Pub Date : 2016-10-01DOI: 10.1109/CISP-BMEI.2016.7852884
Congzheng Zhao, Xianxun Yao
Digital pre-distortion technique is one of the most crucial techniques used to solve frequency distortion in wireless communication links, which is due to nonlinear behavior of devices like power amplifier and limits frequency bandwidth of signal. This paper introduces a multi-function digital platform employed to verify and test different digital pre-distortion algorithms. The digital platform is mainly comprised a FPGA chip Xilinx Zynq-7000 (XC7Z030), an integrated transceiver chip ADI AD9361, and external digital interfaces such as HDMI, Ethernet network and fiber-optical. Processes needed by digital pre-distortion including A/D, D/A, and digital down conversion can be achieved in real time adjusted by software. Its working frequency bandwidth is from 70MHz to 6GHz, with instant frequency bandwidth up to 56MHz. The final pre-distortion results can be exhibited in video with graphical form.
{"title":"A digital hardware platform for RF PA digital predistortion algorithms","authors":"Congzheng Zhao, Xianxun Yao","doi":"10.1109/CISP-BMEI.2016.7852884","DOIUrl":"https://doi.org/10.1109/CISP-BMEI.2016.7852884","url":null,"abstract":"Digital pre-distortion technique is one of the most crucial techniques used to solve frequency distortion in wireless communication links, which is due to nonlinear behavior of devices like power amplifier and limits frequency bandwidth of signal. This paper introduces a multi-function digital platform employed to verify and test different digital pre-distortion algorithms. The digital platform is mainly comprised a FPGA chip Xilinx Zynq-7000 (XC7Z030), an integrated transceiver chip ADI AD9361, and external digital interfaces such as HDMI, Ethernet network and fiber-optical. Processes needed by digital pre-distortion including A/D, D/A, and digital down conversion can be achieved in real time adjusted by software. Its working frequency bandwidth is from 70MHz to 6GHz, with instant frequency bandwidth up to 56MHz. The final pre-distortion results can be exhibited in video with graphical form.","PeriodicalId":275095,"journal":{"name":"2016 9th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132718985","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 : 2016-10-01DOI: 10.1109/CISP-BMEI.2016.7852814
Yafeng Li, Ying-wei Lin
This paper describes a method of image sharpness evaluation while taking into account the photographer's aesthetic intention. The main idea is utilizing a visual importance map that estimates the weight of each pixel to guild evaluating image sharpness. The visual importance map is computed automatically with a saliency detection algorithm based on global color contrast. Our technique allows to treat pixels in an image differently based on their content, such that the perceptually important features and photograph's subjective intention can be reflected in the result. The proposed method is validated by experiment on public data set.
{"title":"Image sharpness evaluation based on visual importance","authors":"Yafeng Li, Ying-wei Lin","doi":"10.1109/CISP-BMEI.2016.7852814","DOIUrl":"https://doi.org/10.1109/CISP-BMEI.2016.7852814","url":null,"abstract":"This paper describes a method of image sharpness evaluation while taking into account the photographer's aesthetic intention. The main idea is utilizing a visual importance map that estimates the weight of each pixel to guild evaluating image sharpness. The visual importance map is computed automatically with a saliency detection algorithm based on global color contrast. Our technique allows to treat pixels in an image differently based on their content, such that the perceptually important features and photograph's subjective intention can be reflected in the result. The proposed method is validated by experiment on public data set.","PeriodicalId":275095,"journal":{"name":"2016 9th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115189402","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 : 2016-10-01DOI: 10.1109/CISP-BMEI.2016.7852693
Xin Zheng, Qingfeng Xu, Qingli Li, Xingliang Hu
Countries around the world have paid more and more attention to Magnetic Anomaly Detection (MAD), which is used to detect some magnetic substance. The Orthonormalized Basis Function (OBF) algorithm is a kind of effective method to detect the target signal embedded in the background noise. But in the case that the OBF algorithm does not work well in non-Gaussian noise, an improved algorithm is proposed to enhance the detection capability in this paper. Firstly, a narrowband FIR filter is designed to filter the signal out of the frequency band of the target signal according to the spectrum characteristics of the original signal. Then the filtered signal is decomposed by the OBF algorithm. And the experiment results show that The OBF based on narrowband filtering algorithm can increase the Signal to Noise Ratio (SNR) and enhance the accuracy of the target signal detection. Compared to using the traditional OBF algorithm directly, the improved method has better ability to detect magnetic objects.
