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

From machine generated to handwritten character recognition; a deep learning approach 从机器生成到手写字符识别;深度学习方法

2017 3rd International Conference on Pattern Recognition and Image Analysis (IPRIA) Pub Date : 2017-04-01 DOI: 10.1109/PRIA.2017.7983055

Kian Peymani, M. Soryani

引用次数: 6

Reducing circular hough transform parameters using morphological operations 减少圆形霍夫变换参数使用形态学操作

2017 3rd International Conference on Pattern Recognition and Image Analysis (IPRIA) Pub Date : 2017-04-01 DOI: 10.1109/PRIA.2017.7983018

A. Bosaghzadeh

{"title":"Reducing circular hough transform parameters using morphological operations","authors":"A. Bosaghzadeh","doi":"10.1109/PRIA.2017.7983018","DOIUrl":"https://doi.org/10.1109/PRIA.2017.7983018","url":null,"abstract":"This article introduces a new technique to reduce the parameters of the Circular Hough Transform (CHT). CHT is a well-known technique to locate circles in an image. One of the main drawbacks of CHT is its three-dimensional parameter space (location and radius of the circle) which makes this algorithm not memory efficient. In this article, based on morphological operations, we reduce this parameter space to two parameters which greatly improves its speed and memory. In the first step, we determine the radius of the circles using morphological operations. In the second step, only the location of the circle centers should be found. This trick will reduce the need for a third parameter of the CHT, hence can greatly reduce the consumed memory. Moreover, by using morphologically processed images, the images that we feed to CHT mainly have circles with a specific radius while most of other objects are removed. This trick improves the speed of the algorithm since the number of false edges is greatly reduced. Experimental results on different images prove that the proposed method can detect circles with a two-dimensional accumulator space.","PeriodicalId":336066,"journal":{"name":"2017 3rd International Conference on Pattern Recognition and Image Analysis (IPRIA)","volume":"23 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":"128212261","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

A Zernike moment based method for classification of Alzheimer's disease from structural MRI 基于Zernike矩的结构MRI阿尔茨海默病分类方法

2017 3rd International Conference on Pattern Recognition and Image Analysis (IPRIA) Pub Date : 2017-04-01 DOI: 10.1109/PRIA.2017.7983061

Aref Shams-Baboli, M. Ezoji

引用次数: 9

Superpixel tracking using Kalman filter 使用卡尔曼滤波的超像素跟踪

2017 3rd International Conference on Pattern Recognition and Image Analysis (IPRIA) Pub Date : 2017-04-01 DOI: 10.1109/PRIA.2017.7983041

Mohammad Faghihi, M. Yazdi, Sara Dianat

引用次数: 1

Superpixel-based feature learning for joint sparse representation of hyperspectral images 基于超像素的高光谱图像联合稀疏表示特征学习

2017 3rd International Conference on Pattern Recognition and Image Analysis (IPRIA) Pub Date : 2017-04-01 DOI: 10.1109/PRIA.2017.7983037

Zehtab Alasvand, M. Naderan, G. Akbarizadeh

{"title":"Superpixel-based feature learning for joint sparse representation of hyperspectral images","authors":"Zehtab Alasvand, M. Naderan, G. Akbarizadeh","doi":"10.1109/PRIA.2017.7983037","DOIUrl":"https://doi.org/10.1109/PRIA.2017.7983037","url":null,"abstract":"Hyper-Spectral Images (HSI) have high dimensional data and low number of training samples. Hence classification of these images is an ill posed problem. Existence of inescapable noise makes it more difficult to distinguish between members of each classes. To overcome this problem extracting both spectral and spatial features in a more effective method can raise the accuracy of classifier. For classification of HSIs one appropriate method is endmember extraction. On the other hand applying sparse representation is a hot topic and high performance in this field. This paper presents a novel superpixel-based method for classification of hyperspectral images. The method is called S3EJSR which uses Semi-Supervised Shroedinger Eigenmaps (SSSE) to extract spatial-spectral features and create superpixels. Next a joint sparse representation (JSR)is applied for endmember extraction and determining the category of pixel is based on a learned dictionary. Finally the classification is accomplished on The AVIRIS Indian Pines dataset and accuracy of this method is determined by SVM classifier. The results show that, compared with the same methods, the proposed classification method has better performance.","PeriodicalId":336066,"journal":{"name":"2017 3rd International Conference on Pattern Recognition and Image Analysis (IPRIA)","volume":"14 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":"115218755","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

