Pub Date : 2015-03-11DOI: 10.1109/PRIA.2015.7161624
A. Hariri, Soroush Arabshahi, A. Ghafari, E. Fatemizadeh
Medical image stabilization has been widely used for clinical imaging modalities. Registration is a conspicuous stage for stabilizing dynamic medical images. Some of regular methods are sensitive to bias field distortion. Sparse-induced similarity measure (SISM) is a robust registering method against spatially-varying intensity distortions which is not evitable on clinical imaging instruments. This paper presents a method for registering medical images to average of captured images using SISM method to avoid spatially-varying intensity distortions like Bias field. Proposed method is compared with SSD and MI similarity measure based registrations. Results show enhancement in stabilizing medical dynamic images with SISM method.
{"title":"Medical images stabilization using sparse-induced similarity measure","authors":"A. Hariri, Soroush Arabshahi, A. Ghafari, E. Fatemizadeh","doi":"10.1109/PRIA.2015.7161624","DOIUrl":"https://doi.org/10.1109/PRIA.2015.7161624","url":null,"abstract":"Medical image stabilization has been widely used for clinical imaging modalities. Registration is a conspicuous stage for stabilizing dynamic medical images. Some of regular methods are sensitive to bias field distortion. Sparse-induced similarity measure (SISM) is a robust registering method against spatially-varying intensity distortions which is not evitable on clinical imaging instruments. This paper presents a method for registering medical images to average of captured images using SISM method to avoid spatially-varying intensity distortions like Bias field. Proposed method is compared with SSD and MI similarity measure based registrations. Results show enhancement in stabilizing medical dynamic images with SISM method.","PeriodicalId":163817,"journal":{"name":"2015 2nd International Conference on Pattern Recognition and Image Analysis (IPRIA)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126567291","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}
Pub Date : 2015-03-11DOI: 10.1109/PRIA.2015.7161617
Elham Khodadadi, H. Kanan
In this paper we propose a new algorithm for super-resolution of Farsi text image sequences. Our algorithm contains three main steps as prior super-resolution algorithms; registration, reconstruction, and restoration. Due to special properties of Farsi texts such as appearance of dots in alphabet, selecting a proper super-resolution algorithm, especially in presence of noise, is more important. We propose an algorithm with an accurate sub-pixel registration and IBP reconstruction that reconstructs a high resolution image from a set of noisy low resolution observations. In restoration step we have exploited NLM algorithm to overcome image noise. We test our algorithm on synthetic and real data. Both quantitative and qualitative results show outperformance of our algorithm.
{"title":"Which super-resolution algorithm is proper for Farsi text image sequences","authors":"Elham Khodadadi, H. Kanan","doi":"10.1109/PRIA.2015.7161617","DOIUrl":"https://doi.org/10.1109/PRIA.2015.7161617","url":null,"abstract":"In this paper we propose a new algorithm for super-resolution of Farsi text image sequences. Our algorithm contains three main steps as prior super-resolution algorithms; registration, reconstruction, and restoration. Due to special properties of Farsi texts such as appearance of dots in alphabet, selecting a proper super-resolution algorithm, especially in presence of noise, is more important. We propose an algorithm with an accurate sub-pixel registration and IBP reconstruction that reconstructs a high resolution image from a set of noisy low resolution observations. In restoration step we have exploited NLM algorithm to overcome image noise. We test our algorithm on synthetic and real data. Both quantitative and qualitative results show outperformance of our algorithm.","PeriodicalId":163817,"journal":{"name":"2015 2nd International Conference on Pattern Recognition and Image Analysis (IPRIA)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133595705","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}
Pub Date : 2015-03-11DOI: 10.1109/PRIA.2015.7161628
M. Lotfi, B. Nazari, S. Sadri, Nazila Karimian Sichani
This paper presents a novel method to detect three types of abnormal Red Blood Cells (RBCs) called Poikilocytes in Iron deficient blood smears. Classification and counting the number of Poikilocyte cells is considered as an important step for the automatic detection of Iron Deficiency Anemia (IDA) disease. Dacrocyte, Elliptocyte and Schistocyte cells are three essential Poikilocyte cells that are prevalent in IDA. The suggested cell recognition approach includes preprocessing, segmentation, feature extraction and classification steps. Classification is done by using three distinct classifiers including Neural Network (NNET), Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) classifiers. Finally, the output of all of the three classifiers are used via Maximum Voting theory to choose the proper class. In maximum voting theory, the class that receives the maximum number of votes is chosen as the final predicted class of a sample cell. In this paper, the accuracy of the proposed method is %99, %97 and %100 for detecting Dacrocyte cells, Elliptocyte cells and Schistocyte cells, respectively.