{"title":"An Orthonormalized Basis Function based narrowband filtering algorithm for Magnetic Anomaly Detection","authors":"Xin Zheng, Qingfeng Xu, Qingli Li, Xingliang Hu","doi":"10.1109/CISP-BMEI.2016.7852693","DOIUrl":"https://doi.org/10.1109/CISP-BMEI.2016.7852693","url":null,"abstract":"Countries around the world have paid more and more attention to Magnetic Anomaly Detection (MAD), which is used to detect some magnetic substance. The Orthonormalized Basis Function (OBF) algorithm is a kind of effective method to detect the target signal embedded in the background noise. But in the case that the OBF algorithm does not work well in non-Gaussian noise, an improved algorithm is proposed to enhance the detection capability in this paper. Firstly, a narrowband FIR filter is designed to filter the signal out of the frequency band of the target signal according to the spectrum characteristics of the original signal. Then the filtered signal is decomposed by the OBF algorithm. And the experiment results show that The OBF based on narrowband filtering algorithm can increase the Signal to Noise Ratio (SNR) and enhance the accuracy of the target signal detection. Compared to using the traditional OBF algorithm directly, the improved method has better ability to detect magnetic objects.","PeriodicalId":275095,"journal":{"name":"2016 9th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115224766","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 : 2016-10-01DOI: 10.1109/CISP-BMEI.2016.7852945
Shun Zhang, Yuanyuan Wang, Jinhua Yu
Since the minimum variance beamformer occurred, adaptive beamformers in ultrasound imaging have been widely studied. Eigenspace-based minimum variance beamformer is an outstanding method which utilizes eigenvalue decomposition to construct signal and noise subspaces, enhancing the contrast of minimum variance beamformer. However, due to the constant threshold by which signal and noise subspaces are separated, the image will be distorted even if its contrast is improved. In this paper, a relationship between the eigenvalue threshold and the coherence factor (CF) is established to adjust the threshold adaptively so that the contrast is retained and the distortion is alleviated. Simulated and experimental data are used to reconstruct the image. Results of the proposed method are compared with results of the eigenspace-based minimum variance beamformer, which proves the validity of the proposed method.
{"title":"An adaptive eigenspace-based beamformer using coherence factor in ultrasound imaging","authors":"Shun Zhang, Yuanyuan Wang, Jinhua Yu","doi":"10.1109/CISP-BMEI.2016.7852945","DOIUrl":"https://doi.org/10.1109/CISP-BMEI.2016.7852945","url":null,"abstract":"Since the minimum variance beamformer occurred, adaptive beamformers in ultrasound imaging have been widely studied. Eigenspace-based minimum variance beamformer is an outstanding method which utilizes eigenvalue decomposition to construct signal and noise subspaces, enhancing the contrast of minimum variance beamformer. However, due to the constant threshold by which signal and noise subspaces are separated, the image will be distorted even if its contrast is improved. In this paper, a relationship between the eigenvalue threshold and the coherence factor (CF) is established to adjust the threshold adaptively so that the contrast is retained and the distortion is alleviated. Simulated and experimental data are used to reconstruct the image. Results of the proposed method are compared with results of the eigenspace-based minimum variance beamformer, which proves the validity of the proposed method.","PeriodicalId":275095,"journal":{"name":"2016 9th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124541433","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 : 2016-10-01DOI: 10.1109/CISP-BMEI.2016.7852911
P. Fan, Xinbao Liu
Ultrasonic testing technique has been widely applied for monitoring the metal structure health. It is useful to detect and access the damage condition with the minor crack information being concealed in the ultrasonic signal. Although there has been a large amount of studies, the extraction of robust minor crack features is still a fundamental problem. In this paper, a novel crack identification algorithm is proposed by the wavelet packet transform (WPT) of received signal. With the calculation of sub-band signal energy, the most suitable decomposition level is decided. Then, the features are defined by the correlation coefficient between the damaged signal and undamaged signal. With principal component analysis (PCA), the feature extraction is achieved by reducing the overlapped and redundant ones. Finally, the extracted features are fed into support vector machines (SVM) classier and their outputs are employed to classify the damage type. The performance of the proposed method is confirmed with practical experiment. It indicated that compared with other methods, the proposed algorithm has a higher identification accuracy with more robust features.
