S. Mousavi, Razieh Kazemi, M. Saadatmand, Abbas Ebrahimi Moghadam
{"title":"A New Method for Pupil Detection in Gaze-Point Estimation Systems Based on Active Contours","authors":"S. Mousavi, Razieh Kazemi, M. Saadatmand, Abbas Ebrahimi Moghadam","doi":"10.1109/CFIS49607.2020.9238677","DOIUrl":"https://doi.org/10.1109/CFIS49607.2020.9238677","url":null,"abstract":"Eye tracking and gaze-point estimation has increasing applications in the field of human-machine interface. Although so far a number of gaze-point estimation algorithms were investigated by researchers, video-based methods can be counted as the most important and efficient category in which eye features are obtained by processing of eye images. One of the most important factors affecting on the accuracy of gaze-point estimation is high-accurate extraction of pupil boundary. In this paper, a new method based on active contours is proposed for pupil boundary extraction. Active contours are among the conventional and useful methods for image segmentation. Generally, deformable models are curves that can evolve in order to minimize the internal and external energies in image domain. The internal energy keeps the curve smooth and differentiable, while the external energy directs the curve to the desired properties. Experimental results demonstrated suitable performance of the proposed method for a number of benchmark eye-images. Also, we used our method in an eye-tracker system for pupil segmentation. Significantly good performance of that system compared to a number of other eye-trackers can be counted as another concrete evidence for high solution quality of our method.","PeriodicalId":128323,"journal":{"name":"2020 8th Iranian Joint Congress on Fuzzy and intelligent Systems (CFIS)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114570482","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}
{"title":"Domain adaptation based on incremental adversarial learning","authors":"Hamideh Khadempir, F. Afsari, E. Rashedi","doi":"10.1109/CFIS49607.2020.9238758","DOIUrl":"https://doi.org/10.1109/CFIS49607.2020.9238758","url":null,"abstract":"Domain adaptation is a method of transfer learning. Domain adaptation has a source domain and target domain with related but different distributions. Unsupervised domain adaptation could be a scenario wherever we've labeled unlabeled target data and source data. In this paper, an incremental adversarial learning method is proposed for unsupervised domain adaptation. In this work, the unknown target labels are predicted and according to these estimated labels, some target data with more similarity to the source data are added to the source data to improve the adaptation between two domains. We use the adversarial discriminative approach as the base unsupervised domain adaptation technique. We do this to handle the large domain shift between the source and target domain distributions. Experimental reports prove that our approach performs much better on several visual domain adaptation tasks.","PeriodicalId":128323,"journal":{"name":"2020 8th Iranian Joint Congress on Fuzzy and intelligent Systems (CFIS)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116933896","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}
{"title":"Full-Car Active Suspension System Identification Using Flexible Deep Neural Network","authors":"Amirsaeid Safari, S. Mehralian, M. Teshnehlab","doi":"10.1109/CFIS49607.2020.9238686","DOIUrl":"https://doi.org/10.1109/CFIS49607.2020.9238686","url":null,"abstract":"This paper presents the system identification based on a flexible deep neural network for a seven degree of freedom(7DOF), a full-car active suspension system that is multi-input and multi-output. The proposed flexible deep neural network, according to input and output data, obtained three layers of flexible auto-encoder. The flexible name was chosen for the learnable activation function parameter in the activation layers. This view permits every neuron to adjust its activation function and adapt the neuron to boost performance. Here flexible tanh activation function introduced, which causes better performance with the same neurons in the hidden layer. The comparison shows the identification error between flexible deep neural network and classical deep neural network. This adaptation, of course, provides prediction improvement.","PeriodicalId":128323,"journal":{"name":"2020 8th Iranian Joint Congress on Fuzzy and intelligent Systems (CFIS)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117034176","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}
{"title":"Nonlinear Attitude Control of Satellite Using Optimal Adaptive and Fuzzy Control Methods","authors":"M. Navabi, N. S. Hashkavaei","doi":"10.1109/CFIS49607.2020.9238733","DOIUrl":"https://doi.org/10.1109/CFIS49607.2020.9238733","url":null,"abstract":"Several different control theories have been designed for the problem of the satellite attitude control. According to presence of uncertainties in the space missions, adaptive control techniques are useful. In this paper, the satellite attitude control problem is studied using a novel optimal adaptive controller. The new control method is developed based on rotation matrices. This optimized adaptive controller uses Markov parameters. The controller is a direct adaptive controller which is applicable for following the control command. Also, for comparison, fuzzy control of satellite attitude is included. Finally, the results of simulations indicate that the desired conditions are achieved by the satellite, and the response of the system utilizing the fuzzy control method is faster than when the mentioned adaptive controller is used.","PeriodicalId":128323,"journal":{"name":"2020 8th Iranian Joint Congress on Fuzzy and intelligent Systems (CFIS)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128693013","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}