The purpose of this research is to develop a biometric system for individual identification with the electrocardiogram (ECG) signal. The ECG signal varies from person to person and it can be used as a new biometric for individual identification. This paper presents a robust preprocessing stage to eliminate the effect from noise and heart rate. A new feature extraction technique known as Ensemble Empirical Mode Decomposition (EEMD) with Teager Energy Operator (TEO) is derived and used to generate novel ECG feature vectors. The dimensionality reduction method Principal Component Analysis (PCA) is used reduce the feature space before classification. Finally, K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) algorithm are chosen as the classifiers. The proposed method is validated by experiments on 40 subjects from three public databases; the experiment results show that the subject recognition rate achieves 95.5% and 97.5% with KNN and SVM classifier respectively. For larger changes in heart rate, it also shows strong stability.
{"title":"Improved human identification method based on electrocardiogram using ensemble empirical mode decomposition and Teager Energy Operator","authors":"Yanjun Deng, Zhidong Zhao, Yefei Zhang, Diandian Chen","doi":"10.1109/CISP-BMEI.2017.8302224","DOIUrl":"https://doi.org/10.1109/CISP-BMEI.2017.8302224","url":null,"abstract":"The purpose of this research is to develop a biometric system for individual identification with the electrocardiogram (ECG) signal. The ECG signal varies from person to person and it can be used as a new biometric for individual identification. This paper presents a robust preprocessing stage to eliminate the effect from noise and heart rate. A new feature extraction technique known as Ensemble Empirical Mode Decomposition (EEMD) with Teager Energy Operator (TEO) is derived and used to generate novel ECG feature vectors. The dimensionality reduction method Principal Component Analysis (PCA) is used reduce the feature space before classification. Finally, K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) algorithm are chosen as the classifiers. The proposed method is validated by experiments on 40 subjects from three public databases; the experiment results show that the subject recognition rate achieves 95.5% and 97.5% with KNN and SVM classifier respectively. For larger changes in heart rate, it also shows strong stability.","PeriodicalId":6474,"journal":{"name":"2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"1 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90856597","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 : 2017-10-01DOI: 10.1109/CISP-BMEI.2017.8302240
Y. Liu, Xiaomei An
Regarded as one of the common cancers, the prostate cancer is a main reason harming the health of senile men, especially in Europe and the United States. In China, with increasing of living standards and aging of populations, the incidence of prostate cancer has an upward and younger tendency. Early detection and early treatment are helpful to patients. In the imaging diagnosis methods of prostate, magnetic resonance imaging (MRI) has been recognized as the most effective way. Whereas MRI image has several specialized configurations with a lot of medical information, and diagnosis results have a strong relationship with doctor's professional skill and experience, which makes the diagnosis for prostate cancer more difficult. Based on deep learning and the Convolutional Neural Networks (CNN), an image classification model which can provide some diagnosis classification reference was proposed in this paper. The data sets used in this paper consisted of 10056 diffusion weighted magnetic resonance imaging (DWI) images. Three quarters of the images were used for training and the rest images for testing. Experiments show that the accuracy rate of training set is 80.1539%, and the accuracy rate of testing set is 78.1538%. The curves of testing accuracy rate and loss show that this model has been trained steadily. The accuracy rate for single images are above 64.91%, and some may reach 99.99%. With certain clinical application value, this deep learning method can be widely applied to the grading and staging of prostate cancer and other cancer tasks.