{"title":"A novel method of feature extraction for minor crack identification","authors":"P. Fan, Xinbao Liu","doi":"10.1109/CISP-BMEI.2016.7852911","DOIUrl":"https://doi.org/10.1109/CISP-BMEI.2016.7852911","url":null,"abstract":"Ultrasonic testing technique has been widely applied for monitoring the metal structure health. It is useful to detect and access the damage condition with the minor crack information being concealed in the ultrasonic signal. Although there has been a large amount of studies, the extraction of robust minor crack features is still a fundamental problem. In this paper, a novel crack identification algorithm is proposed by the wavelet packet transform (WPT) of received signal. With the calculation of sub-band signal energy, the most suitable decomposition level is decided. Then, the features are defined by the correlation coefficient between the damaged signal and undamaged signal. With principal component analysis (PCA), the feature extraction is achieved by reducing the overlapped and redundant ones. Finally, the extracted features are fed into support vector machines (SVM) classier and their outputs are employed to classify the damage type. The performance of the proposed method is confirmed with practical experiment. It indicated that compared with other methods, the proposed algorithm has a higher identification accuracy with more robust features.","PeriodicalId":275095,"journal":{"name":"2016 9th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114407529","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 : 2016-10-01DOI: 10.1109/CISP-BMEI.2016.7852790
Xiyue Hou, Qingli Li, Qian Wang, Mei Zhou, Hongying Liu
The segmentation of red blood cells and white blood cells has important research value in the field of rheological properties of blood and the pathogenesis of some diseases. And it is the reflection of bone hematopoietic state, blood diseases and other diseases. Especially for the diagnosis of blood diseases, the detection and prevention of treatment process, there is high value of clinical research. The separation of red blood cells and white blood cells using hyperspectral remote sensing image processing is a new field that it is essentially different from traditional multi spectral classification. Because of the different chemical composition and molecular space structure of red blood cells and white blood cells, it results in different spectrum. Each pixel of hyperspectral image can obtain a unique continuous spectral curve, and it can be compared with the spectral curves which are known to obtain target object. So the author designs a new analytical method which is based on the various processing methods of hyperspectral image. First of all, using the BandMax wizard to lock target image and band based on target detection; secondly, conducting differential search algorithm based on the blind signal; thirdly, using an improved algorithm—based on SAM combined with SID algorithm; finally, using advanced filtering method to get clearer image information. In this paper, it focuses on the effective extraction and improves the classification accuracy of white blood cells.
{"title":"An improved SAM algorithm for red blood cells and white blood cells segmentation","authors":"Xiyue Hou, Qingli Li, Qian Wang, Mei Zhou, Hongying Liu","doi":"10.1109/CISP-BMEI.2016.7852790","DOIUrl":"https://doi.org/10.1109/CISP-BMEI.2016.7852790","url":null,"abstract":"The segmentation of red blood cells and white blood cells has important research value in the field of rheological properties of blood and the pathogenesis of some diseases. And it is the reflection of bone hematopoietic state, blood diseases and other diseases. Especially for the diagnosis of blood diseases, the detection and prevention of treatment process, there is high value of clinical research. The separation of red blood cells and white blood cells using hyperspectral remote sensing image processing is a new field that it is essentially different from traditional multi spectral classification. Because of the different chemical composition and molecular space structure of red blood cells and white blood cells, it results in different spectrum. Each pixel of hyperspectral image can obtain a unique continuous spectral curve, and it can be compared with the spectral curves which are known to obtain target object. So the author designs a new analytical method which is based on the various processing methods of hyperspectral image. First of all, using the BandMax wizard to lock target image and band based on target detection; secondly, conducting differential search algorithm based on the blind signal; thirdly, using an improved algorithm—based on SAM combined with SID algorithm; finally, using advanced filtering method to get clearer image information. In this paper, it focuses on the effective extraction and improves the classification accuracy of white blood cells.","PeriodicalId":275095,"journal":{"name":"2016 9th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114515024","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 : 2016-10-01DOI: 10.1109/CISP-BMEI.2016.7853048
Yong Liu
It is certain that the individual learners should be different from each other in order for a committee machine to reach the better performance. However, differences alone among the individual learners are not enough for the committee machine to predict well on the unknown data. It would be essential for each individual learner to be able to decide whether to learn to be different or not to the other individuals on each given example. One way to implement such decision is through self-awareness. Self-awareness makes the individual learners in the committee machine be even more flexible during the learning process. With self-awareness, an individual learner could choose to go slower to the correct output by scaling down the error signals, or leave away faster from the correct output on a given data. In this paper, negative correlation learning with the scaled error signals were tested on the two medical data sets to show how important it is to adjust the error signals by the individual learners themselves in the committee machines.
