Pub Date : 2010-10-01DOI: 10.1109/IRANIANMVIP.2010.5941176
Tahere Royani, J. Haddadnia, M. Pooshideh
Calculating of vehicles density in traffic images is a challenging research topic as it has to directly deal with hostile but realistic conditions on the road, such as uncontrolled illuminations, cast shadows, and visual occlusion. Yet, the outcome of being able to accurately count and resolve vehicles under such conditions has tremendous benefit to traffic surveillance. Accurate vehicle count enables the extraction of important traffic information such as congestion level and lane occupancy. There are different methods for vehicles counting from traffic images that emphasize on the accuracy, but most of them suffer from long time process and computational complexity, so they can't be used in real-time condition. This paper proposed a novel simple method for traffic density calculation in multiple vehicle occlusions based on counting object pixels and assigning a distance index to each region of image that concentrates on time and computational complexity and has tolerable accuracy in traffic density calculation. Suppose that the occluded vehicles are segmented from the road background by previously proposed vehicle segmentation method. The proposed method has been tested on real-world monocular traffic images with multiple vehicle occlusions. The experimental results show that the proposed method can provide real-time and useful information for traffic surveillance.
{"title":"A simple method for calculating vehicle density in traffic images","authors":"Tahere Royani, J. Haddadnia, M. Pooshideh","doi":"10.1109/IRANIANMVIP.2010.5941176","DOIUrl":"https://doi.org/10.1109/IRANIANMVIP.2010.5941176","url":null,"abstract":"Calculating of vehicles density in traffic images is a challenging research topic as it has to directly deal with hostile but realistic conditions on the road, such as uncontrolled illuminations, cast shadows, and visual occlusion. Yet, the outcome of being able to accurately count and resolve vehicles under such conditions has tremendous benefit to traffic surveillance. Accurate vehicle count enables the extraction of important traffic information such as congestion level and lane occupancy. There are different methods for vehicles counting from traffic images that emphasize on the accuracy, but most of them suffer from long time process and computational complexity, so they can't be used in real-time condition. This paper proposed a novel simple method for traffic density calculation in multiple vehicle occlusions based on counting object pixels and assigning a distance index to each region of image that concentrates on time and computational complexity and has tolerable accuracy in traffic density calculation. Suppose that the occluded vehicles are segmented from the road background by previously proposed vehicle segmentation method. The proposed method has been tested on real-world monocular traffic images with multiple vehicle occlusions. The experimental results show that the proposed method can provide real-time and useful information for traffic surveillance.","PeriodicalId":350778,"journal":{"name":"2010 6th Iranian Conference on Machine Vision and Image Processing","volume":"5 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":"127719405","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.2010.5941183
AmirEhsan Lashkari
Nowadays, automatic defect detection in Breast images which obtains from mommogram is very important in many diagnostic and therapeutic applications. This paper introduces a Novel automatic breast abnormality detection method that uses mammogram images to determine any abnormality in breast tissues. Here, has been tried to give clear description from breast tissues using Gabor wavelets, Geometric Moment Invariants(GMIs), energy, entropy, contrast and some other statistic features such as mean, median, variance, correlation, values of maximum and minimum intensity. It is used from a feature selection method to reduce the feature space too. This method uses from neural network to do this classification. The purpose of this project is to classify the breast tissues to normal and abnormal classes automatically, that saves the radiologist time, increases accuracy and yield of diagnosis.
