Pub Date : 2009-10-30DOI: 10.1109/CISP.2009.5304546
Zhifeng Zeng, Hua Ma
To achieve the theoretical high performance, array calibration is an indispensable procedure. In this paper, a novel mutual coupling calibration algorithm is proposed for uniform and circular array (UCA). The new method uses the subspace principle to estimate the coupling coefficients, requiring none calibration sources. Conditions are provided for the existence of a solution. Computer simulations show the effectiveness and behavior of the proposed method and prove that the nice statistical properties of classical super-resolution DOA estimation algorithms can be restored despite the presence of mutual coupling.
{"title":"Subspace-Based Self-Calibration of Mutual Coupling in Uniform and Circular Array","authors":"Zhifeng Zeng, Hua Ma","doi":"10.1109/CISP.2009.5304546","DOIUrl":"https://doi.org/10.1109/CISP.2009.5304546","url":null,"abstract":"To achieve the theoretical high performance, array calibration is an indispensable procedure. In this paper, a novel mutual coupling calibration algorithm is proposed for uniform and circular array (UCA). The new method uses the subspace principle to estimate the coupling coefficients, requiring none calibration sources. Conditions are provided for the existence of a solution. Computer simulations show the effectiveness and behavior of the proposed method and prove that the nice statistical properties of classical super-resolution DOA estimation algorithms can be restored despite the presence of mutual coupling.","PeriodicalId":263281,"journal":{"name":"2009 2nd International Congress on Image and Signal Processing","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115373099","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-10-30DOI: 10.1109/CISP.2009.5304497
Xin Zhang, G. He, Jiying Yuan
This paper introduces a method for extracting the distinctive feature of rotation invariance from image that shows reliable matching between images of different angle. First, we use the method of Harris corner detection for finding the interest points from the two images to be matched, in which an extreme tactics is adopted for exactly determining the interest points. Follow by adopting a sub-block checking method for eliminating the cluster and reduce the number of interest points. Second, this paper describes an approach to depict the characteristics of interest points which are gained by rotating the area centered the interest point. By this means the rotation invariance of images can implement. Third, the marching method is introduced calculating the similar information among these points, for example the NPROD algorithm. Through a number of experimental images, we prove that this method is proved viable and robust.
{"title":"A Rotation Invariance Image Matching Method Based on Harris Corner Detection","authors":"Xin Zhang, G. He, Jiying Yuan","doi":"10.1109/CISP.2009.5304497","DOIUrl":"https://doi.org/10.1109/CISP.2009.5304497","url":null,"abstract":"This paper introduces a method for extracting the distinctive feature of rotation invariance from image that shows reliable matching between images of different angle. First, we use the method of Harris corner detection for finding the interest points from the two images to be matched, in which an extreme tactics is adopted for exactly determining the interest points. Follow by adopting a sub-block checking method for eliminating the cluster and reduce the number of interest points. Second, this paper describes an approach to depict the characteristics of interest points which are gained by rotating the area centered the interest point. By this means the rotation invariance of images can implement. Third, the marching method is introduced calculating the similar information among these points, for example the NPROD algorithm. Through a number of experimental images, we prove that this method is proved viable and robust.","PeriodicalId":263281,"journal":{"name":"2009 2nd International Congress on Image and Signal Processing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115490287","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-10-30DOI: 10.1109/CISP.2009.5303680
Yejing Cai, Yonghong Long, Haixia Luo
The same object will show different colors under different illumination. An illuminant-independ color representation were proposed. Firstly, estimate the illumination color, and transform the RGB cube to a color space XLYLZL, then transform the XLYLZL back to RGB color space. To verify the effectiveness of color correction algorithm, a set of images taken under CIE A illuminant which are yellow-biased were processed to evaluate the performance. The original images were transformed to images under the CIE Standard Illuminant D65. Color difference of recovered image and standard image as well as original images and standard image were calculated to evaluate the performance, the experimental results show that with the color correction algorithm based on color space transformation, the accuracy rate of the recovered image improved by 68.98% compared with the original image.