{"title":"Control of the error signals by self-awareness in committee machines","authors":"Yong Liu","doi":"10.1109/CISP-BMEI.2016.7853048","DOIUrl":"https://doi.org/10.1109/CISP-BMEI.2016.7853048","url":null,"abstract":"It is certain that the individual learners should be different from each other in order for a committee machine to reach the better performance. However, differences alone among the individual learners are not enough for the committee machine to predict well on the unknown data. It would be essential for each individual learner to be able to decide whether to learn to be different or not to the other individuals on each given example. One way to implement such decision is through self-awareness. Self-awareness makes the individual learners in the committee machine be even more flexible during the learning process. With self-awareness, an individual learner could choose to go slower to the correct output by scaling down the error signals, or leave away faster from the correct output on a given data. In this paper, negative correlation learning with the scaled error signals were tested on the two medical data sets to show how important it is to adjust the error signals by the individual learners themselves in the committee machines.","PeriodicalId":275095,"journal":{"name":"2016 9th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117019780","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 : 2016-10-01DOI: 10.1109/CISP-BMEI.2016.7852937
Wenxiong Zhong, Dongxiao Li, Lianghao Wang, Ming Zhang
This work proposes a low-rank plus sparse model using dictionary learning for 3D-MRI reconstruction from downsampling k-space data. The scheme decomposes the dynamic image signal into two parts: low-rank part L and sparse part S and then, constructing it as a constrained optimization problem. In the optimization process,a nonconvex penalty function is used to optimize the low rank part L. The sparse part S is expressed by a over-complete dictionary using blind compressed sensing and we formulate the sparsity of coffecient matrix using l1 norm. To avoid the ill-posed of the problem, the Frobenius norm is used in dictionary. We adopt an alternate optimization algorithm to solve the problem, which cycles through the minimization of five subproblems. Finally, we prove the effectiveness of proposed method in two cardiac cine data sets. Experimental results were compared with exsiting L+S, L&S and BCS schemes, which demonstrate that the proposed method behaves better in removal of artifacts and maintaining the image details.
{"title":"Low-rank plus sparse reconstruction using dictionary learning for 3D-MRI","authors":"Wenxiong Zhong, Dongxiao Li, Lianghao Wang, Ming Zhang","doi":"10.1109/CISP-BMEI.2016.7852937","DOIUrl":"https://doi.org/10.1109/CISP-BMEI.2016.7852937","url":null,"abstract":"This work proposes a low-rank plus sparse model using dictionary learning for 3D-MRI reconstruction from downsampling k-space data. The scheme decomposes the dynamic image signal into two parts: low-rank part L and sparse part S and then, constructing it as a constrained optimization problem. In the optimization process,a nonconvex penalty function is used to optimize the low rank part L. The sparse part S is expressed by a over-complete dictionary using blind compressed sensing and we formulate the sparsity of coffecient matrix using l1 norm. To avoid the ill-posed of the problem, the Frobenius norm is used in dictionary. We adopt an alternate optimization algorithm to solve the problem, which cycles through the minimization of five subproblems. Finally, we prove the effectiveness of proposed method in two cardiac cine data sets. Experimental results were compared with exsiting L+S, L&S and BCS schemes, which demonstrate that the proposed method behaves better in removal of artifacts and maintaining the image details.","PeriodicalId":275095,"journal":{"name":"2016 9th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117027071","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 : 2016-10-01DOI: 10.1109/CISP-BMEI.2016.7852934
Jingyi Lou, Mei Zhou, Qingli Li, Chen Yuan, Hongying Liu
Blood cell analysis, including blood cell counting, is the key point for modern pathological study as well as medical diagnosis. Taking into account both resources and environment of the medical research, analyzing blood cells under the microscope, instead of dedicated blood cell analyzer, provides a more intuitive and convenient way for research uses. This paper aims to provide a method to count red blood cells (RBCs) automatically by analyzing blood cell images collected from a microscopic hyperspectral imaging system. The classification algorithms—spectral angle mappings (SAMs) and support vector machines (SVMs) are used to segment blood cell image. In order to identify RBCs in the image, a standard RBC model has been built to match RBCs in the segmentation results based on SAM classification algorithm. RBC counting results are therefore obtained from the identification and the counting accuracy reaches about 93%. For the sake of higher precision, an improved algorithm, using segmentation results based on SVM classification algorithm to screen the previous matching results, is proposed and the counting accuracy increases to about 98% after applying the improved algorithm.
