Pub Date : 2010-10-01DOI: 10.1109/IRANIANMVIP.2010.5941180
Shahin Sohrabi, A. Shirazi, Siavash Ghalamifard
The key performances of the star pattern recognition algorithms are the identification efficiency and the time consumed. In the past decades, much effort has been made in database search methods and lots of them are made out. To reduce the computation database search time a novel technique using star magnitudes is proposed. Also, we propose a novel, smart and fast star identification algorithm by using this accurate and fast searching method. The simulation results based on the Desktop Universes images show that the proposed star identification and database search algorithm can achieve both high accuracy and fast recognition. The database search and star features extraction time is o(n). In addition to, since the quality of star images play an important role in improving accuracy of star pattern recognition algorithm, therefore for image pre-processing we propose a fuzzy edge detection technique. This method highly affects noise cancellation, star features extraction, database production and matching algorithm.
{"title":"A novel smart and fast searching method for star identification algorithm","authors":"Shahin Sohrabi, A. Shirazi, Siavash Ghalamifard","doi":"10.1109/IRANIANMVIP.2010.5941180","DOIUrl":"https://doi.org/10.1109/IRANIANMVIP.2010.5941180","url":null,"abstract":"The key performances of the star pattern recognition algorithms are the identification efficiency and the time consumed. In the past decades, much effort has been made in database search methods and lots of them are made out. To reduce the computation database search time a novel technique using star magnitudes is proposed. Also, we propose a novel, smart and fast star identification algorithm by using this accurate and fast searching method. The simulation results based on the Desktop Universes images show that the proposed star identification and database search algorithm can achieve both high accuracy and fast recognition. The database search and star features extraction time is o(n). In addition to, since the quality of star images play an important role in improving accuracy of star pattern recognition algorithm, therefore for image pre-processing we propose a fuzzy edge detection technique. This method highly affects noise cancellation, star features extraction, database production and matching algorithm.","PeriodicalId":350778,"journal":{"name":"2010 6th Iranian Conference on Machine Vision and Image Processing","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131334515","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 : 2010-10-01DOI: 10.1109/IRANIANMVIP.2010.5941165
Seyyed Mohammad Talebi, A. Ayatollahi, S. Moosavi
Image segmentation is a tool which is widely used in many applications related to image processing, and it comprises one of the most difficult steps of the process. This matter becomes more significant when we try to use the algorithms presented in complicated systems, such as the identity recognition systems. Different methods of image segmentation have been presented so far. In this article, a two-step algorithm has been proposed for iris segmentation. In the first step, a median filter has been applied to eliminate the noise, and in the next step, the modified active contour has been used for iris segmentation. The proposed algorithm was tested on the CASIA image data base and the obtained results showed that the proposed method has an acceptable accuracy.
{"title":"A novel Iris segmentation method based on balloon active contour","authors":"Seyyed Mohammad Talebi, A. Ayatollahi, S. Moosavi","doi":"10.1109/IRANIANMVIP.2010.5941165","DOIUrl":"https://doi.org/10.1109/IRANIANMVIP.2010.5941165","url":null,"abstract":"Image segmentation is a tool which is widely used in many applications related to image processing, and it comprises one of the most difficult steps of the process. This matter becomes more significant when we try to use the algorithms presented in complicated systems, such as the identity recognition systems. Different methods of image segmentation have been presented so far. In this article, a two-step algorithm has been proposed for iris segmentation. In the first step, a median filter has been applied to eliminate the noise, and in the next step, the modified active contour has been used for iris segmentation. The proposed algorithm was tested on the CASIA image data base and the obtained results showed that the proposed method has an acceptable accuracy.","PeriodicalId":350778,"journal":{"name":"2010 6th Iranian Conference on Machine Vision and Image Processing","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116991857","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 : 2010-10-01DOI: 10.1109/IRANIANMVIP.2010.5941147
M. Hajizadeh, M. Helfroush, M. Dehghani
This paper proposes an efficient blind watermarking algorithm using distance vector for copyright protection. In this algorithm, the wavelet coefficients from various frequency locations have been grouped into a sequence and named as Group for invisible watermark embedding and extraction. In the next step, after choosing the maximum and second maximum amplitude coefficients of each Group, the distance vector between two coefficients is computed. For embedding bit zero and bit 1, values of the distance vector elements are decreased or increased, respectively. The proposed method, takes advantage of considerable robustness against prevalent attacks. Comparison analysis demonstrates that our method has better performance than the other watermarking schemes reported recently and also watermarked images do not suffer from obvious visual distortion.