{"title":"Full automatic micro calcification detection in mammogram images using artificial neural network and Gabor wavelets","authors":"AmirEhsan Lashkari","doi":"10.1109/IRANIANMVIP.2010.5941183","DOIUrl":"https://doi.org/10.1109/IRANIANMVIP.2010.5941183","url":null,"abstract":"Nowadays, automatic defect detection in Breast images which obtains from mommogram is very important in many diagnostic and therapeutic applications. This paper introduces a Novel automatic breast abnormality detection method that uses mammogram images to determine any abnormality in breast tissues. Here, has been tried to give clear description from breast tissues using Gabor wavelets, Geometric Moment Invariants(GMIs), energy, entropy, contrast and some other statistic features such as mean, median, variance, correlation, values of maximum and minimum intensity. It is used from a feature selection method to reduce the feature space too. This method uses from neural network to do this classification. The purpose of this project is to classify the breast tissues to normal and abnormal classes automatically, that saves the radiologist time, increases accuracy and yield of diagnosis.","PeriodicalId":350778,"journal":{"name":"2010 6th Iranian Conference on Machine Vision and Image Processing","volume":"2018 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":"128072846","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.2010.5941140
Mohammad Haghighat, A. Aghagolzadeh, Hadi Seyedarabi
The objective of image fusion is to combine information from multiple images of the same scene in order to deliver only the useful information. The discrete cosine transform (DCT) based methods of image fusion are more suitable and time-saving in real-time systems using DCT based standards of still image or video. In this paper an efficient approach for fusion of multi-focus images based on variance calculated in DCT domain is presented. The experimental results on several images show the efficiency improvement of our method both in quality and complexity reduction in comparison with several recent proposed techniques.
{"title":"Real-time fusion of multi-focus images for visual sensor networks","authors":"Mohammad Haghighat, A. Aghagolzadeh, Hadi Seyedarabi","doi":"10.1109/IRANIANMVIP.2010.5941140","DOIUrl":"https://doi.org/10.1109/IRANIANMVIP.2010.5941140","url":null,"abstract":"The objective of image fusion is to combine information from multiple images of the same scene in order to deliver only the useful information. The discrete cosine transform (DCT) based methods of image fusion are more suitable and time-saving in real-time systems using DCT based standards of still image or video. In this paper an efficient approach for fusion of multi-focus images based on variance calculated in DCT domain is presented. The experimental results on several images show the efficiency improvement of our method both in quality and complexity reduction in comparison with several recent proposed techniques.","PeriodicalId":350778,"journal":{"name":"2010 6th Iranian Conference on Machine Vision and Image Processing","volume":"18 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120911529","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.2010.5941167
Seyed Mohammad Seyedzade, S. Mirzakuchaki, Reza Ebrahimi Atani
In this paper, a novel algorithm for image encryption based on SHA-512 is proposed. The main idea of the algorithm is to use one half of image data for encryption of the other half of the image reciprocally. Distinct characteristics of the algorithm are high security, high sensitivity and high speed that can be applied for encryption of gray-level and color images. The algorithm consists of two main sections: The first does preprocessing operation to shuffle one half of image. The second uses hash function to generate a random number mask. The mask is then XORed with the other part of the image which is going to be encrypted. The aim of this work is to increase the image entropy. Both security and performance aspects of the proposed algorithm are analyzed and satisfactory results are achieved in various rounds.
{"title":"A novel image encryption algorithm based on hash function","authors":"Seyed Mohammad Seyedzade, S. Mirzakuchaki, Reza Ebrahimi Atani","doi":"10.1109/IRANIANMVIP.2010.5941167","DOIUrl":"https://doi.org/10.1109/IRANIANMVIP.2010.5941167","url":null,"abstract":"In this paper, a novel algorithm for image encryption based on SHA-512 is proposed. The main idea of the algorithm is to use one half of image data for encryption of the other half of the image reciprocally. Distinct characteristics of the algorithm are high security, high sensitivity and high speed that can be applied for encryption of gray-level and color images. The algorithm consists of two main sections: The first does preprocessing operation to shuffle one half of image. The second uses hash function to generate a random number mask. The mask is then XORed with the other part of the image which is going to be encrypted. The aim of this work is to increase the image entropy. Both security and performance aspects of the proposed algorithm are analyzed and satisfactory results are achieved in various rounds.","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":"121063785","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.2010.5941144
F. Daraee, S. Mozaffari
Process of bank checks and money notes play an important role in today commercial society. Usually automatic teller machines (ATM) have problems with eroded money notes. In this paper we proposed a new method for worn out Farsi money notes recognition using their texture content and wavelet transform. First, with the help of face detection algorithm, the obverse of the money note is separated from its reverse side. Then, central part of the money note, containing texture is extracted. Finally, wavelet transform is applied to this region of interest (ROI) to extract some features. Some distance measures are utilized to classify the input money note into predefined groups according to minimum distance. To increase accuracy of the system, in the post processing step, we used courtesy amount of the money note and template matching technique. The experimental results have shown that system performance is 80% for eroded money notes recognition.