{"title":"A Color Recovery Algorithm Based on Color Space Transformation","authors":"Yejing Cai, Yonghong Long, Haixia Luo","doi":"10.1109/CISP.2009.5303680","DOIUrl":"https://doi.org/10.1109/CISP.2009.5303680","url":null,"abstract":"The same object will show different colors under different illumination. An illuminant-independ color representation were proposed. Firstly, estimate the illumination color, and transform the RGB cube to a color space XLYLZL, then transform the XLYLZL back to RGB color space. To verify the effectiveness of color correction algorithm, a set of images taken under CIE A illuminant which are yellow-biased were processed to evaluate the performance. The original images were transformed to images under the CIE Standard Illuminant D65. Color difference of recovered image and standard image as well as original images and standard image were calculated to evaluate the performance, the experimental results show that with the color correction algorithm based on color space transformation, the accuracy rate of the recovered image improved by 68.98% compared with the original image.","PeriodicalId":263281,"journal":{"name":"2009 2nd International Congress on Image and Signal Processing","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123147304","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-10-30DOI: 10.1109/CISP.2009.5304431
Xiao-xia Shi, Jun Yu Li
to confirm the useful information better, the changing magnitude of the value must be considered. Secondly, the effect of filter is related to the choice and the input parameters of filter, but it is so difficult to confirm the choice and the input parameters of filter due to the complicated wave of the real time series. So this de-noising method will be complicated to apply. According to this shortage, here proposes a kind of de-noising method based on event. This method directly utilizes the mathematical features of curve such as the extremum slope and curvature to de-noise. The principle of this method is simple and easy to operate. In order to prove the validity and advantage, Here first it researches on the de-noising method based on Fourier transform and makes experiment based on real time series of the stock with two methods. The result proves that the new method can de-noise effectively, at the same time it is easy to operate and can keep the original characters of the time series furthest. II、 DE-NOISING METHOD BASED ON FOURIER TRANSFORM Fourier transform is one of the basic and common-used signal processing method. If let {at :t=…,-1,0,1,…} denote one infinite series of real value variables, then Fourier transform can be defined as complex value function below:
{"title":"Research on the De-Noising Algorithm","authors":"Xiao-xia Shi, Jun Yu Li","doi":"10.1109/CISP.2009.5304431","DOIUrl":"https://doi.org/10.1109/CISP.2009.5304431","url":null,"abstract":"to confirm the useful information better, the changing magnitude of the value must be considered. Secondly, the effect of filter is related to the choice and the input parameters of filter, but it is so difficult to confirm the choice and the input parameters of filter due to the complicated wave of the real time series. So this de-noising method will be complicated to apply. According to this shortage, here proposes a kind of de-noising method based on event. This method directly utilizes the mathematical features of curve such as the extremum slope and curvature to de-noise. The principle of this method is simple and easy to operate. In order to prove the validity and advantage, Here first it researches on the de-noising method based on Fourier transform and makes experiment based on real time series of the stock with two methods. The result proves that the new method can de-noise effectively, at the same time it is easy to operate and can keep the original characters of the time series furthest. II、 DE-NOISING METHOD BASED ON FOURIER TRANSFORM Fourier transform is one of the basic and common-used signal processing method. If let {at :t=…,-1,0,1,…} denote one infinite series of real value variables, then Fourier transform can be defined as complex value function below:","PeriodicalId":263281,"journal":{"name":"2009 2nd International Congress on Image and Signal Processing","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121812269","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Partial transmit sequences (PTS) is an effective technique to reduce the peak-to-average power ratio (PAPR) of orthogonal frequency division multiplexing signals. However, the conventional partial transmit sequence (PTS) technique requires an exhaustive searching over all the combinations of the given phase factors, which results in the computational complexity increases exponentially with the number of the sub-blocks, so it is difficult to achieve in practical system. This paper proposed a novel scheme which searches the combination of phase factor by means of a modified neighborhood search method. Simulation results show that this scheme can greatly reduce the complexity with a little degradation of system's performance.
{"title":"PAPR Reduction of OFDM Signals Using Modified Partial Transmit Sequences","authors":"Ya-fei Tian, Rong-hua Ding, Xiao-an Yao, Haiwei Tang","doi":"10.1109/CISP.2009.5302979","DOIUrl":"https://doi.org/10.1109/CISP.2009.5302979","url":null,"abstract":"Partial transmit sequences (PTS) is an effective technique to reduce the peak-to-average power ratio (PAPR) of orthogonal frequency division multiplexing signals. However, the conventional partial transmit sequence (PTS) technique requires an exhaustive searching over all the combinations of the given phase factors, which results in the computational complexity increases exponentially with the number of the sub-blocks, so it is difficult to achieve in practical system. This paper proposed a novel scheme which searches the combination of phase factor by means of a modified neighborhood search method. Simulation results show that this scheme can greatly reduce the complexity with a little degradation of system's performance.","PeriodicalId":263281,"journal":{"name":"2009 2nd International Congress on Image and Signal Processing","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116666953","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-10-30DOI: 10.1109/CISP.2009.5303537
Zhaohui Zhang, Ruiqing Chen, Hanqing Lu, YuKun Yan, HuiQing Cui
This paper presents a modified codebook model for real-time moving foreground detection. The proposed method is an effective combination of background modeling and motion detection. Without a long training sequence, the background model can be represented in a compressed form, a series of codebooks, which means sample background values for each pixel are quantized into codebooks that can used in detection process. In this way, we can capture the structural variation of background in different conditions such as periodic-like motion , hostile environment or change of scene caused by moving object over a long period of time under limited memory. Compared with the original codebook model, this proposed method is more efficient in computation and takes up less memory. Experimental results show that the proposed algorithm is effective, quick for motion detection, and can meet the demands of real-time applications.
