2013 First Iranian Conference on Pattern Recognition and Image Analysis (PRIA)最新文献_第2页

An Improved soft decision method in Viterbi decoder using artificial neural networks 一种改进的基于人工神经网络的Viterbi解码器软决策方法

2013 First Iranian Conference on Pattern Recognition and Image Analysis (PRIA) Pub Date : 2013-03-06 DOI: 10.1109/PRIA.2013.6528456

Qolamreza Nadalinia Charei, H. F. Aghamalek, S. Razavi, M. Golestanian

引用次数: 10

Intelligent mineral identification using clustering and artificial neural networks techniques 基于聚类和人工神经网络技术的智能矿物识别

2013 First Iranian Conference on Pattern Recognition and Image Analysis (PRIA) Pub Date : 2013-03-06 DOI: 10.1109/PRIA.2013.6528426

H. Izadi, J. Sadri, Nosrat-Agha Mehran

{"title":"Intelligent mineral identification using clustering and artificial neural networks techniques","authors":"H. Izadi, J. Sadri, Nosrat-Agha Mehran","doi":"10.1109/PRIA.2013.6528426","DOIUrl":"https://doi.org/10.1109/PRIA.2013.6528426","url":null,"abstract":"Identifying of minerals in petrographic thin sections is done by mineralogist using polarized microscope rotation stage. Mineral identification will be a tedious work if the number of thin sections is large; this may cause some errors in final identification. Therefore, in this study, artificial neural networks (ANNs) are utilized for mineral identification. ANNs inspired by neural activities of humans have been widely being used in myriad fields of science, they are capable of estimating complex non-linear functions. Digital images are captured from every thin section, by plane-polarized and cross-polarized lights that yield twelve features (red, green, blue, hue, saturation and intensity in two states of lights) for identification of minerals. The first six features are related to plane-polarized light and the rest are related to cross-polarize light. Then, extracted features are fed into the ANN as inputs, which has been trained therefore minerals will be recognized. The high accuracy and precision of minerals identification in this study, have given the proposed intelligent system remarkable capabilities.","PeriodicalId":370476,"journal":{"name":"2013 First Iranian Conference on Pattern Recognition and Image Analysis (PRIA)","volume":"290 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":"124177318","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: 8

Improving Farsi font recognition accuracy by using proposed directional elliptic Gabor filters 利用所提出的方向椭圆Gabor滤波器提高波斯语字体识别精度

2013 First Iranian Conference on Pattern Recognition and Image Analysis (PRIA) Pub Date : 2013-03-06 DOI: 10.1109/PRIA.2013.6528429

M. Ziaratban, F. Bagheri

引用次数: 2

A new approach of training Hidden Markov Model by PSO algorithm for gene Sequence Modeling 基于粒子群算法的隐马尔可夫模型基因序列建模新方法

2013 First Iranian Conference on Pattern Recognition and Image Analysis (PRIA) Pub Date : 2013-03-06 DOI: 10.1109/PRIA.2013.6528441

M. Soruri, S. Hamid Zahiri, J. Sadri

引用次数: 8

Evaluate and determine the most effective treatment parameters in esophageal cancer using intelligent systems 使用智能系统评估和确定食管癌最有效的治疗参数

2013 First Iranian Conference on Pattern Recognition and Image Analysis (PRIA) Pub Date : 2013-03-06 DOI: 10.1109/PRIA.2013.6528455

H. Zahedi, N. Mehrshad, M. Graili

引用次数: 0

A recognition approach using multilayer perceptron and keyboard dynamics patterns 使用多层感知器和键盘动态模式的识别方法

2013 First Iranian Conference on Pattern Recognition and Image Analysis (PRIA) Pub Date : 2013-03-06 DOI: 10.1109/PRIA.2013.6528445

A. Rezaei, S. Mirzakuchaki

引用次数: 4

Rapid off-line signature verification based on Signature Envelope and Adaptive Density Partitioning 基于签名包络和自适应密度划分的离线签名快速验证

2013 First Iranian Conference on Pattern Recognition and Image Analysis (PRIA) Pub Date : 2013-03-06 DOI: 10.1109/PRIA.2013.6528428

V. Malekian, A. Aghaei, M. Rezaeian, M. Alian

引用次数: 15

A new approach for froth image segmentation using fuzzy logic 一种基于模糊逻辑的泡沫图像分割新方法

2013 First Iranian Conference on Pattern Recognition and Image Analysis (PRIA) Pub Date : 2013-03-06 DOI: 10.1109/PRIA.2013.6528459

F. Daneshmand, N. Mehrshad, M. Massinaei

引用次数: 1

A new image steganalysis method using block based optimal wavelet packet decomposition 一种基于分块最优小波包分解的图像隐写分析新方法

2013 First Iranian Conference on Pattern Recognition and Image Analysis (PRIA) Pub Date : 2013-03-06 DOI: 10.1109/PRIA.2013.6528446

L. Omrani, K. Faez

{"title":"A new image steganalysis method using block based optimal wavelet packet decomposition","authors":"L. Omrani, K. Faez","doi":"10.1109/PRIA.2013.6528446","DOIUrl":"https://doi.org/10.1109/PRIA.2013.6528446","url":null,"abstract":"Feature extraction is the base of steganalysis which is a part of image processing research field. This article has proposed a steganalysis method for digital images. Common steganalysis techniques go over the entire image; this will reduce their focus on higher frequencies in which there is a higher probability for hidden messages. Accordingly, in this article, images are first decomposed into smaller blocks and then optimal wavelet packet decomposition method is applied to extract the features of each block. In the proposed algorithm, characteristic function moments obtained from wavelet sub-bands are used as features. These features are arranged in a tree structure and then an entropy cost function is used to select the optimal values of these features. In the next step, the blocks are classified in several categories and a classifier appropriate to the features of each category is applied to distinguish cover or stego blocks. Finally, the majority vote rule is applied on the results obtained from the blocks to determine whether the entire image is a cover or stego image. The experimental results of this steganalysis method show its high accuracy as compared to the common steganalysis algorithms in the frequency domain.","PeriodicalId":370476,"journal":{"name":"2013 First Iranian Conference on Pattern Recognition and Image Analysis (PRIA)","volume":"29 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":"129059280","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: 0

Face recognition with Linear Discriminant Analysis and neural networks 基于线性判别分析和神经网络的人脸识别

2013 First Iranian Conference on Pattern Recognition and Image Analysis (PRIA) Pub Date : 2013-03-06 DOI: 10.1109/PRIA.2013.6528431

Sepide Fatahi, Ehsan Zadkhosh, Abdollah Chalechale

引用次数: 3