{"title":"A classification model for the prostate cancer based on deep learning","authors":"Y. Liu, Xiaomei An","doi":"10.1109/CISP-BMEI.2017.8302240","DOIUrl":"https://doi.org/10.1109/CISP-BMEI.2017.8302240","url":null,"abstract":"Regarded as one of the common cancers, the prostate cancer is a main reason harming the health of senile men, especially in Europe and the United States. In China, with increasing of living standards and aging of populations, the incidence of prostate cancer has an upward and younger tendency. Early detection and early treatment are helpful to patients. In the imaging diagnosis methods of prostate, magnetic resonance imaging (MRI) has been recognized as the most effective way. Whereas MRI image has several specialized configurations with a lot of medical information, and diagnosis results have a strong relationship with doctor's professional skill and experience, which makes the diagnosis for prostate cancer more difficult. Based on deep learning and the Convolutional Neural Networks (CNN), an image classification model which can provide some diagnosis classification reference was proposed in this paper. The data sets used in this paper consisted of 10056 diffusion weighted magnetic resonance imaging (DWI) images. Three quarters of the images were used for training and the rest images for testing. Experiments show that the accuracy rate of training set is 80.1539%, and the accuracy rate of testing set is 78.1538%. The curves of testing accuracy rate and loss show that this model has been trained steadily. The accuracy rate for single images are above 64.91%, and some may reach 99.99%. With certain clinical application value, this deep learning method can be widely applied to the grading and staging of prostate cancer and other cancer tasks.","PeriodicalId":6474,"journal":{"name":"2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"36 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91349742","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 : 2017-10-01DOI: 10.1109/CISP-BMEI.2017.8302332
Xuekun Zhang, Jing An, Wen Liu
With the development of Internet, Data information is growing at an explosive rate. With the era of big data coming, information social value can only be reflected by people's utilization. In the vast amounts of data, keywords as relatively concise summary of the documentation, its can provide efficient information management methods. Keyword extraction technology (KET)can help people get the data information accurately and quickly, so KET is widely used in the information management system. According to the study of keyword extraction method recent years, the classic TF — IDF algorithm and TextRank algorithm were studied in this paper, TextRank algorithm improved and innovated based on the idea of TF-IDF algorithm, the process of TextRank improved algorithms designed and experiments proved the accuracy of the keyword extraction of the improved TextRank algorithm.
{"title":"Research and implementation of keyword extraction algorithm based on professional background knowledge","authors":"Xuekun Zhang, Jing An, Wen Liu","doi":"10.1109/CISP-BMEI.2017.8302332","DOIUrl":"https://doi.org/10.1109/CISP-BMEI.2017.8302332","url":null,"abstract":"With the development of Internet, Data information is growing at an explosive rate. With the era of big data coming, information social value can only be reflected by people's utilization. In the vast amounts of data, keywords as relatively concise summary of the documentation, its can provide efficient information management methods. Keyword extraction technology (KET)can help people get the data information accurately and quickly, so KET is widely used in the information management system. According to the study of keyword extraction method recent years, the classic TF — IDF algorithm and TextRank algorithm were studied in this paper, TextRank algorithm improved and innovated based on the idea of TF-IDF algorithm, the process of TextRank improved algorithms designed and experiments proved the accuracy of the keyword extraction of the improved TextRank algorithm.","PeriodicalId":6474,"journal":{"name":"2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"40 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76595759","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 : 2017-10-01DOI: 10.1109/CISP-BMEI.2017.8302081
Wanyan Wu, G. Shen, Bo Wei, A. Runa, Hongyang Mao, Yazhu Chen
Phased High Intensity Focused Ultrasound (pHIFU) is an efficacious tool toward treating a variety of tumors, where procedure must be performed under Magnetic Resonance (MR) guidance. Because the MR system and the phased High Intensity Focused Ultrasound (pHIFU) system have separated coordinate systems independently, the registration of two coordinate system has to be done. To find the correct coordinate transformation matrix will affect the accuracy of the following treatment. A new method is proposed in this study to calculate the transformation matrix using specially designed markers and corresponding algorithms. Series of markers (>4) on the ultrasound probe are introduced which can be identified in the MR images. As only four markers which are not on the same plane are needed theoretically, algorithms are proposed to handle the problem of information redundancy and also further improve the accuracy of the calculation. The experiments using the proposed methods showed better results than those without using the redundant information.