{"title":"Eroded money notes recognition using wavelet transform","authors":"F. Daraee, S. Mozaffari","doi":"10.1109/IRANIANMVIP.2010.5941144","DOIUrl":"https://doi.org/10.1109/IRANIANMVIP.2010.5941144","url":null,"abstract":"Process of bank checks and money notes play an important role in today commercial society. Usually automatic teller machines (ATM) have problems with eroded money notes. In this paper we proposed a new method for worn out Farsi money notes recognition using their texture content and wavelet transform. First, with the help of face detection algorithm, the obverse of the money note is separated from its reverse side. Then, central part of the money note, containing texture is extracted. Finally, wavelet transform is applied to this region of interest (ROI) to extract some features. Some distance measures are utilized to classify the input money note into predefined groups according to minimum distance. To increase accuracy of the system, in the post processing step, we used courtesy amount of the money note and template matching technique. The experimental results have shown that system performance is 80% for eroded money notes recognition.","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":"122575833","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.2010.5941160
A. Javaherian, A. Movafeghi, R. Faghihi
This study modifies a Tikhonov regularized "maximum a posteriori" algorithm proposed for reconstructing both the conductivity changes and electrode positioning variations in EIT and uses this algorithm for reconstructing images of 2d elliptical and square models, instead of simple circular model used in previous works. This algorithm had been proposed By C. Gomez for compensating the errors due to electrode movements in image reconstruction. The jacobian matrix has been constructed via perturbation both conductivity and electrode positioning. The prior image matrix should incorporate some kind of augmented inter-electrode positioning correlations to impose a smoothness constraint on both the conductivity change distribution and electrode movement. For each model, conductivity change image is reconstructed in 3 cases: a) With no electrode displacement using standard algorithm b) With electrode displacement using standard algorithm c) With electrode displacement using proposed algorithm. In all models, a comparison between 3 cases has been implemented. Also, the results obtained from each model have been compared with the other models in similar cases. The results obtained in this study will be useful to investigate the ellipticity effects of organs being imaged in clinical applications. Moreover, the effects of model deviation from circular form on reconstructed images can be used in special industrial applications.