{"title":"A novel blind watermarking method based on distance vector of significant wavelet coefficients","authors":"M. Hajizadeh, M. Helfroush, M. Dehghani","doi":"10.1109/IRANIANMVIP.2010.5941147","DOIUrl":"https://doi.org/10.1109/IRANIANMVIP.2010.5941147","url":null,"abstract":"This paper proposes an efficient blind watermarking algorithm using distance vector for copyright protection. In this algorithm, the wavelet coefficients from various frequency locations have been grouped into a sequence and named as Group for invisible watermark embedding and extraction. In the next step, after choosing the maximum and second maximum amplitude coefficients of each Group, the distance vector between two coefficients is computed. For embedding bit zero and bit 1, values of the distance vector elements are decreased or increased, respectively. The proposed method, takes advantage of considerable robustness against prevalent attacks. Comparison analysis demonstrates that our method has better performance than the other watermarking schemes reported recently and also watermarked images do not suffer from obvious visual distortion.","PeriodicalId":350778,"journal":{"name":"2010 6th Iranian Conference on Machine Vision and Image Processing","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126758089","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 : 2010-10-01DOI: 10.1109/IRANIANMVIP.2010.5941157
Ali Rekabdar, O. Khayat, Noushin Khatib, Mina Aminghafari
Within this framework we describe a novel technique for removing noise from digital noisy images, based on the modeling of wavelet coefficient with bivariate normal distribution and statistical calculation. A method for image denoising is presented in this paper to maximize a posterior density function (MAP) estimator using a bivariate normal random variable. We use our denoising algorithm in 2-D complex wavelet domain comparing with soft and hard thresholding method of stationary wavelet analysis tool (2-D SWT). Despite the simplicity of our method in its implementation, our denoising results achieves better performance than the other mentioned methods both visually and in terms of peak signal-to-noise ratio (PSNR).
{"title":"Using bivariate Gaussian distribution for image denoising in the 2-D complex wavelet domain","authors":"Ali Rekabdar, O. Khayat, Noushin Khatib, Mina Aminghafari","doi":"10.1109/IRANIANMVIP.2010.5941157","DOIUrl":"https://doi.org/10.1109/IRANIANMVIP.2010.5941157","url":null,"abstract":"Within this framework we describe a novel technique for removing noise from digital noisy images, based on the modeling of wavelet coefficient with bivariate normal distribution and statistical calculation. A method for image denoising is presented in this paper to maximize a posterior density function (MAP) estimator using a bivariate normal random variable. We use our denoising algorithm in 2-D complex wavelet domain comparing with soft and hard thresholding method of stationary wavelet analysis tool (2-D SWT). Despite the simplicity of our method in its implementation, our denoising results achieves better performance than the other mentioned methods both visually and in terms of peak signal-to-noise ratio (PSNR).","PeriodicalId":350778,"journal":{"name":"2010 6th Iranian Conference on Machine Vision and Image Processing","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126100730","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 : 2010-10-01DOI: 10.1109/IRANIANMVIP.2010.5941174
M. H. Ghaeminia, S. B. Shokouhi
Background modeling and detecting moving objects in scene is a convenient method in many surveillance systems. We propose an approach that is useful in estimating background. In our approach, first each frame is divided to blocks, and blocks in frame sequences sorted to make block series. Finally PCA process applied to these block series. Based on PCA theorem if there is change in block series which means there is not pure background, the main component of block series is comparable to other components of series. By detecting these regions and neglecting it from scene a background modeled. This approach was known as multi block PCA which was used before for detection changes in images and now in this paper we apply it to video sequences adaptively. In this model dimension of database equals to number of frames which made block series. Also our experiments show that this method is robust in change illumination because the model is updated periodically. Moreover computational complexity of the algorithm and accuracy in localizing moving objects could be compared with other fast clustering based background modeling such as Mixture of Gaussian (MoG) and mean shift technique.