{"title":"Multi-element ultrasound transducer positioning method in MR-guided phased HIFU system","authors":"Wanyan Wu, G. Shen, Bo Wei, A. Runa, Hongyang Mao, Yazhu Chen","doi":"10.1109/CISP-BMEI.2017.8302081","DOIUrl":"https://doi.org/10.1109/CISP-BMEI.2017.8302081","url":null,"abstract":"Phased High Intensity Focused Ultrasound (pHIFU) is an efficacious tool toward treating a variety of tumors, where procedure must be performed under Magnetic Resonance (MR) guidance. Because the MR system and the phased High Intensity Focused Ultrasound (pHIFU) system have separated coordinate systems independently, the registration of two coordinate system has to be done. To find the correct coordinate transformation matrix will affect the accuracy of the following treatment. A new method is proposed in this study to calculate the transformation matrix using specially designed markers and corresponding algorithms. Series of markers (>4) on the ultrasound probe are introduced which can be identified in the MR images. As only four markers which are not on the same plane are needed theoretically, algorithms are proposed to handle the problem of information redundancy and also further improve the accuracy of the calculation. The experiments using the proposed methods showed better results than those without using the redundant information.","PeriodicalId":6474,"journal":{"name":"2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"284 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76995681","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 : 2017-10-01DOI: 10.1109/CISP-BMEI.2017.8302119
Yu Su
Traffic signal control has become one of the most important topics in this era. Considering the current situation of urban traffic signal control, a single intersection two-stage fuzzy control system is proposed in the paper. The first stage consists of the green light phase traffic intensity module and the red light phase traffic intensity module, the second stage is the decision module. The controller can analyse and estimate traffic situation through gathering the sensor data, determine whether the current signal phase should be extended or terminated. The simulation procedure has been developed by matlab, in contrast, under the same condition, an actuated control method is used in intersection, procedure is also been written. Result of comparison shows that the performance of the two-stage fuzzy method is better than the traffic actuated control, two-stage fuzzy method can decrease vehicle average delay effectively.
{"title":"Urban plane intersection fuzzy control and simulation","authors":"Yu Su","doi":"10.1109/CISP-BMEI.2017.8302119","DOIUrl":"https://doi.org/10.1109/CISP-BMEI.2017.8302119","url":null,"abstract":"Traffic signal control has become one of the most important topics in this era. Considering the current situation of urban traffic signal control, a single intersection two-stage fuzzy control system is proposed in the paper. The first stage consists of the green light phase traffic intensity module and the red light phase traffic intensity module, the second stage is the decision module. The controller can analyse and estimate traffic situation through gathering the sensor data, determine whether the current signal phase should be extended or terminated. The simulation procedure has been developed by matlab, in contrast, under the same condition, an actuated control method is used in intersection, procedure is also been written. Result of comparison shows that the performance of the two-stage fuzzy method is better than the traffic actuated control, two-stage fuzzy method can decrease vehicle average delay effectively.","PeriodicalId":6474,"journal":{"name":"2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"25 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78224498","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 : 2017-10-01DOI: 10.1109/CISP-BMEI.2017.8302079
Fengquan Zhang, Yujie Zhao, Jinhong Li
We design and implement a virtual-real interaction system using the optical motion capture device. First, we construct immersive interaction environment, such as setting OptiTrack parameter, calibrating cameras and transferring data, which play an important role in building environment. Second, we research virtual-real interaction technology based on optical motion capture device. And we acquire the motion data of human and rigid body, transferring them to the virtual scene. So we can accurately rotate and translate the corresponding virtual objects in real time. Finally, we design and implement natural immersive virtual-real interaction system. The experiment result shows that our system can preferably implement virtual-real interaction such as human and rigid body with markers, human and virtual objects.
{"title":"Design and implementation of a virtual-real interaction system","authors":"Fengquan Zhang, Yujie Zhao, Jinhong Li","doi":"10.1109/CISP-BMEI.2017.8302079","DOIUrl":"https://doi.org/10.1109/CISP-BMEI.2017.8302079","url":null,"abstract":"We design and implement a virtual-real interaction system using the optical motion capture device. First, we construct immersive interaction environment, such as setting OptiTrack parameter, calibrating cameras and transferring data, which play an important role in building environment. Second, we research virtual-real interaction technology based on optical motion capture device. And we acquire the motion data of human and rigid body, transferring them to the virtual scene. So we can accurately rotate and translate the corresponding virtual objects in real time. Finally, we design and implement natural immersive virtual-real interaction system. The experiment result shows that our system can preferably implement virtual-real interaction such as human and rigid body with markers, human and virtual objects.","PeriodicalId":6474,"journal":{"name":"2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"125 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75040837","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 : 2017-10-01DOI: 10.1109/CISP-BMEI.2017.8301992
Jiarui Liang, Xiaolin Tian
With the development of the science and space technology, human collected a lot of image data about the lunar terrain. Since the middle of the 20th century, human began landing experiment to the Lunar. So the study of lunar terrain had become a hot topic in recent years. This paper proposed a new automatic recognition algorithm. We introduced the Wavelet domain into the lunar terrain recognition. This new algorithm did Wavelet Transform to Lunar CCD data first, then according to the difference of DWT components, we chose different features to form feature vector. Then we normalized the feature vector, finally we used K-means to cluster in vector space. In order to compared with the existing algorithm fairly, we chose four typical areas: ‘H010’, ‘SI’, ‘Crisium’ and ‘W4’ as testing areas, we compared the recognition rates and Cohen's kappa coefficients with three previous algorithms. The results show that the new algorithm has satisfied results with more faster processing speeds.
