Parisa Jahanshahi, Amir Masoud, Eftekhari Moghadam
{"title":"Multi-view tracking using Kalman filter and graph cut","authors":"Parisa Jahanshahi, Amir Masoud, Eftekhari Moghadam","doi":"10.1109/RIOS.2015.7270729","DOIUrl":"https://doi.org/10.1109/RIOS.2015.7270729","url":null,"abstract":"In this paper, we propose a multi-view approach to detect and track based on graph-cut and Kalman filter algorithms to solve this problem. The first, object appears in the scene be detected as foreground in each view using a background model and background difference. Next, for related between cameras used homographic constraint. Any pixel inside the foreground object in every view will be related by homographies inducted by the reference view. reference view Images converted to binary images by a graph-cut segmentation. This step separated the position of the intersection points from other parts inside reference images. This added step significantly reduce false positives and missed detections due to points noise or when it cannot be guaranteed that a single reference view image will consistently by scene objects. To track, We measurement the average position of the points. The kakman filter provides an optimal estimate of its position at each time step. The filter kalman, the first one is the prediction of the next state estimate using the previous one; the second is the correction of that estimate using the measurement. Experimental results with detailed qualitative analysis are demonstrated in challenging multiview crowded scenes.","PeriodicalId":437944,"journal":{"name":"2015 AI & Robotics (IRANOPEN)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121835305","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":"A feature selection method based on minimum redundancy maximum relevance for learning to rank","authors":"Mehrnoush Barani Shirzad, M. Keyvanpour","doi":"10.1109/RIOS.2015.7270735","DOIUrl":"https://doi.org/10.1109/RIOS.2015.7270735","url":null,"abstract":"Learning to rank has considered as a promising approach for ranking in information retrieval. In recent years feature selection for learning to rank introduced as a crucial issue. Reducing the feature set by removing irrelevant and redundant features can improve the prediction performance. In this paper we address the problem of filter feature selection for ranking. We propose to apply minimum redundancy maximum relevance (mRMR) method that select feature subset based on importance of features and similarity between them. We reweight the component of mRMR to balance between importance and similarity. We apply two methods for measuring the similarity between features and two methods for evaluating importance. Experimental results on two standard datasets from Letor demonstrate that the proposed algorithm 1)outperform two stateof- the-art learning to rank algorithms in term of accuracy, 2) learn a more spars model compared to a feature selection model for ranking.","PeriodicalId":437944,"journal":{"name":"2015 AI & Robotics (IRANOPEN)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114931483","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}
S. J. Haddadi, Omid Emamagholi, Farahnaz Javidi, A. Fakharian
{"title":"Attitude control and trajectory tracking of an autonomous miniature aerial vehicle","authors":"S. J. Haddadi, Omid Emamagholi, Farahnaz Javidi, A. Fakharian","doi":"10.1109/RIOS.2015.7270741","DOIUrl":"https://doi.org/10.1109/RIOS.2015.7270741","url":null,"abstract":"This paper introduces a Miniature Aerial Vehicle (MAV) which is Autonomous in outdoor environment. Main contributions of this research are both new trajectory tracking and attitude control scheme in real flight mode. This MAV is based on a traditional quadrotor. For stabilization of the quadrotor's attitude a PID controller is utilized. The proposed controller is designed such that to be able to attenuate effect of external wind disturbance and guarantee stability in this condition. For autonomous trajectory tracking, it is necessary to have a fixed altitude. Also an ARM cortex M4 microcontroller performs processing activities. Then, a trajectory is determined by a GPS in Mission Planner software for the outdoor environment. For real time communication between robot and ground station, HMTR module is used. Flight data is saved in Memory SD card and converts to MATLAB code for real time implementation. Experimental results of the proposed controller on the Autonomous Quadrotor in real conditions show the effectiveness of our approach.","PeriodicalId":437944,"journal":{"name":"2015 AI & Robotics (IRANOPEN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129021502","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":"An efficient hybrid approach based on K-means and generalized fashion algorithms for cluster analysis","authors":"Akram Aghamohseni, R. Ramezanian","doi":"10.1109/RIOS.2015.7270727","DOIUrl":"https://doi.org/10.1109/RIOS.2015.7270727","url":null,"abstract":"Clustering is the process of grouping data objects into set of disjoint classes called clusters so that objects within a class are highly similar with one another and dissimilar with the objects in other classes. The k-means algorithm is a simple and efficient algorithm that is widely used for data clustering. However, its performance depends on the initial state of centroids and may trap in local optima. In order to overcome local optima obstacles, a lot of studies have been done in clustering. The Fashion Algorithm is one effective method for searching problem space to find a near optimal solution. This paper presents a hybrid optimization algorithm based on Generalized Fashion Algorithm and k-means for optimum clustering. The new algorithm is tested on several data sets and its performance is compared with those of Generalized Fashion Algorithm, particle swarm optimization, imperialist competitive algorithm, genetic algorithm and k-means algorithm. The experimental results are encouraging in term of the quality of the solutions and the convergence speed of the proposed algorithm.","PeriodicalId":437944,"journal":{"name":"2015 AI & Robotics (IRANOPEN)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115410258","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":"Adaptive fuzzy-PD controller for 3D walking of biped robots","authors":"Malihe Talebi, M. Farrokhi","doi":"10.1109/RIOS.2015.7270738","DOIUrl":"https://doi.org/10.1109/RIOS.2015.7270738","url":null,"abstract":"This paper proposes a control method for walking of biped robots in three dimensions, while most papers consider only walking in the sagittal plane and consider the frontal stabilization issues. The proposed method employs an adaptive fuzzy-PD control scheme to