Pub Date : 2020-03-01DOI: 10.1109/CTISC49998.2020.00011
Wei Wang, Yong Yue
The human eye is a complex and precise optical imaging system. The refractive surgeries, e.g. LASIK, aim to correct the refractive error of the human eye. An important precondition of refractive surgeries is to model the human eye. The human eye model is used to simulate the human vision to guide and evaluate refractive surgeries. Especially, the defocus blur that is caused by the refractive error is considered in the human vision simulation. However, the asymmetry and irregularity of eyeball shape and optical media make it impossible to model the human eye accurately. This inaccuracy in the modelling of the human eye makes it is difficult to perform the surgeries accurately with several surgical complications, e.g. the halo and vision acuity in dark environments. In addition, the current studies of human eye modelling are for the human eye with normal vision. The main reason is the complex causes of pathological vision in pathological human eye modelling, e.g. the abnormal length of the optical axis and the abnormal dioptre of the cornea in myopia and hyperopia. This study proposes an improved schematic human eye model to achieve human eye modelling and simulation. The different length of the optical axis and the change of corneal thickness are considered in this human eye model. The main contributions of this study are to achieve the personalised modelling of the human eye with pathological vision and provide the visual quality evaluation of refractive surgeries.
{"title":"An Improved Schematic Human Eye Model for Human Vision Simulation","authors":"Wei Wang, Yong Yue","doi":"10.1109/CTISC49998.2020.00011","DOIUrl":"https://doi.org/10.1109/CTISC49998.2020.00011","url":null,"abstract":"The human eye is a complex and precise optical imaging system. The refractive surgeries, e.g. LASIK, aim to correct the refractive error of the human eye. An important precondition of refractive surgeries is to model the human eye. The human eye model is used to simulate the human vision to guide and evaluate refractive surgeries. Especially, the defocus blur that is caused by the refractive error is considered in the human vision simulation. However, the asymmetry and irregularity of eyeball shape and optical media make it impossible to model the human eye accurately. This inaccuracy in the modelling of the human eye makes it is difficult to perform the surgeries accurately with several surgical complications, e.g. the halo and vision acuity in dark environments. In addition, the current studies of human eye modelling are for the human eye with normal vision. The main reason is the complex causes of pathological vision in pathological human eye modelling, e.g. the abnormal length of the optical axis and the abnormal dioptre of the cornea in myopia and hyperopia. This study proposes an improved schematic human eye model to achieve human eye modelling and simulation. The different length of the optical axis and the change of corneal thickness are considered in this human eye model. The main contributions of this study are to achieve the personalised modelling of the human eye with pathological vision and provide the visual quality evaluation of refractive surgeries.","PeriodicalId":266384,"journal":{"name":"2020 2nd International Conference on Advances in Computer Technology, Information Science and Communications (CTISC)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134212921","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 : 2020-03-01DOI: 10.1109/ctisc49998.2020.00007
T. Rahman
William (Michael) Pace, Texas A&M University, USA Francesco Colace, University of Salerno, Itlay Bok-Min Goi (SMIEEE), Universiti Tunku Abdul Rahman (UTAR), Malaysia Emanuel S. Grant, University of North Dakota, US Hosam El-Ocla (SMIEEE), Lakehead University, Canada Yung-Hui Li, National Central University, Taiwan Wai Lam Hoo, University of Malaya, Malaysia Jain-shing Liu, Providence University, Taiwan Xin Lou, Advanced Digital Sciences Center, Singapore Muhammad Roil Bilad, Universiti Teknologi Petronas, Malaysia Hussain Al-Aqrabi, University of Huddersfield, UK Bohumil Brtník, University of Pardubice, Czech Republic Zainb Dawod, Brunel University London, UK Cathryn Peoples, The Open University, UK Ahmad El-Banna, Benha University, Egypt Zakariya Chabani, Istanbul University, Turkey