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

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Face detection using color segmentation and RHT 使用颜色分割和RHT进行人脸检测
2017 3rd International Conference on Pattern Recognition and Image Analysis (IPRIA) Pub Date : 2017-04-01 DOI: 10.1109/PRIA.2017.7983032
A. Aminian, Mohammad Salimi Beni
{"title":"Face detection using color segmentation and RHT","authors":"A. Aminian, Mohammad Salimi Beni","doi":"10.1109/PRIA.2017.7983032","DOIUrl":"https://doi.org/10.1109/PRIA.2017.7983032","url":null,"abstract":"Face detection is one of the most researched topics in computer vision. During the past decades, several fast and accurate methods have been developed by using different computer vision and statistical tools. In fact, accuracy and applicability are two main factors which researchers try to improve. In this paper a method for face region detection using color segmentation and randomized Hough transform (RHT) is proposed. In the first step, by using the extracted color information of an image in HSV color space, most probable candidates for face-like regions are defined. Then, a quantization process is performed on the segmented image. Finally, based on the reality that oval shape of a face could be approximated by an ellipse, the RHT algorithm is used to find face region. The efficiency of the proposed method is demonstrated by the experiment on the UPCFaceDatabase face database, where the images vary in pose, expression, illumination. Proposed method has shown 95.4% average of positive predictive value.","PeriodicalId":336066,"journal":{"name":"2017 3rd International Conference on Pattern Recognition and Image Analysis (IPRIA)","volume":"32 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":"115943317","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: 2
Content-based image retrieval with color and texture features in neutrosophic domain 中性粒细胞域颜色和纹理特征的基于内容图像检索
2017 3rd International Conference on Pattern Recognition and Image Analysis (IPRIA) Pub Date : 2017-04-01 DOI: 10.1109/PRIA.2017.7983063
A. Rashno, S. Sadri
{"title":"Content-based image retrieval with color and texture features in neutrosophic domain","authors":"A. Rashno, S. Sadri","doi":"10.1109/PRIA.2017.7983063","DOIUrl":"https://doi.org/10.1109/PRIA.2017.7983063","url":null,"abstract":"In this paper, a new content-based image retrieval (CBIR) scheme is proposed in neutrosophic (NS) domain. For this task, RGB images are first transformed to three subsets in NS domain and then segmented. For each segment of an image, color features including dominant color discribtor (DCD), histogram and statistic components are extracted. Wavelet features are also extracted as texture features from the whole image. All extracted features from either segmented image or the whole image are combined to create a feature vector. Feature vectors are presented for ant colony optimization (ACO) feature selection which selects the most relevant features. Selected features are used for final retrieval process. Proposed CBIR scheme is evaluated on Corel image dataset. Experimental results show that the proposed method outperforms our prior method (with the same feature vector and feature selection method) by 2% and 1% with respect to precision and recall, respectively. Also, the proposed method achieves the improvement of 13% and 2% in precision and recall, respectively, in comparison with prior methods.","PeriodicalId":336066,"journal":{"name":"2017 3rd International Conference on Pattern Recognition and Image Analysis (IPRIA)","volume":"117 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":"116708855","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: 42
Introducing a new method robust against crop attack in digital image watermarking using two-step sudoku 介绍了一种基于两步数独的数字图像水印鲁棒抗裁剪攻击方法
2017 3rd International Conference on Pattern Recognition and Image Analysis (IPRIA) Pub Date : 2017-04-01 DOI: 10.1109/PRIA.2017.7983054
Mohammad Goli, Alireza Naghsh
{"title":"Introducing a new method robust against crop attack in digital image watermarking using two-step sudoku","authors":"Mohammad Goli, Alireza Naghsh","doi":"10.1109/PRIA.2017.7983054","DOIUrl":"https://doi.org/10.1109/PRIA.2017.7983054","url":null,"abstract":"Several methods are exploited to watermark digital images as a safety measure for storing information, but an attacker can destroy the information by cropping a segment of the watermarked image. In recent years, numerous schemes were proposed that reduce the impact of such attacks. A new method has been proposed to confront cropping attack that is carried out using two sudoku tables. In this method, the watermark image is scattered in two sudoku table layouts with different solutions and is watermarked in the host image. Using this method, the watermark image is repeated 81 times in the host image, and to this effect the watermark image can be reconstructed using other segments when cropped by the attacker. Both sudokus used in this paper are in the classic 9×9 form and using this method, resistance to cropping attacks increases up to 98.8%.","PeriodicalId":336066,"journal":{"name":"2017 3rd International Conference on Pattern Recognition and Image Analysis (IPRIA)","volume":"19 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":"125299255","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: 17
A level set based method for coastline detection of SAR images 基于水平集的SAR图像海岸线检测方法
