Pub Date : 2010-10-01DOI: 10.1109/IRANIANMVIP.2010.5941132
F. Moayedi, Alireza Kazemi, Z. Azimifar
This paper present a novel attempt to introduce an HMM-based multi-resolution and multi-cue segmentation in combination with the unscented Kalman filter tracking method. It combines multiple features distribution and multiple resolutions to facilitate 2D video tracking. The advantages of this method lie in its speed and its robustness. Speed is dramatically improved by taking into account multiple resolutions which reduce number of measurement points (number of HMM states) while keeping its quality. Robustness is achieved by using multiple cues. We propose an algorithm to find an optimal operating point for a tracker in terms of the image scale. Furthermore, we propose a faster multi-scale (spatial) tracker based on a minimum acceptable performance limit. The proposed method is demonstrated on human head tracking with a non-stationary camera. Visual tests indicate that the optimized algorithms produce qualitatively better results. Results show that we are able to maintain real-time processing on quite generous video resolutions. Therefore it will be shown that our approach is faster and more efficient than conventional UKF and UKF with multi-cue.
{"title":"Hidden Markov model-unscented Kalman filter contour tracking: A multi-cue and multi-resolution approach","authors":"F. Moayedi, Alireza Kazemi, Z. Azimifar","doi":"10.1109/IRANIANMVIP.2010.5941132","DOIUrl":"https://doi.org/10.1109/IRANIANMVIP.2010.5941132","url":null,"abstract":"This paper present a novel attempt to introduce an HMM-based multi-resolution and multi-cue segmentation in combination with the unscented Kalman filter tracking method. It combines multiple features distribution and multiple resolutions to facilitate 2D video tracking. The advantages of this method lie in its speed and its robustness. Speed is dramatically improved by taking into account multiple resolutions which reduce number of measurement points (number of HMM states) while keeping its quality. Robustness is achieved by using multiple cues. We propose an algorithm to find an optimal operating point for a tracker in terms of the image scale. Furthermore, we propose a faster multi-scale (spatial) tracker based on a minimum acceptable performance limit. The proposed method is demonstrated on human head tracking with a non-stationary camera. Visual tests indicate that the optimized algorithms produce qualitatively better results. Results show that we are able to maintain real-time processing on quite generous video resolutions. Therefore it will be shown that our approach is faster and more efficient than conventional UKF and UKF with multi-cue.","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":"126731836","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.2010.5941156
Zahra Zareizadeh, Reza P. R. Hasanzadeh, G. Baghersalimi
In this paper, an extended version of differential equations is introduced on the concept of edge detection methods for color images based on the correlation of R, G, and B components. To obtain the color edge detection operator, the Green's function approach is used to derive the obtained differential equation. The proposed color edge detection method is compared with other color edge detection methods on the several test images. The experimental results show the feasibility of the proposed approach.
{"title":"Vector-valued color image edge detection using Green function approach","authors":"Zahra Zareizadeh, Reza P. R. Hasanzadeh, G. Baghersalimi","doi":"10.1109/IRANIANMVIP.2010.5941156","DOIUrl":"https://doi.org/10.1109/IRANIANMVIP.2010.5941156","url":null,"abstract":"In this paper, an extended version of differential equations is introduced on the concept of edge detection methods for color images based on the correlation of R, G, and B components. To obtain the color edge detection operator, the Green's function approach is used to derive the obtained differential equation. The proposed color edge detection method is compared with other color edge detection methods on the several test images. The experimental results show the feasibility of the proposed approach.","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":"131380488","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.2010.5941151
V. Anari, P. Mahzouni, R. Amirfattahi
Immuonohistochemically images of meningioma which are stained by ki67 marker contain positive and negative cells. Accurate counting the number of positive and negative cells in such images play a critical role in diagnosing diffrent type of meningioma cancer. Since pathological images of meningioma contain complex cell cluster accurate cell counting methodology is a major challenge for pathologist physicians. In this paper we provide a computer aided algorithm for detecting proliferative cells and mitosis index in immunohistochemically images of meningioma. In the first stage of the algorithm fuzzy c-means clustering was used to extract positive and negative cells based on CIElab color space. In the second stage, ultraerosion operation was applied to count the number of individual and overlapped cells. Experimental result show that the proposed algorithm is able to overcome some disadvantage of traditional approaches with acceptable accuracy by pathologist physicians.
