M. Awrangjeb, Guojun Lu, C. Fraser, M. Ravanbakhsh
The previously proposed contour-based multi-scale corner detector based on the chord-to-point distance accumulation (CPDA) technique has proved its superior robustness over many other single- and multi-scale detectors. However, the original CPDA detector is computationally expensive since it calculates the CPDA discrete curvature on each point of the curve. The proposed improvement obtains a set of probable candidate points before the CPDA curvature estimation. The CPDA curvature is estimated on these chosen candidate points only. Consequently, the improved CPDA detector becomes faster, while retaining a similar robustness to the original CPDA detector.
{"title":"A Fast Corner Detector Based on the Chord-to-Point Distance Accumulation Technique","authors":"M. Awrangjeb, Guojun Lu, C. Fraser, M. Ravanbakhsh","doi":"10.1109/DICTA.2009.91","DOIUrl":"https://doi.org/10.1109/DICTA.2009.91","url":null,"abstract":"The previously proposed contour-based multi-scale corner detector based on the chord-to-point distance accumulation (CPDA) technique has proved its superior robustness over many other single- and multi-scale detectors. However, the original CPDA detector is computationally expensive since it calculates the CPDA discrete curvature on each point of the curve. The proposed improvement obtains a set of probable candidate points before the CPDA curvature estimation. The CPDA curvature is estimated on these chosen candidate points only. Consequently, the improved CPDA detector becomes faster, while retaining a similar robustness to the original CPDA detector.","PeriodicalId":277395,"journal":{"name":"2009 Digital Image Computing: Techniques and Applications","volume":"115 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123466481","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}
This paper shows that most surveillance cameras fall well short of providing sufficient image quality, in both spatial resolution and colour reproduction, for the reliable identification of faces. In addition, the low resolution of surveillance images means that when compression is applied the MPEG/JPEG DCT block size can be such that the spatial frequencies most important for face recognition are corrupted. Making things even worse, the compression process heavily quantizes colour information disrupting the use of pigmentation information to recognize faces. Indeed, the term 'security camera' is probably misplaced. Many surveillance cameras are legally blind, or nearly so.
{"title":"Video Surveillance: Legally Blind?","authors":"P. Kovesi","doi":"10.1109/DICTA.2009.41","DOIUrl":"https://doi.org/10.1109/DICTA.2009.41","url":null,"abstract":"This paper shows that most surveillance cameras fall well short of providing sufficient image quality, in both spatial resolution and colour reproduction, for the reliable identification of faces. In addition, the low resolution of surveillance images means that when compression is applied the MPEG/JPEG DCT block size can be such that the spatial frequencies most important for face recognition are corrupted. Making things even worse, the compression process heavily quantizes colour information disrupting the use of pigmentation information to recognize faces. Indeed, the term 'security camera' is probably misplaced. Many surveillance cameras are legally blind, or nearly so.","PeriodicalId":277395,"journal":{"name":"2009 Digital Image Computing: Techniques and Applications","volume":"44 9","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114120510","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}
This paper addresses the challenges of flood mapping using multispectral images. Quantitative flood mapping is critical for flood damage assessment and management. Remote sensing images obtained from various satellite or airborne sensors provide valuable data for this application, from which the information on the extent of flood can be extracted. However the great challenge involved in the data interpretation is to achieve more reliable flood extent mapping including both the fully inundated areas and the ‘wet’ areas where trees and houses are partly covered by water. This is a typical combined pure pixel and mixed pixel problem. In this paper, an extended Support Vector Machines method for spectral unmixing developed recently has been applied to generate an integrated map showing both pure pixels (fully inundated areas) and mixed pixels (trees and houses partly covered by water). The outputs were compared with the conventional mean based linear spectral mixture model, and better performance was demonstrated with a subset of Landsat ETM+ data recorded at the Daly River Basin, NT, Australia, on 3rd March, 2008, after a flood event.