{"title":"The detection of Dacrocyte, Schistocyte and Elliptocyte cells in Iron Deficiency Anemia","authors":"M. Lotfi, B. Nazari, S. Sadri, Nazila Karimian Sichani","doi":"10.1109/PRIA.2015.7161628","DOIUrl":"https://doi.org/10.1109/PRIA.2015.7161628","url":null,"abstract":"This paper presents a novel method to detect three types of abnormal Red Blood Cells (RBCs) called Poikilocytes in Iron deficient blood smears. Classification and counting the number of Poikilocyte cells is considered as an important step for the automatic detection of Iron Deficiency Anemia (IDA) disease. Dacrocyte, Elliptocyte and Schistocyte cells are three essential Poikilocyte cells that are prevalent in IDA. The suggested cell recognition approach includes preprocessing, segmentation, feature extraction and classification steps. Classification is done by using three distinct classifiers including Neural Network (NNET), Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) classifiers. Finally, the output of all of the three classifiers are used via Maximum Voting theory to choose the proper class. In maximum voting theory, the class that receives the maximum number of votes is chosen as the final predicted class of a sample cell. In this paper, the accuracy of the proposed method is %99, %97 and %100 for detecting Dacrocyte cells, Elliptocyte cells and Schistocyte cells, respectively.","PeriodicalId":163817,"journal":{"name":"2015 2nd International Conference on Pattern Recognition and Image Analysis (IPRIA)","volume":"174 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117206980","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}
Pub Date : 2015-03-11DOI: 10.1109/PRIA.2015.7161623
M. Rahimi, M. Yazdi
One of the inherent characteristics of radar images is the presence of speckle noise. Speckle appears as a grainy texture in the image and highly reduces the image quality. Therefore, it is desirable to reduce speckle, prior to any image interpretation. With regard to the importance of synthetic aperture radar (SAR) images, a lot of efforts have already been made to remove speckle noise from radar images, and accordingly famous filters have been introduced, each with their special advantages and disadvantages. In this paper, we examine five methods like the ones in the field of space and frequency domain. we will compare five different approaches: Wavelet Thresholding methods, anisotropic diffusion and speckle reducing anisotropic diffusion, also we suggest a method for reducing speckle of synthetic aperture radar images which is in fact a combination of hybrid mean-median filter and the method of speckle reducing anisotropic diffusion. The results indicate that the performance of our proposed method, based on criteria such as PSNR, improving SNR, standard protect the edge (β), in almost all cases is better the other compared methods; and it also offers more desirable results from the point of visual quality.
{"title":"A new hybrid algorithm for speckle noise reduction of SAR images based on mean-median filter and SRAD method","authors":"M. Rahimi, M. Yazdi","doi":"10.1109/PRIA.2015.7161623","DOIUrl":"https://doi.org/10.1109/PRIA.2015.7161623","url":null,"abstract":"One of the inherent characteristics of radar images is the presence of speckle noise. Speckle appears as a grainy texture in the image and highly reduces the image quality. Therefore, it is desirable to reduce speckle, prior to any image interpretation. With regard to the importance of synthetic aperture radar (SAR) images, a lot of efforts have already been made to remove speckle noise from radar images, and accordingly famous filters have been introduced, each with their special advantages and disadvantages. In this paper, we examine five methods like the ones in the field of space and frequency domain. we will compare five different approaches: Wavelet Thresholding methods, anisotropic diffusion and speckle reducing anisotropic diffusion, also we suggest a method for reducing speckle of synthetic aperture radar images which is in fact a combination of hybrid mean-median filter and the method of speckle reducing anisotropic diffusion. The results indicate that the performance of our proposed method, based on criteria such as PSNR, improving SNR, standard protect the edge (β), in almost all cases is better the other compared methods; and it also offers more desirable results from the point of visual quality.","PeriodicalId":163817,"journal":{"name":"2015 2nd International Conference on Pattern Recognition and Image Analysis (IPRIA)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128117173","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}
Pub Date : 2015-03-11DOI: 10.1109/PRIA.2015.7161641
Mir Abbas Daneshyar, M. Nahvi
Object tracking is an important issue in machine vision, which has many applications. A tracking method is particle filtering that is based on Monte Carlo techniques. This method is based on random sampling of a probability density function and estimating the desired variable using samples weight. In this paper, particle filter algorithm is implemented by considering the color histogram model as the existing observations. In order to investigate the particle filter performance, a comparison between this technique and the mean shift method is presented which reveals that the proposed method has better performance. A problem associated with particle filter method is degeneracy phenomenon. By modifying the particles distribution, we avoid increasing in the particles weight variance, which is the main reason of degeneracy phenomenon. Applying the proposed method on the standard databases demonstrated better results. Further, since in the proposed scheme the particles are distributed in improbable areas, if any occlusion occurs, the probability of the target missing decreases and the target tracking will be done more successfully.