{"title":"Moving Foreground Detection Based on Modified Codebook","authors":"Zhaohui Zhang, Ruiqing Chen, Hanqing Lu, YuKun Yan, HuiQing Cui","doi":"10.1109/CISP.2009.5303537","DOIUrl":"https://doi.org/10.1109/CISP.2009.5303537","url":null,"abstract":"This paper presents a modified codebook model for real-time moving foreground detection. The proposed method is an effective combination of background modeling and motion detection. Without a long training sequence, the background model can be represented in a compressed form, a series of codebooks, which means sample background values for each pixel are quantized into codebooks that can used in detection process. In this way, we can capture the structural variation of background in different conditions such as periodic-like motion , hostile environment or change of scene caused by moving object over a long period of time under limited memory. Compared with the original codebook model, this proposed method is more efficient in computation and takes up less memory. Experimental results show that the proposed algorithm is effective, quick for motion detection, and can meet the demands of real-time applications.","PeriodicalId":263281,"journal":{"name":"2009 2nd International Congress on Image and Signal Processing","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116702380","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-10-30DOI: 10.1109/CISP.2009.5302590
Xue Mei Li, R. Tao, Yue Wang
The time delay estimation between two signals in the passive system has been an important issue. In this paper, we propose a new time delay estimator based on the delay property of the fractional Fourier transform (FRFT). It is suitable for chirp signals in the passive system. The time delay is evaluated in the fractional Fourier domain by measuring the time differential between the time delays obtained from the two peak values of the fractional spectra of the received signals. Simulation results show that the proposed time delay method performs better than the conventional cross correlation approach at low signal-to-noise (SNR).
{"title":"Time Delay Estimation Based on the Fractional Fourier Transform in the Passive System","authors":"Xue Mei Li, R. Tao, Yue Wang","doi":"10.1109/CISP.2009.5302590","DOIUrl":"https://doi.org/10.1109/CISP.2009.5302590","url":null,"abstract":"The time delay estimation between two signals in the passive system has been an important issue. In this paper, we propose a new time delay estimator based on the delay property of the fractional Fourier transform (FRFT). It is suitable for chirp signals in the passive system. The time delay is evaluated in the fractional Fourier domain by measuring the time differential between the time delays obtained from the two peak values of the fractional spectra of the received signals. Simulation results show that the proposed time delay method performs better than the conventional cross correlation approach at low signal-to-noise (SNR).","PeriodicalId":263281,"journal":{"name":"2009 2nd International Congress on Image and Signal Processing","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116785467","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-10-30DOI: 10.1109/CISP.2009.5301561
S. Tong, Ruimin Wang, Huaishan Liu, Jin Zhang, C. Bu
The multiple reflections develop in deep-sea. Especially in rough deep-sea bottom, the multiple is more complicated. Conventional radon transform filtering has its deficiency, and the effection is not obvious in multiple suppressing of seismic data in deep-sea. In order to achieve high resolution radon transform, this paper proposes an improved method. This method is able to build a very good model of multiple reflections, then suppress the multiples, overcome the deficiency by conventional radon transform. To verify the efficiency of this method, the rough deep seafloor model was established. By using this method, good results were obtained. At the same time, the method has been applied to process many real deep-sea seismic data, the highprecision seismic data profile was gained and obvious effects of application were achieved. KeywordsRadon Transform;high resolution; multiple suppression
{"title":"High Resolution Radon Transform and its Applications in Multiple Suppression of Seismic Data in Deep-Sea","authors":"S. Tong, Ruimin Wang, Huaishan Liu, Jin Zhang, C. Bu","doi":"10.1109/CISP.2009.5301561","DOIUrl":"https://doi.org/10.1109/CISP.2009.5301561","url":null,"abstract":"The multiple reflections develop in deep-sea. Especially in rough deep-sea bottom, the multiple is more complicated. Conventional radon transform filtering has its deficiency, and the effection is not obvious in multiple suppressing of seismic data in deep-sea. In order to achieve high resolution radon transform, this paper proposes an improved method. This method is able to build a very good model of multiple reflections, then suppress the multiples, overcome the deficiency by conventional radon transform. To verify the efficiency of this method, the rough deep seafloor model was established. By using this method, good results were obtained. At the same time, the method has been applied to process many real deep-sea seismic data, the highprecision seismic data profile was gained and obvious effects of application were achieved. KeywordsRadon Transform;high resolution; multiple suppression","PeriodicalId":263281,"journal":{"name":"2009 2nd International Congress on Image and Signal Processing","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116952618","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-10-30DOI: 10.1109/CISP.2009.5300913