{"title":"Assessment and elimination of errors due to electrode displacements in elliptical and square models in EIT","authors":"A. Javaherian, A. Movafeghi, R. Faghihi","doi":"10.1109/IRANIANMVIP.2010.5941160","DOIUrl":"https://doi.org/10.1109/IRANIANMVIP.2010.5941160","url":null,"abstract":"This study modifies a Tikhonov regularized \"maximum a posteriori\" algorithm proposed for reconstructing both the conductivity changes and electrode positioning variations in EIT and uses this algorithm for reconstructing images of 2d elliptical and square models, instead of simple circular model used in previous works. This algorithm had been proposed By C. Gomez for compensating the errors due to electrode movements in image reconstruction. The jacobian matrix has been constructed via perturbation both conductivity and electrode positioning. The prior image matrix should incorporate some kind of augmented inter-electrode positioning correlations to impose a smoothness constraint on both the conductivity change distribution and electrode movement. For each model, conductivity change image is reconstructed in 3 cases: a) With no electrode displacement using standard algorithm b) With electrode displacement using standard algorithm c) With electrode displacement using proposed algorithm. In all models, a comparison between 3 cases has been implemented. Also, the results obtained from each model have been compared with the other models in similar cases. The results obtained in this study will be useful to investigate the ellipticity effects of organs being imaged in clinical applications. Moreover, the effects of model deviation from circular form on reconstructed images can be used in special industrial applications.","PeriodicalId":350778,"journal":{"name":"2010 6th Iranian Conference on Machine Vision and Image Processing","volume":"13 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":"131067307","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.2010.5941137
M. Shafiee, Z. Azimifar, P. Fieguth
We present a modified Temporal Conditional Random Fields framework for modeling and predicting object motion. To facilitate such a powerful graphical model with prediction and come up with a CRF-based predictor, we propose a set of new temporal relations for object tracking, with feature functions such as optical flow (calculated among consequent frames). We evaluate our proposed Temporal Conditional Random Field method with real and synthetic data sequences and will show that the TCRF prediction is nearly equivalent with result of template matching. Experimental results show that our proposed method estimates future target state with zero error until target dynamic changes. Our proposed modified CRF method with simple and easy to implement feature functions, can learn any target dynamic, thus, it can predict next state of target with zero error.
{"title":"Temporal Conditional Random Fields: A conditional state space predictor for visual tracking","authors":"M. Shafiee, Z. Azimifar, P. Fieguth","doi":"10.1109/IRANIANMVIP.2010.5941137","DOIUrl":"https://doi.org/10.1109/IRANIANMVIP.2010.5941137","url":null,"abstract":"We present a modified Temporal Conditional Random Fields framework for modeling and predicting object motion. To facilitate such a powerful graphical model with prediction and come up with a CRF-based predictor, we propose a set of new temporal relations for object tracking, with feature functions such as optical flow (calculated among consequent frames). We evaluate our proposed Temporal Conditional Random Field method with real and synthetic data sequences and will show that the TCRF prediction is nearly equivalent with result of template matching. Experimental results show that our proposed method estimates future target state with zero error until target dynamic changes. Our proposed modified CRF method with simple and easy to implement feature functions, can learn any target dynamic, thus, it can predict next state of target with zero error.","PeriodicalId":350778,"journal":{"name":"2010 6th Iranian Conference on Machine Vision and Image Processing","volume":"26 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":"131313511","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.2010.5941150
V. Anari, P. Mahzouni, R. Amirfattahi
This paper describes a computer-aided system for analyzing immunohistochemically stained meningioma cancer cell images. Accurate segmentation of cells in such images plays a critical role in diagnosing diffrent type of meningioma cancer. The methodpresented to automatically extract the positive cells in meninigioma tumor immunohistochemical pathology images based on HSV color space. First, according to distribution rules of positive cells in the HSV color space, it uses the component H, S and V as threshold conditions and leverages the maximal entropy principle to build a model to segment and extract positive cells. Experimental results shows that proposed algorithm can be used by pathologist to detection reliable quantitatively analyze the parameter of tumor cells and over come to disadvantages of the traditional approach.
