Pub Date : 2019-12-01DOI: 10.1109/ICICIP47338.2019.9012200
Liang'en Yuan, Tie-shan Li, C. L. P. Chen, Qihe Shan, Min Han
In this paper, a Broad Learning System (BLS)-based learning controller is proposed for course control problem of marine vessels. The training data set of BLS comes from a PID controller, the learning control method is proposed to improve the control performance using the learned knowledge. Simulation studies are performed to demonstrate the proposed scheme can achieve the better control performance with smaller tracking error.
{"title":"Broad Learning System-Based Learning Controller for Course Control of Marine Vessels","authors":"Liang'en Yuan, Tie-shan Li, C. L. P. Chen, Qihe Shan, Min Han","doi":"10.1109/ICICIP47338.2019.9012200","DOIUrl":"https://doi.org/10.1109/ICICIP47338.2019.9012200","url":null,"abstract":"In this paper, a Broad Learning System (BLS)-based learning controller is proposed for course control problem of marine vessels. The training data set of BLS comes from a PID controller, the learning control method is proposed to improve the control performance using the learned knowledge. Simulation studies are performed to demonstrate the proposed scheme can achieve the better control performance with smaller tracking error.","PeriodicalId":431872,"journal":{"name":"2019 Tenth International Conference on Intelligent Control and Information Processing (ICICIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122824929","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 : 2019-12-01DOI: 10.1109/ICICIP47338.2019.9012179
Fan Lin, Zhelong Wang, Debin Shen, Kaida Li, Hongyu Zhao, S. Qiu, Fang Xu
Fire prevention and control had significant meaning for public safety and social development. To realize automatic monitoring of compartment fire, this paper proposed an intelligent indoor fire detection method based on infrared thermal image. The first step in the process was to locate and detect suspicious areas in the infrared image. Then the Principal Component Analysis method was utilized to extract features and reduce the dimension of feature. Finally, a Support Vector Machine classifier was designed and trained to distinguish a potential flame from a fire and a light. Compared with k-nearest neighbor (KNN) classifier, Random Forest(RF) classifier, and Logical Regression(LR) classifier, SVM classifier had better performance. The accuracy rate of SVM classifier in the test set was 99.97%, and the flame recall rate by SVM was 99.996%. Experimental results demonstrated that the flame detection method proposed in this paper had significant detection effect and good application prospects.
{"title":"Intelligent Flame Detection Based on Principal Component Analysis and Support Vector Machine","authors":"Fan Lin, Zhelong Wang, Debin Shen, Kaida Li, Hongyu Zhao, S. Qiu, Fang Xu","doi":"10.1109/ICICIP47338.2019.9012179","DOIUrl":"https://doi.org/10.1109/ICICIP47338.2019.9012179","url":null,"abstract":"Fire prevention and control had significant meaning for public safety and social development. To realize automatic monitoring of compartment fire, this paper proposed an intelligent indoor fire detection method based on infrared thermal image. The first step in the process was to locate and detect suspicious areas in the infrared image. Then the Principal Component Analysis method was utilized to extract features and reduce the dimension of feature. Finally, a Support Vector Machine classifier was designed and trained to distinguish a potential flame from a fire and a light. Compared with k-nearest neighbor (KNN) classifier, Random Forest(RF) classifier, and Logical Regression(LR) classifier, SVM classifier had better performance. The accuracy rate of SVM classifier in the test set was 99.97%, and the flame recall rate by SVM was 99.996%. Experimental results demonstrated that the flame detection method proposed in this paper had significant detection effect and good application prospects.","PeriodicalId":431872,"journal":{"name":"2019 Tenth International Conference on Intelligent Control and Information Processing (ICICIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128040257","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}
Steel sheets have been widely used in the industrial field. With higher requirements for steel production, there is a growing need for factories to produce better quality steel sheets. Conventional steel sheet defect detection methods such as manual inspection are too laborious and inefficient. Therefore, in this paper, we manage to explore a possible solution for steel sheet defect detection and propose a novel image-based processing method. The image processing data enhancement method is used to extend the datasets for further training, then we use the transfer learning technique to train CNNs and extract features on the enhanced image set. A hierarchical model ensemble is applied to detect defects according to their locations. Experiments on enhanced datasets and real-world defect images achieve satisfying accuracy.