Seppo Sirkemaa, University of Turku, Finland Juryon Paik, Pyeongtaek University, South Korea Anas M.R. AlSobeh, Yarmouk University, Jordan Shamsul Jamel Elias, Universiti Teknologi MARA, Malaysia Syed Farooq Ali, University of Management and Technology, Pakistan Hadi Sutopo, Kalbis Institute, Indonesia Turi, Michael, California State University, Fullerton, USA Anastasia Anagnostou, Brunel University London, UK Ping Guo, University of Illinois at Springfield, USA Sohaib Majzoub, University of Sharjah, UAE Shadi Atalla, University of Dubai, UAE Xiaochen Yuan, Macau University of Science and Technology, Macau, China Bo-Hao Chen, Yuan Ze University, Taiwan Jiankang Ren, Dalian University of Technology, China Hamed Sarbazhosseini, University of Canberra, Australia Muhammad Asif Khan, Qatar University, Qatar Bin Xue, National University of Defense Technology, China Yao Tong, Tokyo University of Science, Japan Sun-Yuan Hsieh, National Cheng Kung University, Taiwan Shigeo Akashi, Tokyo University of Science, Japan Pascal Lorenz, University of Haute-Alsace, France Nobuo Funabiki, Okayama University, Japan Yong Yue, Xi'an Jiaotong-Liverpool University, China Craig, Xi'an Jiaotong-Liverpool University, China Yang Ming-Hour, Chung Yuan Christian University, Taiwan Wai Lok Woo (SMIEEE), Northumbria University, UK Nicolas H. Younan, Mississippi State University, USA Li, Dong, Macau University of Science and Technology, Macau, China Gai-Ge Wang, Ocean University of China, China Sule Yildirim Yayilgan, Norwegian University of Science and Technology, Norway Shahzad Ashraf, Hohai University, China Valentin Montmirail, Avisto Telecom, IIoT Solutions Team, Vallauris, France Hadi Sutopo, Kalbis Institute, Indonesia
{"title":"CTISC 2020 List Reviewer Page","authors":"T. Rahman","doi":"10.1109/ctisc49998.2020.00007","DOIUrl":"https://doi.org/10.1109/ctisc49998.2020.00007","url":null,"abstract":"William (Michael) Pace, Texas A&M University, USA Francesco Colace, University of Salerno, Itlay Bok-Min Goi (SMIEEE), Universiti Tunku Abdul Rahman (UTAR), Malaysia Emanuel S. Grant, University of North Dakota, US Hosam El-Ocla (SMIEEE), Lakehead University, Canada Yung-Hui Li, National Central University, Taiwan Wai Lam Hoo, University of Malaya, Malaysia Jain-shing Liu, Providence University, Taiwan Xin Lou, Advanced Digital Sciences Center, Singapore Muhammad Roil Bilad, Universiti Teknologi Petronas, Malaysia Hussain Al-Aqrabi, University of Huddersfield, UK Bohumil Brtník, University of Pardubice, Czech Republic Zainb Dawod, Brunel University London, UK Cathryn Peoples, The Open University, UK Ahmad El-Banna, Benha University, Egypt Zakariya Chabani, Istanbul University, Turkey Seppo Sirkemaa, University of Turku, Finland Juryon Paik, Pyeongtaek University, South Korea Anas M.R. AlSobeh, Yarmouk University, Jordan Shamsul Jamel Elias, Universiti Teknologi MARA, Malaysia Syed Farooq Ali, University of Management and Technology, Pakistan Hadi Sutopo, Kalbis Institute, Indonesia Turi, Michael, California State University, Fullerton, USA Anastasia Anagnostou, Brunel University London, UK Ping Guo, University of Illinois at Springfield, USA Sohaib Majzoub, University of Sharjah, UAE Shadi Atalla, University of Dubai, UAE Xiaochen Yuan, Macau University of Science and Technology, Macau, China Bo-Hao Chen, Yuan Ze University, Taiwan Jiankang Ren, Dalian University of Technology, China Hamed Sarbazhosseini, University of Canberra, Australia Muhammad Asif Khan, Qatar University, Qatar Bin Xue, National University of Defense Technology, China Yao Tong, Tokyo University of Science, Japan Sun-Yuan Hsieh, National Cheng Kung University, Taiwan Shigeo Akashi, Tokyo University of Science, Japan Pascal Lorenz, University of Haute-Alsace, France Nobuo Funabiki, Okayama University, Japan Yong Yue, Xi'an Jiaotong-Liverpool University, China Craig, Xi'an Jiaotong-Liverpool University, China Yang Ming-Hour, Chung Yuan Christian University, Taiwan Wai Lok Woo (SMIEEE), Northumbria University, UK Nicolas H. Younan, Mississippi State University, USA Li, Dong, Macau University of Science and Technology, Macau, China Gai-Ge Wang, Ocean University of China, China Sule Yildirim Yayilgan, Norwegian University of Science and Technology, Norway Shahzad Ashraf, Hohai University, China Valentin Montmirail, Avisto Telecom, IIoT Solutions Team, Vallauris, France Hadi Sutopo, Kalbis Institute, Indonesia","PeriodicalId":266384,"journal":{"name":"2020 2nd International Conference on Advances in Computer Technology, Information Science and Communications (CTISC)","volume":"135 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114093088","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 : 2020-03-01DOI: 10.1109/CTISC49998.2020.00016
