{"title":"Persian heritage image binarization competition (PHIBC 2012)","authors":"Seyed Morteza Ayatollahi, Hossein Ziaei Nafchi","doi":"10.1109/PRIA.2013.6528442","DOIUrl":"https://doi.org/10.1109/PRIA.2013.6528442","url":null,"abstract":"The first competition on the binarization of historical Persian documents and manuscripts (PHIBC 2012) has been organized in conjunction with the first Iranian conference on pattern recognition and image analysis (PRIA 2013). The main objective of PHIBC 2012 is to evaluate performance of the binarization methodologies, when applied on the Persian heritage images. This paper provides a report on the methodology and performance of the three submitted algorithms based on evaluation measures has been used.","PeriodicalId":370476,"journal":{"name":"2013 First Iranian Conference on Pattern Recognition and Image Analysis (PRIA)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127615043","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":"Application of Multi-Objective optimization algorithm and Artificial Neural Networks at machining process","authors":"F. Jafarian, H. Amirabadi, J. Sadri","doi":"10.1109/PRIA.2013.6528449","DOIUrl":"https://doi.org/10.1109/PRIA.2013.6528449","url":null,"abstract":"Since, experimentally investigation of machining processes is difficult and costly, the problem becomes more difficult if the aim is simultaneously optimization of the machining outputs. This paper presents a novel hybrid method based on the Artificial Neural networks (ANNs), Multi-Objective Optimization (MOO) and Finite Element Method (FEM) for evaluation of thermo-mechanical loads during turning process. After calibrating controllable parameters of simulation by comparison between FE results and experimental results of literature, the results of FE simulation were employed for training neural networks by Genetic algorithm. Finally, the functions implemented by neural networks were considered as objective functions of Non-Dominated Genetic Algorithm (NSGA-II) and optimal non-dominated solution set were determined at the different states of thermo-mechanical loads. Comparison between obtained results of NSGA-II and predicted results of FE simulation showed that, developed hybrid technique of FEM-ANN-MOO in this study provides a robust framework for manufacturing processes.","PeriodicalId":370476,"journal":{"name":"2013 First Iranian Conference on Pattern Recognition and Image Analysis (PRIA)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124613105","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 method for vehicle classification and resolving vehicle occlusion in traffic images","authors":"V. Heidari, M. Ahmadzadeh","doi":"10.1109/PRIA.2013.6528435","DOIUrl":"https://doi.org/10.1109/PRIA.2013.6528435","url":null,"abstract":"This paper presents a new method to classify vehicles with resolving vehicle occlusions in traffic images. Moving objects are detected in an image sequence using a probability-based background extraction and object segmentation algorithm. The partially occluded vehicles are detected by evaluating the convexity of the moving objects and split by the so-called “dividing line” of the occlusion region. Then the divided objects are classified by evaluating their normalized size. If the object is not partially occluded, its width and the ratio between length and width is extracted to detect if it is a full occlusion and classify it by developing a hierarchical classifier. The proposed method has been evaluated to see if the results are satisfying. Experimental results exhibit that the method is efficiently able to classify vehicles and process occlusions.","PeriodicalId":370476,"journal":{"name":"2013 First Iranian Conference on Pattern Recognition and Image Analysis (PRIA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122866106","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":"Isolated dynamic Persian sign language recognition based on camshift algorithm and radon transform","authors":"H. Madani, M. Nahvi","doi":"10.1109/PRIA.2013.6528452","DOIUrl":"https://doi.org/10.1109/PRIA.2013.6528452","url":null,"abstract":"Sign language is the initial tool for communication of deaf people in their everyday life. A lot of attention has recently been assigned to sign language recognition (SLR) by researchers in various domains such as computer vision, image processing and pattern recognition. Sign language gestures are divided in two groups, static and dynamic. The former includes the alphabets and the latter presents particular concepts. This paper presents a system for recognizing Persian sign language (PSL) in color video sequences. The system includes three main parts: tracking hand using continuously adaptive mean-shift (CAMSHIFT) algorithm, feature extraction using radon transform and discrete cosine transform (DCT). Finally to evaluate the impact of feature extraction technique on recognition rate, four different classifiers include minimum distance (MD), K-nearest neighbor (KNN), neural network (NN), and support vector machine (SVM) are used. The experimental results show that the suggested system is successfully able to recognize Persian gestures.","PeriodicalId":370476,"journal":{"name":"2013 First Iranian Conference on Pattern Recognition and Image Analysis (PRIA)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121843464","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}