2017 3rd International Conference on Pattern Recognition and Image Analysis (IPRIA) Pub Date : 2017-04-01 DOI: 10.1109/PRIA.2017.7983057
Mohammad Modava, G. Akbarizadeh
{"title":"A level set based method for coastline detection of SAR images","authors":"Mohammad Modava, G. Akbarizadeh","doi":"10.1109/PRIA.2017.7983057","DOIUrl":"https://doi.org/10.1109/PRIA.2017.7983057","url":null,"abstract":"Coastline detection is important for surveying and mapping reasons. This paper presents an efficient approach to detect coastlines from synthetic aperture radar (SAR) images. The proposed approach is based on fuzzy c-means (FCM) clustering and level set segmentation. It consists of a sequence of image processing algorithms. First, the FCM clustering is applied to the input SAR image. Second, the level set method has been used the result of FCM clustering as initial contours to extract the coastline. The method proposed in this paper, does not require determining the initial shape for active contour. Also it is robust to speckle noise. Experimental results on high and low resolution SAR images show the good performance of this method for coastline detection.","PeriodicalId":336066,"journal":{"name":"2017 3rd International Conference on Pattern Recognition and Image Analysis (IPRIA)","volume":"399 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":"122995890","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: 28
Enhance support relation extraction accuracy using improvement of segmentation in RGB-D images 改进RGB-D图像的分割，提高支持关系提取的精度
2017 3rd International Conference on Pattern Recognition and Image Analysis (IPRIA) Pub Date : 2017-04-01 DOI: 10.1109/PRIA.2017.7983040
Shokouh S. Ahmadi, Hassan Khotanlou
{"title":"Enhance support relation extraction accuracy using improvement of segmentation in RGB-D images","authors":"Shokouh S. Ahmadi, Hassan Khotanlou","doi":"10.1109/PRIA.2017.7983040","DOIUrl":"https://doi.org/10.1109/PRIA.2017.7983040","url":null,"abstract":"Todays, increasing in machine vision fields and applications make it necessary to have accurate scene understanding and analyzing. Support relation extraction is one of the most important and critical problem in robotic and machine vision task. In this article, we enhance support relation extraction accuracy using improvement of segmentation. Having the depth, moreover the color, in RGB-D images enable us to obtain accurate and precise support relation. In this paper an approach is also presented to redress discontinuities in point cloud occurred while recording. Experimental result shows the accuracy of the extracted support relation will be significantly increase after segmentation improvement.","PeriodicalId":336066,"journal":{"name":"2017 3rd International Conference on Pattern Recognition and Image Analysis (IPRIA)","volume":"63 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":"126440655","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: 1
SAR image segmentation using region growing and spectral cluster 基于区域生长和光谱聚类的SAR图像分割
2017 3rd International Conference on Pattern Recognition and Image Analysis (IPRIA) Pub Date : 2017-04-01 DOI: 10.1109/PRIA.2017.7983052
A. Baghi, A. Karami
{"title":"SAR image segmentation using region growing and spectral cluster","authors":"A. Baghi, A. Karami","doi":"10.1109/PRIA.2017.7983052","DOIUrl":"https://doi.org/10.1109/PRIA.2017.7983052","url":null,"abstract":"In this paper a new method based on Region Growing (RG) and Spectral Cluster (SC) for segmentation of synthetic aperture radar (SAR) images is introduced. In the proposed method first RG is applied to the SAR images in order to find the edge and then segmentation is done using SC method. The proposed method (RG+SC) is compared with some state-of-the-art segmentation algorithms on real SAR image. Obtained results show the efficiency of the proposed approach.","PeriodicalId":336066,"journal":{"name":"2017 3rd International Conference on Pattern Recognition and Image Analysis (IPRIA)","volume":"32 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":"133774006","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
Recognizing multiple observations using adaptive graph based label propagation 使用基于自适应图的标签传播来识别多个观测值
2017 3rd International Conference on Pattern Recognition and Image Analysis (IPRIA) Pub Date : 2017-04-01 DOI: 10.1109/PRIA.2017.7983046
F. Dornaika, Radouan Dhabi, Y. Ruichek, A. Bosaghzadeh
{"title":"Recognizing multiple observations using adaptive graph based label propagation","authors":"F. Dornaika, Radouan Dhabi, Y. Ruichek, A. Bosaghzadeh","doi":"10.1109/PRIA.2017.7983046","DOIUrl":"https://doi.org/10.1109/PRIA.2017.7983046","url":null,"abstract":"Recently, we introduced a robust and adaptive method for constructing sparse graphs. This method was termed Two Phase Weighted Regularized Least Square (TPWRLS) [6]. In this framework, the graph structure and its affinity matrix are simultaneously computed through a two phase sample coding. The second phase of coding utilizes adaptive sample pruning and re-weighting. In the context of graph-based semi-supervised label propagation, the obtained graph can achieve or outperform state-of-the art graph construction methods. In this paper, we present a performance study of the proposed method by considering two main aspects that were not addressed before. First, the new graph is exploited in order to tackle the problem of recognizing multiple images corresponding to the same category-a non straightforward scenario for supervised recognition techniques. Second, a performance evaluation on different image descriptor types is carried out. Experiments are conducted on three public image datasets: two face datasets and one handwritten digit dataset. These experiments show that in addition to its superiority over competing graph construction methods, the proposed method can easily solve the label inference of multiple observations and can work with several types of image descriptors and scenes.","PeriodicalId":336066,"journal":{"name":"2017 3rd International Conference on Pattern Recognition and Image Analysis (IPRIA)","volume":"274 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":"129167523","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