Fan Bu, Dan Li
Aiming at the defects that the traditional Census algorithm uses a fixed window and a fixed threshold to cause the image to have discontinuous depths and low matching accuracy in weak texture regions, an improvement is proposed. The cost computation phase uses SAD-Census algorithm, and proposes a new type of adaptive window method. The gradient information is used to dynamically select the threshold value to realize the selection of the window, and the Census cost computation is optimized. Consider the whole picture, Complete cost aggregation at multiple scales based on minimum spanning tree(MST); introduce left and right consistency detection methods to detect mismatched points in occluded areas, smooth the image through singular point filling and median filtering, and improve the overall accuracy of the improved algorithm. Using Middlebury dataset for testing, the experimental results show that the improved algorithm proposed in this paper has significantly improved matching accuracy and robustness compared with traditional algorithms, especially in areas with deep discontinuities and weak textures.
{"title":"Research on Improved Census Binocular Stereo Matching Algorithm","authors":"Fan Bu, Dan Li","doi":"10.1109/CTISC49998.2020.00016","DOIUrl":"https://doi.org/10.1109/CTISC49998.2020.00016","url":null,"abstract":"Aiming at the defects that the traditional Census algorithm uses a fixed window and a fixed threshold to cause the image to have discontinuous depths and low matching accuracy in weak texture regions, an improvement is proposed. The cost computation phase uses SAD-Census algorithm, and proposes a new type of adaptive window method. The gradient information is used to dynamically select the threshold value to realize the selection of the window, and the Census cost computation is optimized. Consider the whole picture, Complete cost aggregation at multiple scales based on minimum spanning tree(MST); introduce left and right consistency detection methods to detect mismatched points in occluded areas, smooth the image through singular point filling and median filtering, and improve the overall accuracy of the improved algorithm. Using Middlebury dataset for testing, the experimental results show that the improved algorithm proposed in this paper has significantly improved matching accuracy and robustness compared with traditional algorithms, especially in areas with deep discontinuities and weak textures.","PeriodicalId":266384,"journal":{"name":"2020 2nd International Conference on Advances in Computer Technology, Information Science and Communications (CTISC)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124094971","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 : 2020-03-01DOI: 10.1109/CTISC49998.2020.00025
Hao Ge, X. Tu, W. Ai, Yao Luo, Zheng Ma, M. Xie
Spatio-temporal information is very important to capture the discriminative cues between genuine and fake faces from video sequences. To explore such a temporal feature, the fine-grained motions (e.g., eye blinking, mouth movements and head swing) across video frames are very critical. In this paper, we propose a joint CNN-LSTM network for face anti-spoofing, focusing on the motion cues across video frames. We first extract the high discriminative features of video frames using the conventional Convolutional Neural Network (CNN). Then we leverage Long Short-Term Memory (LSTM) with the extracted features as inputs to capture the temporal dynamics in videos. To ensure the fine-grained motions more easily to be perceived in the training process, the eulerian motion magnification is used as the preprocessing to enhance the facial expressions exhibited by individuals, and the attention mechanism is embedded in LSTM to ensure the model learn to focus selectively on the dynamic frames across the video clips. Experiments on MSU-MFSD and Replay Attack databases show that the proposed method yields state-of-the-art performance with better generalization ability compared with several other popular algorithms.