{"title":"Automatic extraction of positive cells in pathology images of meningioma based on the maximal entropy principle and HSV color space","authors":"V. Anari, P. Mahzouni, R. Amirfattahi","doi":"10.1109/IRANIANMVIP.2010.5941150","DOIUrl":"https://doi.org/10.1109/IRANIANMVIP.2010.5941150","url":null,"abstract":"This paper describes a computer-aided system for analyzing immunohistochemically stained meningioma cancer cell images. Accurate segmentation of cells in such images plays a critical role in diagnosing diffrent type of meningioma cancer. The methodpresented to automatically extract the positive cells in meninigioma tumor immunohistochemical pathology images based on HSV color space. First, according to distribution rules of positive cells in the HSV color space, it uses the component H, S and V as threshold conditions and leverages the maximal entropy principle to build a model to segment and extract positive cells. Experimental results shows that proposed algorithm can be used by pathologist to detection reliable quantitatively analyze the parameter of tumor cells and over come to disadvantages of the traditional approach.","PeriodicalId":350778,"journal":{"name":"2010 6th Iranian Conference on Machine Vision and Image Processing","volume":"56 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":"115028591","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.2010.6313969
Maryam Ziaeefard, Hossein Ebrahimnezhad
In this paper, a new approach using Adaptive Neuro-Fuzzy Inference System (ANFIS) as a human action recognition system is proposed. ANFIS is an intelligence method which combines both fuzzy inference system and neural networks. The basis of the method is the representation of each action as a bivariate histogram that is computed from skeleton history image in one action duration. Skeleton image is extracted from the human silhouette in each frame then these images gather to generate skeleton history image. This approach automatically performs segmentation on the feature space with RANSAC algorithm to select some features yielded better results. Also some actions, which are similar in spatial features such as 'sit down' and 'stand up' but they are inverse in temporal domain, are discriminated with temporal window implemented in the first half duration. Real human action dataset, Weizmann, is selected for evaluation. The resulting average recognition rate of the proposed method is 98.3%.
{"title":"Human action recognition by RANSAC based salient features of skeleton history image using ANFIS","authors":"Maryam Ziaeefard, Hossein Ebrahimnezhad","doi":"10.1109/IRANIANMVIP.2010.6313969","DOIUrl":"https://doi.org/10.1109/IRANIANMVIP.2010.6313969","url":null,"abstract":"In this paper, a new approach using Adaptive Neuro-Fuzzy Inference System (ANFIS) as a human action recognition system is proposed. ANFIS is an intelligence method which combines both fuzzy inference system and neural networks. The basis of the method is the representation of each action as a bivariate histogram that is computed from skeleton history image in one action duration. Skeleton image is extracted from the human silhouette in each frame then these images gather to generate skeleton history image. This approach automatically performs segmentation on the feature space with RANSAC algorithm to select some features yielded better results. Also some actions, which are similar in spatial features such as 'sit down' and 'stand up' but they are inverse in temporal domain, are discriminated with temporal window implemented in the first half duration. Real human action dataset, Weizmann, is selected for evaluation. The resulting average recognition rate of the proposed method is 98.3%.","PeriodicalId":350778,"journal":{"name":"2010 6th Iranian Conference on Machine Vision and Image Processing","volume":"59 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":"114951684","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.2010.5941182
M. Abadi, S. Nikbakht
In this paper the new two-dimensional (TD) adaptive filter algorithms are introduced. The presented algorithms are TD variable step-size (VSS) normalized least mean squares (TD-VSS-NLMS) and TD-VSS affine projection algorithms (TD-VSS-APA). In these algorithms, the step-size changes during the adaptation which leads to the low steady-state mean square error (MSE), and fast convergence speed. We demonstrate the good performance of the derived algorithms in TD system identification and adaptive noise cancellation in digital images for image restoration.
{"title":"Two-dimensional variable step-size normalized least mean squares and affine projection adaptive filter algorithms","authors":"M. Abadi, S. Nikbakht","doi":"10.1109/IRANIANMVIP.2010.5941182","DOIUrl":"https://doi.org/10.1109/IRANIANMVIP.2010.5941182","url":null,"abstract":"In this paper the new two-dimensional (TD) adaptive filter algorithms are introduced. The presented algorithms are TD variable step-size (VSS) normalized least mean squares (TD-VSS-NLMS) and TD-VSS affine projection algorithms (TD-VSS-APA). In these algorithms, the step-size changes during the adaptation which leads to the low steady-state mean square error (MSE), and fast convergence speed. We demonstrate the good performance of the derived algorithms in TD system identification and adaptive noise cancellation in digital images for image restoration.","PeriodicalId":350778,"journal":{"name":"2010 6th Iranian Conference on Machine Vision and Image Processing","volume":"27 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":"125473088","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：