{"title":"Adaptive background model for moving objects based on PCA","authors":"M. H. Ghaeminia, S. B. Shokouhi","doi":"10.1109/IRANIANMVIP.2010.5941174","DOIUrl":"https://doi.org/10.1109/IRANIANMVIP.2010.5941174","url":null,"abstract":"Background modeling and detecting moving objects in scene is a convenient method in many surveillance systems. We propose an approach that is useful in estimating background. In our approach, first each frame is divided to blocks, and blocks in frame sequences sorted to make block series. Finally PCA process applied to these block series. Based on PCA theorem if there is change in block series which means there is not pure background, the main component of block series is comparable to other components of series. By detecting these regions and neglecting it from scene a background modeled. This approach was known as multi block PCA which was used before for detection changes in images and now in this paper we apply it to video sequences adaptively. In this model dimension of database equals to number of frames which made block series. Also our experiments show that this method is robust in change illumination because the model is updated periodically. Moreover computational complexity of the algorithm and accuracy in localizing moving objects could be compared with other fast clustering based background modeling such as Mixture of Gaussian (MoG) and mean shift technique.","PeriodicalId":350778,"journal":{"name":"2010 6th Iranian Conference on Machine Vision and Image Processing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130048121","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 : 2010-10-01DOI: 10.1109/IRANIANMVIP.2010.5941166
M. Habibi
The efficient transmission of video rate data is a demanding need in camera surveillance systems. This paper presents a low power smart CMOS image sensor which is suitable for surveillance applications. The sensor captures the image scene and using in-pixel difference detectors, it detects the temporal change events in the image. To reduce the power consumption, only the portions of the image scene with intensity change are transferred to the output. For this purpose, the performance of two different event driven data transfer methods, pixel based and window based, are investigated and it is shown that each method is appropriate under different surveillance conditions. The performance of the technique is shown using a 64×64 pixel sensor designed in a 0.18μm standard CMOS technology. The sensor chip consumes 0.5mW of power while operating at 30fps.
{"title":"A low power smart CMOS image sensor for surveillance applications","authors":"M. Habibi","doi":"10.1109/IRANIANMVIP.2010.5941166","DOIUrl":"https://doi.org/10.1109/IRANIANMVIP.2010.5941166","url":null,"abstract":"The efficient transmission of video rate data is a demanding need in camera surveillance systems. This paper presents a low power smart CMOS image sensor which is suitable for surveillance applications. The sensor captures the image scene and using in-pixel difference detectors, it detects the temporal change events in the image. To reduce the power consumption, only the portions of the image scene with intensity change are transferred to the output. For this purpose, the performance of two different event driven data transfer methods, pixel based and window based, are investigated and it is shown that each method is appropriate under different surveillance conditions. The performance of the technique is shown using a 64×64 pixel sensor designed in a 0.18μm standard CMOS technology. The sensor chip consumes 0.5mW of power while operating at 30fps.","PeriodicalId":350778,"journal":{"name":"2010 6th Iranian Conference on Machine Vision and Image Processing","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128248399","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 : 2010-10-01DOI: 10.1109/IRANIANMVIP.2010.5941148
R. Keshavarzian, A. Aghagolzadeh, Hadi Seyedarabi, J. M. Niya
The transmission of block-coded images over wireless channels results in lost blocks. In this paper, we propose a new error concealment method for covering up the high packet losses of an original image after its transmission through an error-prone channel. In this scheme, Discrete Wavelet Transform (DWT) is applied to each block of the original image in order to produce a lower resolution copy of the each block. Then, we choose approximation coefficients of each block as replica of the block and embed it into a remote block of the image in the spatial domain. It is shown that the proposed scheme provides significant improvement over existing algorithms in terms of both subjective and objective evaluations. This technique can be implemented for wireless channels to combat degradations in a backward-compatible scheme.