Non-local means denoising based on SVD basis images 非局部是指基于SVD基图像去噪

2017 3rd International Conference on Pattern Recognition and Image Analysis (IPRIA) Pub Date : 2017-04-01 DOI: 10.1109/PRIA.2017.7983047

Marzieh Seyedebrahim, Azadeh Mansouri

引用次数: 0

Inclined planes optimization algorithm in optimal architecture of MLP neural networks MLP神经网络最优结构中的斜面优化算法

2017 3rd International Conference on Pattern Recognition and Image Analysis (IPRIA) Pub Date : 2017-04-01 DOI: 10.1109/PRIA.2017.7983044

N. S. Shahraki, S. Zahiri

引用次数: 8

Evaluation of graph embedding approach for dimensionality reduction using different kernels 不同核的图嵌入降维方法评价

2017 3rd International Conference on Pattern Recognition and Image Analysis (IPRIA) Pub Date : 2017-04-01 DOI: 10.1109/PRIA.2017.7983020

Mohammad Amin Naeemi, H. Mohseni

{"title":"Evaluation of graph embedding approach for dimensionality reduction using different kernels","authors":"Mohammad Amin Naeemi, H. Mohseni","doi":"10.1109/PRIA.2017.7983020","DOIUrl":"https://doi.org/10.1109/PRIA.2017.7983020","url":null,"abstract":"By passing of time, the size of data such as fMRI scans, speech signals and digital photographs becomes very high and it takes large amount of time for data processing. To overcome this problem, the dimensionality of data should be reduced. Whereas graph embedding introduces a successful framework for dimensionality reduction, we use it as the base of our proposed method. In this framework, similarity and penalty graphs are constructed based on data relations. These graphs characterize the statistical or geometric property of the data that should be kept or avoided during dimensionality reduction. Our proposed method constructs these two graphs on data using Euclidean distance, in which similarity graph connects each data point with its neighboring points on the same class and characterizes compactness of within class data, while penalty graph connects the marginal points and characterizes separability out of classes. These two graphs show the geometry of the neighboring space of each data and are able to describe whole data space. Two extensions of graph embedding are discussed in this paper which are called as linearization and kernelization of graph embedding. In kernel extension, the impression of different kernel functions is evaluated on different databases. Obtained results show that the proposed method improves the accuracy of classification on data such as face and digit databases.","PeriodicalId":336066,"journal":{"name":"2017 3rd International Conference on Pattern Recognition and Image Analysis (IPRIA)","volume":"1 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":"128616688","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

Embedding and extracting two separate images signal in salt & pepper noises in digital images based on watermarking 基于水印的数字图像中椒盐噪声中两幅独立图像信号的嵌入与提取

2017 3rd International Conference on Pattern Recognition and Image Analysis (IPRIA) Pub Date : 2017-04-01 DOI: 10.1109/PRIA.2017.7983060

Khalil Shekaramiz, Alireza Naghsh

引用次数: 5

A new approach to locate the hippocampus nest in brain MR images 脑磁共振成像海马巢定位的新方法

2017 3rd International Conference on Pattern Recognition and Image Analysis (IPRIA) Pub Date : 2017-04-01 DOI: 10.1109/PRIA.2017.7983034

Maryam Hajiesmaeili, M. Amirfakhrian

引用次数: 2