{"title":"An automatic red blood cell counting method based on spectral images","authors":"Jingyi Lou, Mei Zhou, Qingli Li, Chen Yuan, Hongying Liu","doi":"10.1109/CISP-BMEI.2016.7852934","DOIUrl":"https://doi.org/10.1109/CISP-BMEI.2016.7852934","url":null,"abstract":"Blood cell analysis, including blood cell counting, is the key point for modern pathological study as well as medical diagnosis. Taking into account both resources and environment of the medical research, analyzing blood cells under the microscope, instead of dedicated blood cell analyzer, provides a more intuitive and convenient way for research uses. This paper aims to provide a method to count red blood cells (RBCs) automatically by analyzing blood cell images collected from a microscopic hyperspectral imaging system. The classification algorithms—spectral angle mappings (SAMs) and support vector machines (SVMs) are used to segment blood cell image. In order to identify RBCs in the image, a standard RBC model has been built to match RBCs in the segmentation results based on SAM classification algorithm. RBC counting results are therefore obtained from the identification and the counting accuracy reaches about 93%. For the sake of higher precision, an improved algorithm, using segmentation results based on SVM classification algorithm to screen the previous matching results, is proposed and the counting accuracy increases to about 98% after applying the improved algorithm.","PeriodicalId":275095,"journal":{"name":"2016 9th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123462532","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 : 2016-10-01DOI: 10.1109/CISP-BMEI.2016.7852798
Ma Chi, Wang Guosheng, Ban Xiao-juan, Ying Tian
Ear recognition is an emerging biometric technology and it has great potential and broad application and development space in the field of identity verification. SIFT (Scale invariant feature transform) has the advantages of better description of the model features, maintaining the structure information, the stability of the extracted feature points, the translation scale and rotation of the image and so on. In order to improve the efficiency and accuracy of image matching, a new bidirectional matching algorithm is proposed in this paper. In the experiment, to begin with different feature points are extracted from two images. Next using the BBF-based bi-directional matching method matched all these feature points respectively. the final matches were the integrated matching correspondences. Experiments results demonstrated that the new method can improve the matching accuracy and efficiency and reduce the time consuming by 44%.
{"title":"SIFT-based matching algorithm and its application in ear recognition","authors":"Ma Chi, Wang Guosheng, Ban Xiao-juan, Ying Tian","doi":"10.1109/CISP-BMEI.2016.7852798","DOIUrl":"https://doi.org/10.1109/CISP-BMEI.2016.7852798","url":null,"abstract":"Ear recognition is an emerging biometric technology and it has great potential and broad application and development space in the field of identity verification. SIFT (Scale invariant feature transform) has the advantages of better description of the model features, maintaining the structure information, the stability of the extracted feature points, the translation scale and rotation of the image and so on. In order to improve the efficiency and accuracy of image matching, a new bidirectional matching algorithm is proposed in this paper. In the experiment, to begin with different feature points are extracted from two images. Next using the BBF-based bi-directional matching method matched all these feature points respectively. the final matches were the integrated matching correspondences. Experiments results demonstrated that the new method can improve the matching accuracy and efficiency and reduce the time consuming by 44%.","PeriodicalId":275095,"journal":{"name":"2016 9th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121944904","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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