{"title":"Improvement of moving objects tracking via modified particle distribution in particle filter algorithm","authors":"Mir Abbas Daneshyar, M. Nahvi","doi":"10.1109/PRIA.2015.7161641","DOIUrl":"https://doi.org/10.1109/PRIA.2015.7161641","url":null,"abstract":"Object tracking is an important issue in machine vision, which has many applications. A tracking method is particle filtering that is based on Monte Carlo techniques. This method is based on random sampling of a probability density function and estimating the desired variable using samples weight. In this paper, particle filter algorithm is implemented by considering the color histogram model as the existing observations. In order to investigate the particle filter performance, a comparison between this technique and the mean shift method is presented which reveals that the proposed method has better performance. A problem associated with particle filter method is degeneracy phenomenon. By modifying the particles distribution, we avoid increasing in the particles weight variance, which is the main reason of degeneracy phenomenon. Applying the proposed method on the standard databases demonstrated better results. Further, since in the proposed scheme the particles are distributed in improbable areas, if any occlusion occurs, the probability of the target missing decreases and the target tracking will be done more successfully.","PeriodicalId":163817,"journal":{"name":"2015 2nd International Conference on Pattern Recognition and Image Analysis (IPRIA)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127111662","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}
Pub Date : 2015-03-11DOI: 10.1109/PRIA.2015.7161632
M. Alimohammadi, M. Zahedi
Communication with hearing people society is an important problem for deaf people. Because they are not learned the valid rules of spoken language that hearing people use them. We therefor prepare an efficient corpus and apply it to Moses Machine Translation to simplify these communications. We choose communications between children because they use e-communications more than adults. All of the systems that automatically process sign language corpus rely on appropriate data. So our corpus with a limited set of words and with specific subject is the first Persian corpus containing Persian language, PL, and Persian sign language, PSL, based on the domain of children conversations. At the first step raw data are pre-processed which provides necessary information for translation. These data are statistic information extracted of sentences. After getting important data from initial sentences, the corpus is applied for training of Moses machine translation. Beside on the main goal of this system, we can educate deaf people the valid Persian grammar that is a problem for deaf people in school and society. In this paper we compare our results with the results taken from Moses decoder in other spoken languages that indicate our purpose is applicable in real world.
{"title":"Communication between deaf and hearing children using statistical machine translation","authors":"M. Alimohammadi, M. Zahedi","doi":"10.1109/PRIA.2015.7161632","DOIUrl":"https://doi.org/10.1109/PRIA.2015.7161632","url":null,"abstract":"Communication with hearing people society is an important problem for deaf people. Because they are not learned the valid rules of spoken language that hearing people use them. We therefor prepare an efficient corpus and apply it to Moses Machine Translation to simplify these communications. We choose communications between children because they use e-communications more than adults. All of the systems that automatically process sign language corpus rely on appropriate data. So our corpus with a limited set of words and with specific subject is the first Persian corpus containing Persian language, PL, and Persian sign language, PSL, based on the domain of children conversations. At the first step raw data are pre-processed which provides necessary information for translation. These data are statistic information extracted of sentences. After getting important data from initial sentences, the corpus is applied for training of Moses machine translation. Beside on the main goal of this system, we can educate deaf people the valid Persian grammar that is a problem for deaf people in school and society. In this paper we compare our results with the results taken from Moses decoder in other spoken languages that indicate our purpose is applicable in real world.","PeriodicalId":163817,"journal":{"name":"2015 2nd International Conference on Pattern Recognition and Image Analysis (IPRIA)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125347872","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}
Pub Date : 2015-03-11DOI: 10.1109/PRIA.2015.7161636
H. Rezayi, S. Seyedin
In this paper we propose a new joint image registration (IR) and super-resolution (SR) method by combining the three principal operations of warping, blurring and down-sampling. Unlike previous methods, we neither calculate the Jacobian matrix numerically nor derive the Jacobian matrix by treating the three principal operations separately. We develop a new approach to derive the Jacobian matrix analytically from the combination of the three principal operations. Experimental results show that our method has better Peak Signal-to-Noise Ratio (PSNR) than the recently proposed Tian's joint method of IR and SR. Computational complexity also has been decreased in our proposed method.
{"title":"Joint image registration and super-resolution based on combinational coefficient matrix","authors":"H. Rezayi, S. Seyedin","doi":"10.1109/PRIA.2015.7161636","DOIUrl":"https://doi.org/10.1109/PRIA.2015.7161636","url":null,"abstract":"In this paper we propose a new joint image registration (IR) and super-resolution (SR) method by combining the three principal operations of warping, blurring and down-sampling. Unlike previous methods, we neither calculate the Jacobian matrix numerically nor derive the Jacobian matrix by treating the three principal operations separately. We develop a new approach to derive the Jacobian matrix analytically from the combination of the three principal operations. Experimental results show that our method has better Peak Signal-to-Noise Ratio (PSNR) than the recently proposed Tian's joint method of IR and SR. Computational complexity also has been decreased in our proposed method.","PeriodicalId":163817,"journal":{"name":"2015 2nd International Conference on Pattern Recognition and Image Analysis (IPRIA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126021836","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}
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