{"title":"A fast auto recognition algorithm for lunar terrain in wavelet domain","authors":"Jiarui Liang, Xiaolin Tian","doi":"10.1109/CISP-BMEI.2017.8301992","DOIUrl":"https://doi.org/10.1109/CISP-BMEI.2017.8301992","url":null,"abstract":"With the development of the science and space technology, human collected a lot of image data about the lunar terrain. Since the middle of the 20th century, human began landing experiment to the Lunar. So the study of lunar terrain had become a hot topic in recent years. This paper proposed a new automatic recognition algorithm. We introduced the Wavelet domain into the lunar terrain recognition. This new algorithm did Wavelet Transform to Lunar CCD data first, then according to the difference of DWT components, we chose different features to form feature vector. Then we normalized the feature vector, finally we used K-means to cluster in vector space. In order to compared with the existing algorithm fairly, we chose four typical areas: ‘H010’, ‘SI’, ‘Crisium’ and ‘W4’ as testing areas, we compared the recognition rates and Cohen's kappa coefficients with three previous algorithms. The results show that the new algorithm has satisfied results with more faster processing speeds.","PeriodicalId":6474,"journal":{"name":"2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"1 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77929190","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 : 2017-10-01DOI: 10.1109/CISP-BMEI.2017.8302256
Jun-an Zhang, G. Mo, Ke Zhang
A new type of sleep disorder treatment system based on transcranial microcurrent stimulation has been developed, Which consumes less power. Main technical indexes: the stimulation frequency of 0.5 Hz, 1.5Hz and 100Hz which can be adjusted by the two-phase constant current stimulation, the stimulation current 0–1mA which can be adjusted continuously, and the stimulation pulse width of 50∼1000ms which can be adjusted continuously. The effectiveness of using the system to treat sleep disorder has been confirmed in clinical application. This kind of treatment can avoid side effects caused by drug treatment of insomnia, and the statistical result shows that the effective rate of this system for insomnia treatment is 91.24%.
{"title":"The design and clinical application of sleep disorder treatment system","authors":"Jun-an Zhang, G. Mo, Ke Zhang","doi":"10.1109/CISP-BMEI.2017.8302256","DOIUrl":"https://doi.org/10.1109/CISP-BMEI.2017.8302256","url":null,"abstract":"A new type of sleep disorder treatment system based on transcranial microcurrent stimulation has been developed, Which consumes less power. Main technical indexes: the stimulation frequency of 0.5 Hz, 1.5Hz and 100Hz which can be adjusted by the two-phase constant current stimulation, the stimulation current 0–1mA which can be adjusted continuously, and the stimulation pulse width of 50∼1000ms which can be adjusted continuously. The effectiveness of using the system to treat sleep disorder has been confirmed in clinical application. This kind of treatment can avoid side effects caused by drug treatment of insomnia, and the statistical result shows that the effective rate of this system for insomnia treatment is 91.24%.","PeriodicalId":6474,"journal":{"name":"2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"38 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77979078","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 : 2017-10-01DOI: 10.1109/CISP-BMEI.2017.8302019
Wenxia Bao, Guo-fen Yu, Gensheng Hu, Dong Liang, Shaokui Yan
In order to solve the problem of the traditional image matching algorithm based on the spectral feature, the spectral matching algorithm based on hyperbolic mahalanobis metric is proposed in this paper. The algorithm first introduces a hyperbolic metric that has better adaptability to the sample data. And the hyperbolic mahalanobis metric is defined according to the statistical properties of the data. For a point in a given pointset, the sub point-set is selected according to the hyperbolic mahalanobis metric and the weighted graph of the sub point-set is constructed. The eigenvalue vector and the spectral gap vector are obtained by the singular value decomposition (SVD) of the adjacency matrix of the weighted graph, which construct the hyperbolic mahalanobis metric spectral feature. Finally, the matching matrix is constructed based on the similarity between the hyperbolic mahalanobis metric spectral feature and geometric relations between feature points. Thereby establish the matching mathematical model and introduce the greedy algorithm to obtain the matching results. A large number of experimental results show that the proposed algorithm improves the matching accuracy and the robustness. And the algorithm extends the application range of the spectral matching algorithm.
