhance the qualities of the image, the removal of noises becomes imperative and that should not be at the cost of any loss of image information like edges. Removal of noise is one of the most important pre-processing tasks of different of image analysis works and tasks like image enhancement, steganography, segmentation and other enhancement related process. In this research article, we have proposed a new method for the removal and restoration of gray images is introduced, when images are corrupted by impulse noise. This method proposed a novel combination of Mean. Median and trimmed value concept for elimination of fixed valued impulse noise. Our methodology ensures a better performance for different level low, medium and high density of fixed value impulse noise as compare to the other famous filters like Standard Median Filter (SMF), Decision Based Median Filter (DBMF) and Modified Decision Based Median Filter (MDBMF) etc. The main objective of the proposed method is to improve not only a peak signal to noise ratio (PSNR) but also improve the visual perception and reduction in blurring of the resultant image. In the proposed method when previous pixels values, 0's and 255's are present in the particular window and all the pixel values are 0's and 255's then the remaining corrupted pixels are substituted by mean and median value. Proposed methodology was tested on gray-scale images like Mandrill and Lena. The experimental result shows improved value of peak signal to noise ratio (PSNR) and mean square error (MSE) values with better visual and human perception.

关键词： Salt and Pepper Noise Peak signal to Noise ratio (PSNR) Mean Square Error (MSE) Trimmed Mean Median Fixed Valued Impluse Noise (FVIN) Random Valued Impulse Noise (RVIN)

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The aircraft engine blade detection system based on USB2.0 and FPGA 6

The aircraft engine blade detection system based on USB2.0 a...

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6th International Conference on computational intelligence and Communication Networks

作者： Yu, Kunlin Changsha Aeronaut Vocat & Tech Coll Changsha Hunan Peoples R China

ISBN: (纸本)9781479969296

In order to realize automatic detection of aircraft engine blade quickly and accurately, an aircraft engine blade detection system based on USB2.0 and FPGA is designed. The system first converts the image signals of aircraft engine blade for CCD video signal, then the video signals for A/D conversion and digital image is stored in the cache in FIFO, finally the USB interface circuit carry the digital image into a computer for image processing and detection. The detection system has advantage of not being affected by man-made factors, high detection efficiency and detection accuracy, it has very important application value.

关键词： The CCD image sensor USB2.0 FPGA blade detection system image processing

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An Improved SIFT Descriptor Based on In-Out Region Division 2

An Improved SIFT Descriptor Based on In-Out Region Division

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2nd ieee International Conference on signal and image processing (ICSIP)

作者： Xia, Chunlong Wei, Ping Xi An Jiao Tong Univ Inst Artificial Intelligence & Robot Xian Shaanxi Peoples R China State Key Lab Math Engn & Adv Comp Wuxi Peoples R China

ISBN: (纸本)9781538609699

SIFT descriptor is one of the most widely-used local features for image matching. This paper presents an in-out region division method to improve the matching rate and the computational efficiency of SIFT descriptor. The descriptor region composed of pixel blocks around each keypoint is divided into the inner and the outer regions, in which different weights are assigned to the feature vectors. In each pixel block, a low dimensional feature vector is extracted. A bidirectional verification strategy is adopted to determine the keypoint matching. The experimental results show that the proposed method greatly improves the matching rate and the computational efficiency.

关键词： SIFT region division bidrectional verification

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image Denoising using Wavelet Transform and Wiener Filter based on Log Energy Distribution over Poisson-Gaussian Noise Model 5

Image Denoising using Wavelet Transform and Wiener Filter ba...

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5th ieee International Conference on computational intelligence and Computing Research (ieee ICCIC)

作者： Boyat, Ajay Kumar Joshi, Brijendra Kumar MITM Dept ECE Indore Madhya Pradesh India MHOW Mil Coll Tele Commun Engn Dept ECE Indore Madhya Pradesh India

ISBN: (纸本)9781479939756

Generally Gaussian noise model is used in image processing applications due to its presence in electronic circuitry used in image acquisition devices like Charge Coupled Device (CCD) cameras. Off let due to discrete nature of electromagnetic radiation in image acquisition process, due to photon noise that follows poison distribution is also exist with Gaussian noise. In this paper we present an algorithm based on Wavelet transform and wiener filter using log energy distribution to denoised digital images corrupted by Poisson-Gaussian noise. Our results clearly demonstrate the robustness of the algorithm over a large range of variance. We have used peak signal to noise ratio (PSNR) for comparison based on filter outlet.

关键词： Poisson noise Guassian noise Haar wavelet Wiener filter Log energies PSNR

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The Study of Adaptive Multi Threshold Segmentation Method for Apple Fruit Based on the Fractal Characteristics 8

The Study of Adaptive Multi Threshold Segmentation Method fo...

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8th International symposium on computational intelligence and Design (ISCID)

作者： Song, Xin Yang, Lei Tianjin Agr Univ Coll Engn & Technol Tianjin Peoples R China

ISBN: (纸本)9781467395878

According to the characteristics of the fruit and vegetable mechanization picking working environment, a segmentation method for apple fruit based on fractal characteristics is proposed in the paper. Firstly, the image is converted from RGB color space to I1I2I3 color space. Secondly, the image is divided into several regions and the texture features of every region are described by using the fractal theory. Finally, the regional segmentation threshold is determined according to the fractal dimension. Thus the fractal theory and the segmentation method of adaptive multi threshold have been combined to realize the accurate segmentation in the natural working environment of the apple fruit and its background. The experimental verification showed that the method has own stronger anti-noise ability. The method can be applied to precisely and effectively segment fruits from the background in the natural working environment. By comparing with the method of traditional local threshold segmentation and threshold iteration segmentation, the method proposed in the paper has higher processing speed and better segmentation effect at the background with different complexity, and can meet the realtime request of mechanization picking operations.

关键词： fruit image segmentation fractal characteristics adaptive multi threshold color space

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image Deblurring with Impulse Noise Using Split Bregman Algorithm

Image Deblurring with Impulse Noise Using Split Bregman Algo...

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2nd International symposium on computational intelligence and Design

作者： Yi Liya Lu Xiaolei Wang Jinjun Huang Benxiong Huazhong Univ Sci & Technol Dept Elect & Informat Engn Wuhan 430074 Peoples R China NEC Labs Amer Inc Cupertino CA 95014 USA

ISBN: (纸本)9780769538655

we propose an effective method to resolve blurred images with impulse noise. Our method has two steps. First, an improved adaptive median filter is proposed for image denoising;And second, the problem of deblurring the denoised image is formulated as to minimize the object function which consists of L1 data-fedility term and double regularization term. The minimization problem is solved by split Bregman algorithm. Numerical results using image with different blurs and impulse noise show that the proposed method gives better performance than the variable splitting alternative minimization algorithm in [10] by objective peak signal to noise ratio and subjective vision quality, which demonstrates the efficiency of our proposed algorithms.

关键词： image deblurring impulse noise double regularization split Bregman algorithm

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