{"title":"Steel Sheet Defect Detection Based on Deep Learning Method","authors":"Weizhen Zeng, Zhiyuan You, Mingyue Huang, Zelong Kong, Yikuan Yu, Xinyi Le","doi":"10.1109/ICICIP47338.2019.9012199","DOIUrl":"https://doi.org/10.1109/ICICIP47338.2019.9012199","url":null,"abstract":"Steel sheets have been widely used in the industrial field. With higher requirements for steel production, there is a growing need for factories to produce better quality steel sheets. Conventional steel sheet defect detection methods such as manual inspection are too laborious and inefficient. Therefore, in this paper, we manage to explore a possible solution for steel sheet defect detection and propose a novel image-based processing method. The image processing data enhancement method is used to extend the datasets for further training, then we use the transfer learning technique to train CNNs and extract features on the enhanced image set. A hierarchical model ensemble is applied to detect defects according to their locations. Experiments on enhanced datasets and real-world defect images achieve satisfying accuracy.","PeriodicalId":431872,"journal":{"name":"2019 Tenth International Conference on Intelligent Control and Information Processing (ICICIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127902575","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 : 2019-12-01DOI: 10.1109/ICICIP47338.2019.9012169
Yan-Wei Niu, Qiuhua Lin, Yue Qiu, Li-Dan Kuang, V. Calhoun
Convolutional neural networks (CNN) have exhibited great success in image classification. The application of CNN to classification of patients with brain disorders and healthy controls is also promising using functional magnetic resonance imaging (fMRI) data. However, the shortage of the number of subjects is a challenge for training CNN. Spatial maps separated from the fMRI data by independent component analysis (ICA) can provide a solution to this problem within an ICA-CNN framework. As such, we propose three strategies for both prior to and post ICA sample augmentation in the ICA-CNN framework. More precisely, we propose to increase the number of samples by performing spatial smoothing and band-pass filtering on the observed fMRI data before ICA, and spatial smoothing on the spatial maps after ICA. We evaluate the proposed methods using 82 resting-state fMRI datasets including 42 Schizophrenia patients and 40 healthy controls. The spatial map of the default mode network is used for classification, and each data augmentation is constrained to have the same numbers of samples for a fair comparison. The results show a 2%~15% increase in an average accuracy compared to the existing multiple-model-order method when adopting each of the proposed sample augmentation strategies. The spatial smoothing on the spatial maps is the most accurate among the three proposed methods. When using a combination of the proposed spatial smoothing on the spatial maps with the multiple-model-order method, the average accuracy increases above 90%.
{"title":"Sample Augmentation for Classification of Schizophrenia Patients and Healthy Controls Using ICA of fMRI Data and Convolutional Neural Networks","authors":"Yan-Wei Niu, Qiuhua Lin, Yue Qiu, Li-Dan Kuang, V. Calhoun","doi":"10.1109/ICICIP47338.2019.9012169","DOIUrl":"https://doi.org/10.1109/ICICIP47338.2019.9012169","url":null,"abstract":"Convolutional neural networks (CNN) have exhibited great success in image classification. The application of CNN to classification of patients with brain disorders and healthy controls is also promising using functional magnetic resonance imaging (fMRI) data. However, the shortage of the number of subjects is a challenge for training CNN. Spatial maps separated from the fMRI data by independent component analysis (ICA) can provide a solution to this problem within an ICA-CNN framework. As such, we propose three strategies for both prior to and post ICA sample augmentation in the ICA-CNN framework. More precisely, we propose to increase the number of samples by performing spatial smoothing and band-pass filtering on the observed fMRI data before ICA, and spatial smoothing on the spatial maps after ICA. We evaluate the proposed methods using 82 resting-state fMRI datasets including 42 Schizophrenia patients and 40 healthy controls. The spatial map of the default mode network is used for classification, and each data augmentation is constrained to have the same numbers of samples for a fair comparison. The results show a 2%~15% increase in an average accuracy compared to the existing multiple-model-order method when adopting each of the proposed sample augmentation strategies. The spatial smoothing on the spatial maps is the most accurate among the three proposed methods. When using a combination of the proposed spatial smoothing on the spatial maps with the multiple-model-order method, the average accuracy increases above 90%.","PeriodicalId":431872,"journal":{"name":"2019 Tenth International Conference on Intelligent Control and Information Processing (ICICIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126596932","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 : 2019-12-01DOI: 10.1109/ICICIP47338.2019.9012193
Xiang Wang, Xinxin Wang, Xiu Su, Jianchao Fan, Lin Wang, Qinghui Meng
Spectral features of the green tide of inshore high-suspended sediment waters in the South Yellow Sea of China were analyzed. A Multi-spectral identification coupling filtering algorithm (MIF) for green tide recognition is proposed. The method is applied to three typical areas based on GF-l satellite WFV data and compared with the identification outcomes of VB-FAI, MGTI, IGAG and SABI. Result showed that performance of the MIF and IGAG methods are significantly better than the others in both high-noise and clear seawaters; In high-suspended sediment waters, the MIF method can effectively improve the identification accuracy of green tide about 8%. Meanwhile, the MIF method has a stronger noise suppression capability.