{"title":"Computer-aided detection of proliferative cells and mitosis index in immunohistichemically images of meningioma","authors":"V. Anari, P. Mahzouni, R. Amirfattahi","doi":"10.1109/IRANIANMVIP.2010.5941151","DOIUrl":"https://doi.org/10.1109/IRANIANMVIP.2010.5941151","url":null,"abstract":"Immuonohistochemically images of meningioma which are stained by ki67 marker contain positive and negative cells. Accurate counting the number of positive and negative cells in such images play a critical role in diagnosing diffrent type of meningioma cancer. Since pathological images of meningioma contain complex cell cluster accurate cell counting methodology is a major challenge for pathologist physicians. In this paper we provide a computer aided algorithm for detecting proliferative cells and mitosis index in immunohistochemically images of meningioma. In the first stage of the algorithm fuzzy c-means clustering was used to extract positive and negative cells based on CIElab color space. In the second stage, ultraerosion operation was applied to count the number of individual and overlapped cells. Experimental result show that the proposed algorithm is able to overcome some disadvantage of traditional approaches with acceptable accuracy by pathologist physicians.","PeriodicalId":350778,"journal":{"name":"2010 6th Iranian Conference on Machine Vision and Image Processing","volume":"91 1-2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132194029","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.2010.5941141
T. Sedghi, Majid Fakheri, M. Amirani
Content-Based Image Retrieval (CBIR) systems employ colour as primary feature with texture and shape as secondary features. Very few systems exploit spatial features. None of the available systems combines all three visual features, texture, shape and location, for organization and retrieval. In this paper a simple, image retrieval system is presented. The proposed system uses weighted combination of integrated texture features, shape features of texture regions.
{"title":"Features composition for proficient and real time retrieval in content based image retrieval system","authors":"T. Sedghi, Majid Fakheri, M. Amirani","doi":"10.1109/IRANIANMVIP.2010.5941141","DOIUrl":"https://doi.org/10.1109/IRANIANMVIP.2010.5941141","url":null,"abstract":"Content-Based Image Retrieval (CBIR) systems employ colour as primary feature with texture and shape as secondary features. Very few systems exploit spatial features. None of the available systems combines all three visual features, texture, shape and location, for organization and retrieval. In this paper a simple, image retrieval system is presented. The proposed system uses weighted combination of integrated texture features, shape features of texture regions.","PeriodicalId":350778,"journal":{"name":"2010 6th Iranian Conference on Machine Vision and Image Processing","volume":"25 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":"132350572","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.2010.5941138
M. Keshani, Z. Azimifar, R. Boostani, A. Shakibafar
In this paper, we propose an automatic lung nodule segmentation algorithm using computed tomography (CT) images. The main contribution is automatically detecting large or small non-isolated nodules connected to the chest wall and accurately segmenting solid and cavity nodules by active contour modeling. This method consists of several steps. First, the lung is segmented by active contour modeling. The initialization is the main core of this step. It causes to transfer non-isolated nodules into isolated ones. Then, regions of interest are detected using 2D stochastic features. After that, an anatomical 3D feature is used to detect nodules. Finally, contours of detected nodules are extracted by active contour modeling. At the end, the performance of our proposed method is reported by experimental results using clinical CT images. All nodules (including solid and cavity) are detected and the number of FP is 3/scan.
{"title":"Lung nodule segmentation using active contour modeling","authors":"M. Keshani, Z. Azimifar, R. Boostani, A. Shakibafar","doi":"10.1109/IRANIANMVIP.2010.5941138","DOIUrl":"https://doi.org/10.1109/IRANIANMVIP.2010.5941138","url":null,"abstract":"In this paper, we propose an automatic lung nodule segmentation algorithm using computed tomography (CT) images. The main contribution is automatically detecting large or small non-isolated nodules connected to the chest wall and accurately segmenting solid and cavity nodules by active contour modeling. This method consists of several steps. First, the lung is segmented by active contour modeling. The initialization is the main core of this step. It causes to transfer non-isolated nodules into isolated ones. Then, regions of interest are detected using 2D stochastic features. After that, an anatomical 3D feature is used to detect nodules. Finally, contours of detected nodules are extracted by active contour modeling. At the end, the performance of our proposed method is reported by experimental results using clinical CT images. All nodules (including solid and cavity) are detected and the number of FP is 3/scan.","PeriodicalId":350778,"journal":{"name":"2010 6th Iranian Conference on Machine Vision and Image Processing","volume":"282 2 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":"114258419","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.2010.5941162
F. Kheirkhah, S. Haghipour
The ongoing biotechnology revolution promises a complete understanding of the mechanisms by which cells and tissues carry out their functions. As proteins are integral components of cell function, it is critical to understand their properties such as structure and localization. The study of protein subcellular localization (PSL) is important for elucidating protein functions involved in various cellular processes. The subcellular location of proteins is most often determined by visual interpretation of fluorescence microscope images, but in recent years, to perform high-resolution, high-throughput analysis of ten thousands of expressed proteins for the many cell types and cellular conditions under which they may be found creates, automated methods that are needed. In this review, we use a novel method that determines an improved features set, that distinguish subcellular patterns with high accuracy and high speed. This method based on modified threshold adjacency statistics (MTAS), the essence which is to threshold the images. Previous work that uses threshold adjacency statistics (TAS), introduces a simple set of Subcellular Location Features (SLF) which are computed by counting the number of threshold pixels adjacent.