{"title":"Face Anti-Spoofing by the Enhancement of Temporal Motion","authors":"Hao Ge, X. Tu, W. Ai, Yao Luo, Zheng Ma, M. Xie","doi":"10.1109/CTISC49998.2020.00025","DOIUrl":"https://doi.org/10.1109/CTISC49998.2020.00025","url":null,"abstract":"Spatio-temporal information is very important to capture the discriminative cues between genuine and fake faces from video sequences. To explore such a temporal feature, the fine-grained motions (e.g., eye blinking, mouth movements and head swing) across video frames are very critical. In this paper, we propose a joint CNN-LSTM network for face anti-spoofing, focusing on the motion cues across video frames. We first extract the high discriminative features of video frames using the conventional Convolutional Neural Network (CNN). Then we leverage Long Short-Term Memory (LSTM) with the extracted features as inputs to capture the temporal dynamics in videos. To ensure the fine-grained motions more easily to be perceived in the training process, the eulerian motion magnification is used as the preprocessing to enhance the facial expressions exhibited by individuals, and the attention mechanism is embedded in LSTM to ensure the model learn to focus selectively on the dynamic frames across the video clips. Experiments on MSU-MFSD and Replay Attack databases show that the proposed method yields state-of-the-art performance with better generalization ability compared with several other popular algorithms.","PeriodicalId":266384,"journal":{"name":"2020 2nd International Conference on Advances in Computer Technology, Information Science and Communications (CTISC)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122166306","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 : 2020-03-01DOI: 10.1109/CTISC49998.2020.00015
Binbin Fan, Weihong Wan, Dai Zhang, Jianfeng Hou, Fangsheng Li
The procedure of self-differential detection in free space optical (FSO) communication is analyzed in our research, based on which, analytic expressions of the detected signals are derived. According to the derived expressions, low-order wavefront aberration would still exist after transmitted through self-differential interference structure, meanwhile, detected phases would be easily affected by noises from the detectors under a low signal-to-noise ratio (SNR) condition. Aimed at solving above problems, improved K-means clustering algorithm is designed and applied in our research. Simulation results show that the proposed method could successfully improve the quality of phase retrieval for M-DPSK modulation under circumstances of different SNR and transmission rates. Moreover, adaptive communication could be achieved through calculating loss function based on the rule of fuzzy clustering.
{"title":"Phase retrieval of M-DPSK based on improved K-means clustering algorithm","authors":"Binbin Fan, Weihong Wan, Dai Zhang, Jianfeng Hou, Fangsheng Li","doi":"10.1109/CTISC49998.2020.00015","DOIUrl":"https://doi.org/10.1109/CTISC49998.2020.00015","url":null,"abstract":"The procedure of self-differential detection in free space optical (FSO) communication is analyzed in our research, based on which, analytic expressions of the detected signals are derived. According to the derived expressions, low-order wavefront aberration would still exist after transmitted through self-differential interference structure, meanwhile, detected phases would be easily affected by noises from the detectors under a low signal-to-noise ratio (SNR) condition. Aimed at solving above problems, improved K-means clustering algorithm is designed and applied in our research. Simulation results show that the proposed method could successfully improve the quality of phase retrieval for M-DPSK modulation under circumstances of different SNR and transmission rates. Moreover, adaptive communication could be achieved through calculating loss function based on the rule of fuzzy clustering.","PeriodicalId":266384,"journal":{"name":"2020 2nd International Conference on Advances in Computer Technology, Information Science and Communications (CTISC)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131505141","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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