2017 3rd International Conference on Pattern Recognition and Image Analysis (IPRIA)最新文献_第5页

Nonlinear analysis of video images using deep recurrent auto-associative neural networks for facial understanding 基于深度递归自关联神经网络的视频图像非线性分析

2017 3rd International Conference on Pattern Recognition and Image Analysis (IPRIA) Pub Date : 2017-04-01 DOI: 10.1109/PRIA.2017.7983050

S. M. Moghadam, S. Seyyedsalehi

引用次数: 4

A multi-objective component-substitution-based pansharpening 基于多目标部件替换的泛锐化

2017 3rd International Conference on Pattern Recognition and Image Analysis (IPRIA) Pub Date : 2017-04-01 DOI: 10.1109/PRIA.2017.7983056

Ghassem Khademi, H. Ghassemian

引用次数: 16

A new active contour model for tumor segmentation 一种新的肿瘤分割活动轮廓模型

2017 3rd International Conference on Pattern Recognition and Image Analysis (IPRIA) Pub Date : 2017-04-01 DOI: 10.1109/PRIA.2017.7983053

Maryam Taghizadeh Dehkordi

引用次数: 4

Coarse-to-fine parameter tuning for content-based object categorization 基于内容的对象分类从粗到精的参数调优

2017 3rd International Conference on Pattern Recognition and Image Analysis (IPRIA) Pub Date : 2017-04-01 DOI: 10.1109/PRIA.2017.7983038

Vahid Moshkelgosha, Hamed Behzadi-Khormouji, M. Yazdian-Dehkordi

引用次数: 2

Persian logo recognition using local binary patterns 波斯语标志识别使用本地二进制模式

2017 3rd International Conference on Pattern Recognition and Image Analysis (IPRIA) Pub Date : 2017-04-01 DOI: 10.1109/PRIA.2017.7983058

Afsoon Asghari Shirazi, A. Dehghani, H. Farsi, M. Yazdi

{"title":"Persian logo recognition using local binary patterns","authors":"Afsoon Asghari Shirazi, A. Dehghani, H. Farsi, M. Yazdi","doi":"10.1109/PRIA.2017.7983058","DOIUrl":"https://doi.org/10.1109/PRIA.2017.7983058","url":null,"abstract":"Nowadays, image processing is getting more popular due to the daily increase of diverse data acquisition methods such as digital scanners and cameras. Due to the high volume of archived documents, automatic document classification methods can help to save the time and space in digital document organization. Logos in official and business documents are used to identify document identities. Different approaches have been used for logo recognition yet, many of which has complex computations to achieve a high level of precision. In this paper, a novel algorithm for accurate logo recognition with low level of computational complexity is proposed based on Local Binary Pattern (LBP). We proposed PerLogo dataset consisting 850 images of 10 different classes of logos has been proposed in this paper. Through 3 separate experiments over 50, 60, 70 images per each class the proposed system has been evaluated. Experimental results show that recognition rate is increased with increasing the number of training images per class. Experimental results show the recognition accuracy of 98% when 0.09 salt and pepper noise are added to the test images, which is more than 95% accuracy proposed by the state-of-the-art approaches achieving 95% accuracy.","PeriodicalId":336066,"journal":{"name":"2017 3rd International Conference on Pattern Recognition and Image Analysis (IPRIA)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127760425","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}

引用次数: 4

Classification of power quality events using deep learning on event images 在事件图像上使用深度学习的电能质量事件分类

2017 3rd International Conference on Pattern Recognition and Image Analysis (IPRIA) Pub Date : 1900-01-01 DOI: 10.1109/PRIA.2017.7983049

Ebrahim Balouji, O. Salor

{"title":"Classification of power quality events using deep learning on event images","authors":"Ebrahim Balouji, O. Salor","doi":"10.1109/PRIA.2017.7983049","DOIUrl":"https://doi.org/10.1109/PRIA.2017.7983049","url":null,"abstract":"In this paper, a new method for the classification of power quality (PQ) events of the electricity networks based on deep learning approach is presented. In contrast with the existing PQ event data analysis techniques, sampled voltage data of the PQ events are not used, but image files of the three-phase PQ event data are analyzed by taking the advantage of the success of the deep leaning approach on image-file-classification. Therefore, the novelty of the proposed approach is that, image files of the voltage waveforms of the three phases of the power grid are classified. PQ events obtained from four transformer substations of the electricity transmission system for a year are used for training and testing the proposed classification method. DIGITS deep learning platform of NVIDIA is employed for the application of the deep learning algorithm on PQ event data images. It is shown that the test data can be classified with 100% accuracy. The proposed work is believed to serve the needs of the future smart grid applications, which are fast and automatic analysis of the electricity grid and taking automatic countermeasures against potential PQ events.","PeriodicalId":336066,"journal":{"name":"2017 3rd International Conference on Pattern Recognition and Image Analysis (IPRIA)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133685559","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}

引用次数: 38