Shu-xia Zhang, Yu-zhong Jiang
The Middleton Class A interference model is a statistical-physical and parametric model for man-made and natural electromagnetic (EM) interference. In this paper, the efficient estimation of the Class A model parameters based on least square gradient method is derived. The considered estimator converges fast and low-complexity with performance approaching theoretical optima for large data samples. Simulation of this estimator with three unknown parameters indicates that this technique is efficient. Index Terms—Middleton Class A Model. Impulsive Noise. Parameter Estimation. Non-Gaussian Noise. characteristic function has simple form(12). In this paper we proposed a method for parameter estimation based on the characteristic function spectrum estimation from observation samples. Our method is well suited not only for two-parameter estimation of Class A model like Zabin's work(10), but also for estimation of full three-parameter estimation and adaptive to track changes for channel noise. The later is critical to the implementation of signal detection and estimation algorithms in non-Gaussian noise environment.
{"title":"Identification of Class a Noise Parameters via Least Square Gradient Method","authors":"Shu-xia Zhang, Yu-zhong Jiang","doi":"10.1109/CISP.2009.5300913","DOIUrl":"https://doi.org/10.1109/CISP.2009.5300913","url":null,"abstract":"The Middleton Class A interference model is a statistical-physical and parametric model for man-made and natural electromagnetic (EM) interference. In this paper, the efficient estimation of the Class A model parameters based on least square gradient method is derived. The considered estimator converges fast and low-complexity with performance approaching theoretical optima for large data samples. Simulation of this estimator with three unknown parameters indicates that this technique is efficient. Index Terms—Middleton Class A Model. Impulsive Noise. Parameter Estimation. Non-Gaussian Noise. characteristic function has simple form(12). In this paper we proposed a method for parameter estimation based on the characteristic function spectrum estimation from observation samples. Our method is well suited not only for two-parameter estimation of Class A model like Zabin's work(10), but also for estimation of full three-parameter estimation and adaptive to track changes for channel noise. The later is critical to the implementation of signal detection and estimation algorithms in non-Gaussian noise environment.","PeriodicalId":263281,"journal":{"name":"2009 2nd International Congress on Image and Signal Processing","volume":"34 51","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120813562","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-10-30DOI: 10.1109/CISP.2009.5303781
Jun Yan, Yanfang Liu, Jun Wang, Hui Cao, Haibin Zhao
The fundamental of neural network and its application are introduced firstly. Then the main factors which affect the runoff and the sediment transport volume in the problems of the water and sediment fluxes in the Yellow River Mouth during the flood and non-flood period are analyzed. Furthermore, the BP model is set up by using the program in the toolbox of the neural network under the environment of MATLAB. Finally the runoff and the sediment transport volume in Linjin section are forecasted and the forecasting errors are analyzed.
{"title":"BP Model Applied to Forecast the Water and Sediment Fluxes in the Yellow River Mouth","authors":"Jun Yan, Yanfang Liu, Jun Wang, Hui Cao, Haibin Zhao","doi":"10.1109/CISP.2009.5303781","DOIUrl":"https://doi.org/10.1109/CISP.2009.5303781","url":null,"abstract":"The fundamental of neural network and its application are introduced firstly. Then the main factors which affect the runoff and the sediment transport volume in the problems of the water and sediment fluxes in the Yellow River Mouth during the flood and non-flood period are analyzed. Furthermore, the BP model is set up by using the program in the toolbox of the neural network under the environment of MATLAB. Finally the runoff and the sediment transport volume in Linjin section are forecasted and the forecasting errors are analyzed.","PeriodicalId":263281,"journal":{"name":"2009 2nd International Congress on Image and Signal Processing","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121039576","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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