{"title":"Spectral Analysis Based Green Tide Identification in High-suspended Sediment Wasters in South Yellow Sea of China","authors":"Xiang Wang, Xinxin Wang, Xiu Su, Jianchao Fan, Lin Wang, Qinghui Meng","doi":"10.1109/ICICIP47338.2019.9012193","DOIUrl":"https://doi.org/10.1109/ICICIP47338.2019.9012193","url":null,"abstract":"Spectral features of the green tide of inshore high-suspended sediment waters in the South Yellow Sea of China were analyzed. A Multi-spectral identification coupling filtering algorithm (MIF) for green tide recognition is proposed. The method is applied to three typical areas based on GF-l satellite WFV data and compared with the identification outcomes of VB-FAI, MGTI, IGAG and SABI. Result showed that performance of the MIF and IGAG methods are significantly better than the others in both high-noise and clear seawaters; In high-suspended sediment waters, the MIF method can effectively improve the identification accuracy of green tide about 8%. Meanwhile, the MIF method has a stronger noise suppression capability.","PeriodicalId":431872,"journal":{"name":"2019 Tenth International Conference on Intelligent Control and Information Processing (ICICIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124314839","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 : 2019-12-01DOI: 10.1109/ICICIP47338.2019.9012163
Yutong Wang, Yikun Wang, Li Yao, Xiao-jie Zhao
Sleep staging based on electroencephalogram (EEG) signal, as one of the vital bases of study on sleep diagnosis, has been under massive attention. With the spring up of deep learning these years, the idea of combining deep learning structure with automatic sleep staging has been an attractive topic. However, the labeling of sleep stages requires professional knowledge as well as plenty of time, which raise the barrier to evaluate this idea. In this study, the method of unsupervised feature learning based on a mass of unlabeled data and a small number of labeled data was proposed to accomplish sleep staging. The unsupervised feature learning structure was built based on a pair of symmetric convolutional neural networks, with the help of a shallow neural network classifier to classify sleep stages. The results showed that under the condition of the very few labeled data, sleep staging based on unsupervised feature learning can achieve similar accuracy to supervised feature learning, which provides a new direction for the application of deep learning method in dealing with data that is difficult to label or lack of prior knowledge.