{"title":"Block-based image error concealment using fragile watermarking in error-prone channels","authors":"R. Keshavarzian, A. Aghagolzadeh, Hadi Seyedarabi, J. M. Niya","doi":"10.1109/IRANIANMVIP.2010.5941148","DOIUrl":"https://doi.org/10.1109/IRANIANMVIP.2010.5941148","url":null,"abstract":"The transmission of block-coded images over wireless channels results in lost blocks. In this paper, we propose a new error concealment method for covering up the high packet losses of an original image after its transmission through an error-prone channel. In this scheme, Discrete Wavelet Transform (DWT) is applied to each block of the original image in order to produce a lower resolution copy of the each block. Then, we choose approximation coefficients of each block as replica of the block and embed it into a remote block of the image in the spatial domain. It is shown that the proposed scheme provides significant improvement over existing algorithms in terms of both subjective and objective evaluations. This technique can be implemented for wireless channels to combat degradations in a backward-compatible scheme.","PeriodicalId":350778,"journal":{"name":"2010 6th Iranian Conference on Machine Vision and Image Processing","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132666934","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 : 2010-10-01DOI: 10.1109/IRANIANMVIP.2010.5941158
A. Banitalebi, S. Nader-Esfahani, A. Avanaki
Motion estimation is one of the important procedures in the all video encoders. Most of the complexity of the video coder depends on the complexity of the motion estimation step. The original motion estimation algorithm has a remarkable complexity and therefore many improvements were proposed to enhance the crude version of the motion estimation. The basic idea of many of these works were to optimize some distortion function for mean squared error (MSE) or sum of absolute difference (SAD) in block matching But it is shown that these metrics do not conclude the quality as it is, on the other hand, they are not compatible with the human visual system (HVS). In this paper we explored the usage of the image quality metrics in the video coding and more specific in the motion estimation. We have utilized the perceptual image quality metrics instead of MSE or SAD in the block based motion estimation. Three different metrics have used: structural similarity or SSIM, complex wavelet structural similarity or CW-SSIM, visual information fidelity or VIF. Experimental results showed that usage of the quality criterions can improve the compression rate while the quality remains fix and thus better quality in coded video at the same bit budget.
{"title":"A perceptual based motion compensation technique for video coding","authors":"A. Banitalebi, S. Nader-Esfahani, A. Avanaki","doi":"10.1109/IRANIANMVIP.2010.5941158","DOIUrl":"https://doi.org/10.1109/IRANIANMVIP.2010.5941158","url":null,"abstract":"Motion estimation is one of the important procedures in the all video encoders. Most of the complexity of the video coder depends on the complexity of the motion estimation step. The original motion estimation algorithm has a remarkable complexity and therefore many improvements were proposed to enhance the crude version of the motion estimation. The basic idea of many of these works were to optimize some distortion function for mean squared error (MSE) or sum of absolute difference (SAD) in block matching But it is shown that these metrics do not conclude the quality as it is, on the other hand, they are not compatible with the human visual system (HVS). In this paper we explored the usage of the image quality metrics in the video coding and more specific in the motion estimation. We have utilized the perceptual image quality metrics instead of MSE or SAD in the block based motion estimation. Three different metrics have used: structural similarity or SSIM, complex wavelet structural similarity or CW-SSIM, visual information fidelity or VIF. Experimental results showed that usage of the quality criterions can improve the compression rate while the quality remains fix and thus better quality in coded video at the same bit budget.","PeriodicalId":350778,"journal":{"name":"2010 6th Iranian Conference on Machine Vision and Image Processing","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115491299","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 : 2010-10-01DOI: 10.1109/IRANIANMVIP.2010.5941131
G. Akbarizadeh, G. Rezai-Rad
In this paper, a new algorithm for segmentation of Synthetic Aperture Radar images using the skewness wavelet energy has been presented. The skewness is the 3rd order cumulant which extracts the statistical properties of each region of a SAR image. SAR images have Nonlinearity in intensity inhomogeneities because of the speckle noise. The algorithm which we proposed in this paper is a region-based active contour model that is able to use the intensity information in local regions. This algorithm also is able to cope with the speckle noise and nonlinear intensity inhomogeneity of SAR images. We use the wavelet energy to analyze each sub-band of a SAR image. The results of the proposed algorithm on the test SAR images of agricultural and urban regions show a good performance of this method.