{"title":"Classification of subcellular location patterns in fluorescence microscope images based on modified threshold adjacency statistics","authors":"F. Kheirkhah, S. Haghipour","doi":"10.1109/IRANIANMVIP.2010.5941162","DOIUrl":"https://doi.org/10.1109/IRANIANMVIP.2010.5941162","url":null,"abstract":"The ongoing biotechnology revolution promises a complete understanding of the mechanisms by which cells and tissues carry out their functions. As proteins are integral components of cell function, it is critical to understand their properties such as structure and localization. The study of protein subcellular localization (PSL) is important for elucidating protein functions involved in various cellular processes. The subcellular location of proteins is most often determined by visual interpretation of fluorescence microscope images, but in recent years, to perform high-resolution, high-throughput analysis of ten thousands of expressed proteins for the many cell types and cellular conditions under which they may be found creates, automated methods that are needed. In this review, we use a novel method that determines an improved features set, that distinguish subcellular patterns with high accuracy and high speed. This method based on modified threshold adjacency statistics (MTAS), the essence which is to threshold the images. Previous work that uses threshold adjacency statistics (TAS), introduces a simple set of Subcellular Location Features (SLF) which are computed by counting the number of threshold pixels adjacent.","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":"126174443","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.2010.5941173
Ghassem Tofighi, S. A. Monadjemi, N. Ghasem-Aghaee
In this paper, we propose a real-time vision-based hand posture recognition approach, based on appearance-based features of hand. Our approach has three main steps: hand segmentation, feature extraction and posture recognition. For the hand segmentation, we introduce “Adaptive Histogram Template of Skin” which tries to extract histogram of the subject hand by sampling its color and texture. With this template, we can use back projection method to find skin color areas in an image. In the feature extraction step, we extract global hand's features using hand's edge contour, and hand's edge convex hull. The hand can be classified into one of the ten posture classes in the recognition step. Each posture class has a representative template which is used as reference for comparing to subject hand features. This approach is simple and fast enough to provide real-time recognition.
{"title":"Rapid hand posture recognition using Adaptive Histogram Template of Skin and hand edge contour","authors":"Ghassem Tofighi, S. A. Monadjemi, N. Ghasem-Aghaee","doi":"10.1109/IRANIANMVIP.2010.5941173","DOIUrl":"https://doi.org/10.1109/IRANIANMVIP.2010.5941173","url":null,"abstract":"In this paper, we propose a real-time vision-based hand posture recognition approach, based on appearance-based features of hand. Our approach has three main steps: hand segmentation, feature extraction and posture recognition. For the hand segmentation, we introduce “Adaptive Histogram Template of Skin” which tries to extract histogram of the subject hand by sampling its color and texture. With this template, we can use back projection method to find skin color areas in an image. In the feature extraction step, we extract global hand's features using hand's edge contour, and hand's edge convex hull. The hand can be classified into one of the ten posture classes in the recognition step. Each posture class has a representative template which is used as reference for comparing to subject hand features. This approach is simple and fast enough to provide real-time recognition.","PeriodicalId":350778,"journal":{"name":"2010 6th Iranian Conference on Machine Vision and Image Processing","volume":"3 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":"130153001","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.2010.5941155
Omid Sharifi Tehrani, M. Ashourian, P. Moallem
An FPGA-based channel noise canceller using a fixed-point standard-LMS algorithm for image transmission is proposed. The proposed core is designed in VHDL93 language as basis of FIR adaptive filter. The proposed model uses 12-bits word-length for digital input data while internal computations are based on 17-bits word-length because of considering guard bits to prevent overflow. The designed core is FPGA-brand-independent, thus can be implemented on any brand to create a system-on-programmable-chip (SoPC). In this paper, XILINX SPARTAN3E and VIRTEX4 FPGA series are used as implementation platform. A discussion is made on DSP, Hardware/Software co-design and pure-hardware implementations. Although using a pure-hardware implementation results in better performance, it is more complex than other structures. Results obtained show improvements in area-resource utilization, convergence speed and performance in the designed pure-hardware channel noise canceller core.