{"title":"Single Channel Sleep Staging Based on Unsupervised Feature Learning","authors":"Yutong Wang, Yikun Wang, Li Yao, Xiao-jie Zhao","doi":"10.1109/ICICIP47338.2019.9012163","DOIUrl":"https://doi.org/10.1109/ICICIP47338.2019.9012163","url":null,"abstract":"Sleep staging based on electroencephalogram (EEG) signal, as one of the vital bases of study on sleep diagnosis, has been under massive attention. With the spring up of deep learning these years, the idea of combining deep learning structure with automatic sleep staging has been an attractive topic. However, the labeling of sleep stages requires professional knowledge as well as plenty of time, which raise the barrier to evaluate this idea. In this study, the method of unsupervised feature learning based on a mass of unlabeled data and a small number of labeled data was proposed to accomplish sleep staging. The unsupervised feature learning structure was built based on a pair of symmetric convolutional neural networks, with the help of a shallow neural network classifier to classify sleep stages. The results showed that under the condition of the very few labeled data, sleep staging based on unsupervised feature learning can achieve similar accuracy to supervised feature learning, which provides a new direction for the application of deep learning method in dealing with data that is difficult to label or lack of prior knowledge.","PeriodicalId":431872,"journal":{"name":"2019 Tenth International Conference on Intelligent Control and Information Processing (ICICIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125910710","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 : 2019-12-01DOI: 10.1109/ICICIP47338.2019.9012204
Yao Hu, Ming Yang, B. Wang, Chunxiang Wang, Boya Xu
Automated valet parking technology allows the vehicle to automatically drive into the parking lot and park itself without a human in the vehicle, relieving humans from parking completely. However, the existing automated valet parking system relies on infrastructural intelligence or pre-acquisition of parking lot maps. Given the disadvantages of these researches, this paper proposes a general method for automatic valet parking system based on autonomous exploration. This method depends on no prior knowledge of the parking lot. Our method extracts road structure from perception result using the Voronoi diagram. A multi-factor exploration strategy we proposed is used to generate exploration candidates for autonomous exploration from the road structure. Also, a motion planning method based on the lateral priority of the road guides the vehicle to the candidates while obeys the rules as far as possible. The autonomous exploration will continue until it finds free parking slots or all the spaces in the parking lot are explored. Related experiments have verified the effectiveness of the method presented in this paper.
{"title":"Autonomous Exploration for Automated Valet Parking Based on Road Structure","authors":"Yao Hu, Ming Yang, B. Wang, Chunxiang Wang, Boya Xu","doi":"10.1109/ICICIP47338.2019.9012204","DOIUrl":"https://doi.org/10.1109/ICICIP47338.2019.9012204","url":null,"abstract":"Automated valet parking technology allows the vehicle to automatically drive into the parking lot and park itself without a human in the vehicle, relieving humans from parking completely. However, the existing automated valet parking system relies on infrastructural intelligence or pre-acquisition of parking lot maps. Given the disadvantages of these researches, this paper proposes a general method for automatic valet parking system based on autonomous exploration. This method depends on no prior knowledge of the parking lot. Our method extracts road structure from perception result using the Voronoi diagram. A multi-factor exploration strategy we proposed is used to generate exploration candidates for autonomous exploration from the road structure. Also, a motion planning method based on the lateral priority of the road guides the vehicle to the candidates while obeys the rules as far as possible. The autonomous exploration will continue until it finds free parking slots or all the spaces in the parking lot are explored. Related experiments have verified the effectiveness of the method presented in this paper.","PeriodicalId":431872,"journal":{"name":"2019 Tenth International Conference on Intelligent Control and Information Processing (ICICIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132476145","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 : 2019-12-01DOI: 10.1109/ICICIP47338.2019.9012207
Bowen Zhang, Y. Kong, H. Leung, Shiyu Xing
Unmanned aerial vehicles (UAV) have had significant progress in the last decade, applying to many fields for its convenience to explore areas that men cannot reach and the progress of image processing. Still, as basis to further application, semantic image segmentation is one of the most difficult challenges. In this paper, we propose a method for urban UAV images semantic segmentation, utilizing the geographical information, digital surface models (DSM). We introduce an end-to-end, dual stream fully convolutional networks (FCN) based classifier with DSMs to get the segmentation results, which utilizes the proposed fusion decision strategy instead of the pixel-level classification strategy, along with a short-cut scheme. The experiments show that the proposed structure performs better than state-of-the-art networks in multiple metrics.