{"title":"A new cumulant-based active contour model with wavelet energy for segmentation of SAR images","authors":"G. Akbarizadeh, G. Rezai-Rad","doi":"10.1109/IRANIANMVIP.2010.5941131","DOIUrl":"https://doi.org/10.1109/IRANIANMVIP.2010.5941131","url":null,"abstract":"In this paper, a new algorithm for segmentation of Synthetic Aperture Radar images using the skewness wavelet energy has been presented. The skewness is the 3rd order cumulant which extracts the statistical properties of each region of a SAR image. SAR images have Nonlinearity in intensity inhomogeneities because of the speckle noise. The algorithm which we proposed in this paper is a region-based active contour model that is able to use the intensity information in local regions. This algorithm also is able to cope with the speckle noise and nonlinear intensity inhomogeneity of SAR images. We use the wavelet energy to analyze each sub-band of a SAR image. The results of the proposed algorithm on the test SAR images of agricultural and urban regions show a good performance of this method.","PeriodicalId":350778,"journal":{"name":"2010 6th Iranian Conference on Machine Vision and Image Processing","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114420843","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 : 2010-10-01DOI: 10.1109/IRANIANMVIP.2010.5941139
A. Zahedi, H. Sadjedi, A. Behrad
The blood vessels of retinal image have a unique pattern, from eye to eye and person to person. We have used this trait for designed a new person identification system. This approach focused on blood vessels around the optical disc instead of extracting total retinal blood to optimize the computational cost. At first, optical disc is localized using template matching technique and uses it to rotate the retinal image to reference position. This process compensate the rotation effects which might occur during scanning process then a circular region of interest (ROI) around optical disc is selected. Next, a rotation invariant template is created from each ROI by a polar transformation. In the next stage, vessels from each template are enhanced. Radon transform is used for feature definition in our method. Finally we employ 1D discrete Fourier transform and Euclidian distance for feature matching. The proposed algorithm was tested on a 200 image from DRIVE database [9]. Experimental results on the database demonstrated an average identification rate equal to 100 percent for our identification system.
{"title":"A new retinal image processing method for human identification using radon transform","authors":"A. Zahedi, H. Sadjedi, A. Behrad","doi":"10.1109/IRANIANMVIP.2010.5941139","DOIUrl":"https://doi.org/10.1109/IRANIANMVIP.2010.5941139","url":null,"abstract":"The blood vessels of retinal image have a unique pattern, from eye to eye and person to person. We have used this trait for designed a new person identification system. This approach focused on blood vessels around the optical disc instead of extracting total retinal blood to optimize the computational cost. At first, optical disc is localized using template matching technique and uses it to rotate the retinal image to reference position. This process compensate the rotation effects which might occur during scanning process then a circular region of interest (ROI) around optical disc is selected. Next, a rotation invariant template is created from each ROI by a polar transformation. In the next stage, vessels from each template are enhanced. Radon transform is used for feature definition in our method. Finally we employ 1D discrete Fourier transform and Euclidian distance for feature matching. The proposed algorithm was tested on a 200 image from DRIVE database [9]. Experimental results on the database demonstrated an average identification rate equal to 100 percent for our identification system.","PeriodicalId":350778,"journal":{"name":"2010 6th Iranian Conference on Machine Vision and Image Processing","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121159294","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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