A constructive genetic algorithm for LBP in face recognition 一种基于LBP的人脸识别建设性遗传算法
2017 3rd International Conference on Pattern Recognition and Image Analysis (IPRIA) Pub Date : 2017-04-01 DOI: 10.1109/PRIA.2017.7983043
A. Nazari, S. Shouraki
{"title":"A constructive genetic algorithm for LBP in face recognition","authors":"A. Nazari, S. Shouraki","doi":"10.1109/PRIA.2017.7983043","DOIUrl":"https://doi.org/10.1109/PRIA.2017.7983043","url":null,"abstract":"LBP coefficients are essential and determine the priority of gray differences. The objectives of this paper are to reveal this and propose a method for finding an optimal priority through the genetic algorithm. On the other hand, the genetic operators such as initialization and cross-over operators, generate invalid coefficients, defective chromosomes. This paper also recommends a rectifying method for correcting defective chromosomes. Results on the FERET and Extended Yale B datasets indicate that the proposed method has markedly higher recognition rates than LBP.","PeriodicalId":336066,"journal":{"name":"2017 3rd International Conference on Pattern Recognition and Image Analysis (IPRIA)","volume":"116 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":"124027256","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: 6
License plate detection using adaptive morphological closing and local adaptive thresholding 基于自适应形态学关闭和局部自适应阈值的车牌检测
2017 3rd International Conference on Pattern Recognition and Image Analysis (IPRIA) Pub Date : 2017-04-01 DOI: 10.1109/PRIA.2017.7983035
Babak Abad Fomani, A. Shahbahrami
{"title":"License plate detection using adaptive morphological closing and local adaptive thresholding","authors":"Babak Abad Fomani, A. Shahbahrami","doi":"10.1109/PRIA.2017.7983035","DOIUrl":"https://doi.org/10.1109/PRIA.2017.7983035","url":null,"abstract":"Automatic License Plate Recognition (ALPR) is base of many Intelligent Transformation Systems (ITS) services. Many ALPR systems have usually three steps, License Plate Detection (LPD), character segmentation and character recognition. LPD is the first and main step in ALPR. There are many algorithms for LPD, while detecting a license plate in different conditions is still a complex task. The goal of this paper is proposing an algorithm to extract license plate in different conditions. The proposed approach has three following steps, adaptive morphological closing, local adaptive thresholding and morphological opening. Experimental results using some real dataset show that the detection rate of the proposed approach is higher than some related works. In addition, the computational time of the proposed approach is less than other techniques.","PeriodicalId":336066,"journal":{"name":"2017 3rd International Conference on Pattern Recognition and Image Analysis (IPRIA)","volume":"87 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":"122871580","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: 12
A new method in simultaneous estimation of Kinect-V2 sensor calibration using shuffled frog leaping algorithm 基于青蛙跳跃算法的Kinect-V2传感器标定同步估计新方法
2017 3rd International Conference on Pattern Recognition and Image Analysis (IPRIA) Pub Date : 2017-04-01 DOI: 10.1109/PRIA.2017.7983048
Amir Safaei, S. Fazli
{"title":"A new method in simultaneous estimation of Kinect-V2 sensor calibration using shuffled frog leaping algorithm","authors":"Amir Safaei, S. Fazli","doi":"10.1109/PRIA.2017.7983048","DOIUrl":"https://doi.org/10.1109/PRIA.2017.7983048","url":null,"abstract":"Calibration of color and infra-red cameras is the fundamental step in almost every 2D or 3D image processing applications and pre-processing of images and videos. This article presents a novel method for estimation of 19 parameters of RGB and depth camera calibration in Kinect sensor, simultaneously. The proposed algorithm is based on applying shuffled frog leaping algorithm (SFLA) for deep optimization and estimation all parameters of intrinsic, extrinsic and lens distortions of cameras. This algorithm does not need the initial estimation for optimization and it can avoid trapping into local minima. Using non-direct estimation, we achieve middle computing matrices such as homography matrix, used in pinhole camera model. Re-projection error criteria is defined as the objective function in this algorithm. The radial lens distortion is estimated using SFLA. Kinect version 2 sensor is used in this research and experimental results show the proposed method is more efficient and accurate in compare with traditional numerical solutions.","PeriodicalId":336066,"journal":{"name":"2017 3rd International Conference on Pattern Recognition and Image Analysis (IPRIA)","volume":"5 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":"125518967","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: 1
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