{"title":"Urban UAV Images Semantic Segmentation Based on Fully Convolutional Networks with Digital Surface Models","authors":"Bowen Zhang, Y. Kong, H. Leung, Shiyu Xing","doi":"10.1109/ICICIP47338.2019.9012207","DOIUrl":"https://doi.org/10.1109/ICICIP47338.2019.9012207","url":null,"abstract":"Unmanned aerial vehicles (UAV) have had significant progress in the last decade, applying to many fields for its convenience to explore areas that men cannot reach and the progress of image processing. Still, as basis to further application, semantic image segmentation is one of the most difficult challenges. In this paper, we propose a method for urban UAV images semantic segmentation, utilizing the geographical information, digital surface models (DSM). We introduce an end-to-end, dual stream fully convolutional networks (FCN) based classifier with DSMs to get the segmentation results, which utilizes the proposed fusion decision strategy instead of the pixel-level classification strategy, along with a short-cut scheme. The experiments show that the proposed structure performs better than state-of-the-art networks in multiple metrics.","PeriodicalId":431872,"journal":{"name":"2019 Tenth International Conference on Intelligent Control and Information Processing (ICICIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130447138","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 : 2019-12-01DOI: 10.1109/ICICIP47338.2019.9012181
Zhouhua Peng, Baogang Zhang, Oiuvue Sun, Dan Wang, Min Han, Lu Liu, Haoliang Wang
This paper addresses the speed and heading control of under-actuated autonomous surface vehicles (ASVs) subject to model uncertainties and unmeasured states for performance improvement. At first, an extended state observer is developed for estimating unknown system uncertainties, external disturbances as well as unmeasured velocities of surge, sway and yaw. Then, a finite-set model predictive control method is utilized to achieve surge speed and heading stabilization in the presence of model uncertainties. The proposed predictive speed and heading control method is applied to straight-line path following of an ASV, and simulation results show the efficiency of the proposed predictive speed and heading controllers.
{"title":"Finite-set Model Predictive Speed and Heading Control of Autonomous Surface Vehicles with Unmeasured States","authors":"Zhouhua Peng, Baogang Zhang, Oiuvue Sun, Dan Wang, Min Han, Lu Liu, Haoliang Wang","doi":"10.1109/ICICIP47338.2019.9012181","DOIUrl":"https://doi.org/10.1109/ICICIP47338.2019.9012181","url":null,"abstract":"This paper addresses the speed and heading control of under-actuated autonomous surface vehicles (ASVs) subject to model uncertainties and unmeasured states for performance improvement. At first, an extended state observer is developed for estimating unknown system uncertainties, external disturbances as well as unmeasured velocities of surge, sway and yaw. Then, a finite-set model predictive control method is utilized to achieve surge speed and heading stabilization in the presence of model uncertainties. The proposed predictive speed and heading control method is applied to straight-line path following of an ASV, and simulation results show the efficiency of the proposed predictive speed and heading controllers.","PeriodicalId":431872,"journal":{"name":"2019 Tenth International Conference on Intelligent Control and Information Processing (ICICIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120897279","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 : 2019-12-01DOI: 10.1109/ICICIP47338.2019.9012191
Simi Lu, X. Liao, Nankun Mu, Jiahui Wu, Junqing Le
Reversible data hiding(RDH) is a research hotspot in the field of information hiding. Among them, RDH based on histogram shift(HS) is a high performance algorithm. Accurate pixel prediction can reduce image distortion while maintaining high capacity. Therefore, this paper proposes an RDH algorithm based on the improved rhombus prediction method. Experiments show that the improved rhombus prediction method can predict pixels more accurately, and the generated prediction error histogram is more compact and clear. The proposed RDH algorithm has a higher embedding capacity and a lower distortion rate.
{"title":"Reversible Data Hiding Based on Improved Rhombus Prediction Method","authors":"Simi Lu, X. Liao, Nankun Mu, Jiahui Wu, Junqing Le","doi":"10.1109/ICICIP47338.2019.9012191","DOIUrl":"https://doi.org/10.1109/ICICIP47338.2019.9012191","url":null,"abstract":"Reversible data hiding(RDH) is a research hotspot in the field of information hiding. Among them, RDH based on histogram shift(HS) is a high performance algorithm. Accurate pixel prediction can reduce image distortion while maintaining high capacity. Therefore, this paper proposes an RDH algorithm based on the improved rhombus prediction method. Experiments show that the improved rhombus prediction method can predict pixels more accurately, and the generated prediction error histogram is more compact and clear. The proposed RDH algorithm has a higher embedding capacity and a lower distortion rate.","PeriodicalId":431872,"journal":{"name":"2019 Tenth International Conference on Intelligent Control and Information Processing (ICICIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130288788","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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