{"title":"FPGA implementation of a channel noise canceller for image transmission","authors":"Omid Sharifi Tehrani, M. Ashourian, P. Moallem","doi":"10.1109/IRANIANMVIP.2010.5941155","DOIUrl":"https://doi.org/10.1109/IRANIANMVIP.2010.5941155","url":null,"abstract":"An FPGA-based channel noise canceller using a fixed-point standard-LMS algorithm for image transmission is proposed. The proposed core is designed in VHDL93 language as basis of FIR adaptive filter. The proposed model uses 12-bits word-length for digital input data while internal computations are based on 17-bits word-length because of considering guard bits to prevent overflow. The designed core is FPGA-brand-independent, thus can be implemented on any brand to create a system-on-programmable-chip (SoPC). In this paper, XILINX SPARTAN3E and VIRTEX4 FPGA series are used as implementation platform. A discussion is made on DSP, Hardware/Software co-design and pure-hardware implementations. Although using a pure-hardware implementation results in better performance, it is more complex than other structures. Results obtained show improvements in area-resource utilization, convergence speed and performance in the designed pure-hardware channel noise canceller core.","PeriodicalId":350778,"journal":{"name":"2010 6th Iranian Conference on Machine Vision and Image Processing","volume":"26 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131923594","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.2010.6401476
M. Avanaki, A. Podoleanu, Hamid Sarmadi, A. Meadway, S. A. Hojjatoleslami
The imperfection of optical devices in confocal imaging system deteriorates wavefront which results in aberration. This reduces the optical signal to noise ratio of the imaging system and the quality of the produced images. Adaptive optics composed of wavefront sensor and deformable mirror is a straightforward solution for this problem. In this paper, we described a blind optimization technique with an in-expensive electronics without using the sensor to correct the aberration in order to achieve better quality images. The correction system includes a deformable mirror with 52 actuators which are controlled by particle swarm optimization (PSO) algorithm.
{"title":"Blind optimization for aberration correction in confocal imaging system","authors":"M. Avanaki, A. Podoleanu, Hamid Sarmadi, A. Meadway, S. A. Hojjatoleslami","doi":"10.1109/IRANIANMVIP.2010.6401476","DOIUrl":"https://doi.org/10.1109/IRANIANMVIP.2010.6401476","url":null,"abstract":"The imperfection of optical devices in confocal imaging system deteriorates wavefront which results in aberration. This reduces the optical signal to noise ratio of the imaging system and the quality of the produced images. Adaptive optics composed of wavefront sensor and deformable mirror is a straightforward solution for this problem. In this paper, we described a blind optimization technique with an in-expensive electronics without using the sensor to correct the aberration in order to achieve better quality images. The correction system includes a deformable mirror with 52 actuators which are controlled by particle swarm optimization (PSO) algorithm.","PeriodicalId":350778,"journal":{"name":"2010 6th Iranian Conference on Machine Vision and Image Processing","volume":"23 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":"128209206","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.2010.5941134
M. Ziashahabi, H. Sadjedi, H. Khezripour
Defects on the Pipeline surface such as cracks cause main problems for governments, specifically when the pipeline is covered under the ground. Manual examination for surface defects in the pipeline has several disadvantages, including varying standards, and high cost. In this paper, a combination of two algorithms based on mathematical morphology and curvature evaluation for segmentation of defects is proposed. Then, we use fuzzy k-means clustering to classify pipe defects. The proposed method can be completely automated and has been tested on more than 250 scanned images of petroleum pipelines of Iran.
{"title":"Automatic segmentation and classification of pipeline images using mathematic morphology and fuzzy k-means algorithm","authors":"M. Ziashahabi, H. Sadjedi, H. Khezripour","doi":"10.1109/IRANIANMVIP.2010.5941134","DOIUrl":"https://doi.org/10.1109/IRANIANMVIP.2010.5941134","url":null,"abstract":"Defects on the Pipeline surface such as cracks cause main problems for governments, specifically when the pipeline is covered under the ground. Manual examination for surface defects in the pipeline has several disadvantages, including varying standards, and high cost. In this paper, a combination of two algorithms based on mathematical morphology and curvature evaluation for segmentation of defects is proposed. Then, we use fuzzy k-means clustering to classify pipe defects. The proposed method can be completely automated and has been tested on more than 250 scanned images of petroleum pipelines of Iran.","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":"134159736","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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