{"title":"The spectral matching algorithm based on hyperbolic mahalanobis metric","authors":"Wenxia Bao, Guo-fen Yu, Gensheng Hu, Dong Liang, Shaokui Yan","doi":"10.1109/CISP-BMEI.2017.8302019","DOIUrl":"https://doi.org/10.1109/CISP-BMEI.2017.8302019","url":null,"abstract":"In order to solve the problem of the traditional image matching algorithm based on the spectral feature, the spectral matching algorithm based on hyperbolic mahalanobis metric is proposed in this paper. The algorithm first introduces a hyperbolic metric that has better adaptability to the sample data. And the hyperbolic mahalanobis metric is defined according to the statistical properties of the data. For a point in a given pointset, the sub point-set is selected according to the hyperbolic mahalanobis metric and the weighted graph of the sub point-set is constructed. The eigenvalue vector and the spectral gap vector are obtained by the singular value decomposition (SVD) of the adjacency matrix of the weighted graph, which construct the hyperbolic mahalanobis metric spectral feature. Finally, the matching matrix is constructed based on the similarity between the hyperbolic mahalanobis metric spectral feature and geometric relations between feature points. Thereby establish the matching mathematical model and introduce the greedy algorithm to obtain the matching results. A large number of experimental results show that the proposed algorithm improves the matching accuracy and the robustness. And the algorithm extends the application range of the spectral matching algorithm.","PeriodicalId":6474,"journal":{"name":"2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"5 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78473799","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 : 2017-10-01DOI: 10.1109/CISP-BMEI.2017.8301931
Chunting Cai, Gui Feng, Chi Wang, Xue Han
In order to eliminate the error propagation causes by embedding watermark in intra frames, a reversible watermarking algorithm without error propagation for high efficiency video coding (HEVC) is proposed. Firstly, it selects the embedding regions in 4×4 luminance prediction unit (PU) to identify whether it is texture block for low bit rate increasing. Then it uses a novel module for eliminating the error propagation, in which the watermark is embedded in 4×4 quantized discrete sine transform (QDST) coefficients blocks. Lastly, the method of sum invariability is used to embed adaptively watermark in the 4×4 QDST coefficient. The simulation results show that the algorithm has little influence on bit rate and can be well eliminate the error propagation.
{"title":"A reversible watermarking algorithm for high efficiency video coding","authors":"Chunting Cai, Gui Feng, Chi Wang, Xue Han","doi":"10.1109/CISP-BMEI.2017.8301931","DOIUrl":"https://doi.org/10.1109/CISP-BMEI.2017.8301931","url":null,"abstract":"In order to eliminate the error propagation causes by embedding watermark in intra frames, a reversible watermarking algorithm without error propagation for high efficiency video coding (HEVC) is proposed. Firstly, it selects the embedding regions in 4×4 luminance prediction unit (PU) to identify whether it is texture block for low bit rate increasing. Then it uses a novel module for eliminating the error propagation, in which the watermark is embedded in 4×4 quantized discrete sine transform (QDST) coefficients blocks. Lastly, the method of sum invariability is used to embed adaptively watermark in the 4×4 QDST coefficient. The simulation results show that the algorithm has little influence on bit rate and can be well eliminate the error propagation.","PeriodicalId":6474,"journal":{"name":"2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"88 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74976578","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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