show the validity of the proposed model. Simulating results show good performance of the model and the control method as compared with recently proposed method in literature.","PeriodicalId":437944,"journal":{"name":"2015 AI & Robotics (IRANOPEN)","volume":"19 12","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120988025","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":"Polynomial based optimal trajectory planning and obstacle avoidance for an omni-directional robot","authors":"Fatemeh Tohfeh, A. Fakharian","doi":"10.1109/RIOS.2015.7270731","DOIUrl":"https://doi.org/10.1109/RIOS.2015.7270731","url":null,"abstract":"An obstacle avoidance trajectory planning for an omni-directional robot is investigated in this paper where obstacles moves with velocity and acceleration constraints. The key issue is an optimized solution for the problem with respect to a cost function which is related to the states and energy consumption. Moreover, the trajectory functions are considered as polynomial functions to obtain desired trajectory. Consequently, this converts the optimal control problem into a small size parameter optimization problem. The low computational cost make this method ideal for trajectory planning in Dynamic environments. The proposed method is simulated and results show its effectiveness in avoidance of collisions with moving obstacles.","PeriodicalId":437944,"journal":{"name":"2015 AI & Robotics (IRANOPEN)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127339309","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}
Mohammad Hossein Bamorovat Abadi, M. A. Oskoei, A. Fakharian
{"title":"Mobile robot navigation using sonar vision algorithm applied to omnidirectional vision","authors":"Mohammad Hossein Bamorovat Abadi, M. A. Oskoei, A. Fakharian","doi":"10.1109/RIOS.2015.7270728","DOIUrl":"https://doi.org/10.1109/RIOS.2015.7270728","url":null,"abstract":"This paper presents a sonar vision algorithm applied to omnidirectional vision. It provides autonomous navigation for a mobile robot in an unknown environment. It uses omnidirectional images without any prior calibration and detects static and dynamic obstacles. It estimates the most intended path based on visual sonar beams in front of the robot. The proposed method was tested on a mobile robot in indoor environment. The experimental results show acceptable performance considering computation costs.","PeriodicalId":437944,"journal":{"name":"2015 AI & Robotics (IRANOPEN)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126095371","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":"Dynamics modeling of planar continuum robots by finite circular elements for motion control","authors":"M. Dehghani, S. Moosavian","doi":"10.1109/RIOS.2015.7270742","DOIUrl":"https://doi.org/10.1109/RIOS.2015.7270742","url":null,"abstract":"Effective dynamics modeling and control of continuum robots have been a challenge for almost a couple of decades. The most important modeling challenge is to provide a model with good precision, which can be solved numerically in a reasonable time. Specifically, it is valuable if an accurate model can be solved in real-time, in order to be used in real-time control implementations. In this paper, using the proposed dynamics model of continuum robots, a model based control is introduced and simulated. This model is based on the assumption of circular arcs (a series of constant-curvature elements). This model is fast and accurate, and can be solved in real-time. Then, the model base controller is designed and simulated for some trajectory tracking tasks. The controller response is analyzed in two cases, one with consideration of model uncertainties, and the other without uncertainties. The results without uncertainties are appropriate, as discussed in the simulation results. However, the controller should be modified to provide better results when uncertainties are applied.","PeriodicalId":437944,"journal":{"name":"2015 AI & Robotics (IRANOPEN)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121599050","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":"Consensus of heterogeneous multi-agent systems using output feedback","authors":"Navid Danaeefard, V. J. Majd","doi":"10.1109/RIOS.2015.7270734","DOIUrl":"https://doi.org/10.1109/RIOS.2015.7270734","url":null,"abstract":"This paper studies the consensus problem for linear multi-agent systems (MASs) under fixed and switching graph topologies. All agents are modeled by general heterogeneous linear systems, and only the output of each agent can be measured. Using a static output feedback controller, consensus problem is formulated as a stability problem with bilinear matrix inequality (BMI) constraints. Finally, Numerical simulations are provided to show the effectiveness of the proposed methods.","PeriodicalId":437944,"journal":{"name":"2015 AI & Robotics (IRANOPEN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129684244","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":"Web query classification using improved visiting probability algorithm and babelnet semantic graph","authors":"Haniyeh Rashidghalam, F. Mahmoudi","doi":"10.1109/RIOS.2015.7270748","DOIUrl":"https://doi.org/10.1109/RIOS.2015.7270748","url":null,"abstract":"In this paper, an unsupervised method which is not use log data is offered to solve ”the problem of web query classification”. The aim of the proposed approach is the mapping of all the problem components to the BabelNet concepts and solving the problem by using these concepts. Therefore, it is considered a three-phase solutions consist of Offline, Online and Classification phases. In offline phase, all categories are mapping to concepts in BabelNet by using a disambiguation system. In the online phase, first a query is enriched then preprocessing on query is required, after that, by using a disambiguation system all components are mapped to BabelNet's concepts. In the last phase, by improving on visiting probability algorithm, classification is done. For testing process, we used KDD2005 test set, which is leading the series have been used. Results indicate that between the approaches which are unsupervised and do not use log data, proposed approach, has acceptable performance in the point of view F1 measure. In other words, by compare to best approach which is unsupervised and does not use log data, proposed approach improved 2%, but by compare to the best approach which is unsupervised and uses log data the results get worse and shows reduction of 11% in term of F1 measure.","PeriodicalId":437944,"journal":{"name":"2015 AI & Robotics (IRANOPEN)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132014269","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}