{"title":"Mixed Pixel Analysis for Flood Mapping Using Extended Support Vector Machine","authors":"C. Dey, X. Jia, D. Fraser, L. Wang","doi":"10.1109/DICTA.2009.55","DOIUrl":"https://doi.org/10.1109/DICTA.2009.55","url":null,"abstract":"This paper addresses the challenges of flood mapping using multispectral images. Quantitative flood mapping is critical for flood damage assessment and management. Remote sensing images obtained from various satellite or airborne sensors provide valuable data for this application, from which the information on the extent of flood can be extracted. However the great challenge involved in the data interpretation is to achieve more reliable flood extent mapping including both the fully inundated areas and the ‘wet’ areas where trees and houses are partly covered by water. This is a typical combined pure pixel and mixed pixel problem. In this paper, an extended Support Vector Machines method for spectral unmixing developed recently has been applied to generate an integrated map showing both pure pixels (fully inundated areas) and mixed pixels (trees and houses partly covered by water). The outputs were compared with the conventional mean based linear spectral mixture model, and better performance was demonstrated with a subset of Landsat ETM+ data recorded at the Daly River Basin, NT, Australia, on 3rd March, 2008, after a flood event.","PeriodicalId":277395,"journal":{"name":"2009 Digital Image Computing: Techniques and Applications","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114194679","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}
Images captured in foggy weather conditions exhibit losses in quality which are dependent on distance. If the depth and atmospheric conditions are known, one can enhance the images (to some degree) by compensating for the effects of the fog. Recently, several investigations have presented methods for recovering depth maps using only the information contained in a single foggy image. Each technique estimates the depth of each pixel independently, and assumes neighbouring pixels will have similar depths. In this work, we employ the fact that images containing fog are captured from outdoor cameras. As a result, the scene geometry is usually dominated by a ground plane. More importantly, objects which appear towards the top of the image are usually further away. We show how this preference (implemented as a soft constraint) is compatible with the alpha-expansion optimization technique and illustrate how it can be used to improve the robustness of any single image dehazing technique.
{"title":"Improved Single Image Dehazing Using Geometry","authors":"Peter Carr, R. Hartley","doi":"10.1109/DICTA.2009.25","DOIUrl":"https://doi.org/10.1109/DICTA.2009.25","url":null,"abstract":"Images captured in foggy weather conditions exhibit losses in quality which are dependent on distance. If the depth and atmospheric conditions are known, one can enhance the images (to some degree) by compensating for the effects of the fog. Recently, several investigations have presented methods for recovering depth maps using only the information contained in a single foggy image. Each technique estimates the depth of each pixel independently, and assumes neighbouring pixels will have similar depths. In this work, we employ the fact that images containing fog are captured from outdoor cameras. As a result, the scene geometry is usually dominated by a ground plane. More importantly, objects which appear towards the top of the image are usually further away. We show how this preference (implemented as a soft constraint) is compatible with the alpha-expansion optimization technique and illustrate how it can be used to improve the robustness of any single image dehazing technique.","PeriodicalId":277395,"journal":{"name":"2009 Digital Image Computing: Techniques and Applications","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114799213","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}
Benson S. Y. Lam, Yongsheng Gao, Alan Wee-Chung Liew
Due to the spherical shape nature of retina and the illumination effect, detecting bright lesions in a retinal image is a challenging problem. Existing methods depend heavily on a prior knowledge about lesions, which either a user-defined parameter is employed or a supervised learning technique is adopted to estimate the parameter. In this paper, a novel sharpness measure is proposed, which indicates the degree of sharpness of bright lesions in the whole retinal image. It has a sudden jump at the optimal parameter. A polynomial fitting technique is used to capture this jump. We have tested our method on a public available dataset. Experimental results show that the proposed unsupervised approach is able to detect bright lesions accurately in an unhealthy retinal image and it outperforms existing supervised learning method. Also, the proposed method reports no abnormality for a healthy retinal image.
{"title":"Optimizing Sharpness Measure for Bright Lesion Detection in Retinal Image Analysis","authors":"Benson S. Y. Lam, Yongsheng Gao, Alan Wee-Chung Liew","doi":"10.1109/DICTA.2009.14","DOIUrl":"https://doi.org/10.1109/DICTA.2009.14","url":null,"abstract":"Due to the spherical shape nature of retina and the illumination effect, detecting bright lesions in a retinal image is a challenging problem. Existing methods depend heavily on a prior knowledge about lesions, which either a user-defined parameter is employed or a supervised learning technique is adopted to estimate the parameter. In this paper, a novel sharpness measure is proposed, which indicates the degree of sharpness of bright lesions in the whole retinal image. It has a sudden jump at the optimal parameter. A polynomial fitting technique is used to capture this jump. We have tested our method on a public available dataset. Experimental results show that the proposed unsupervised approach is able to detect bright lesions accurately in an unhealthy retinal image and it outperforms existing supervised learning method. Also, the proposed method reports no abnormality for a healthy retinal image.","PeriodicalId":277395,"journal":{"name":"2009 Digital Image Computing: Techniques and Applications","volume":"475 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127555572","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}
Accurate segmentation is a crucial phase in the implementation of an iris recognition system. In this paper we investigate a novel technique for iris segmentation. Morphological operations and area computation are applied together with other iris segmentation techniques in order to increase the speed and accuracy of the preprocessing phase. A rough approximation of the pupil’s location is first determined in the initial stage, followed by edge detection and circular Hough transform for accurate iris segmentation. The edge image used to localize the outer iris border is modified increasing the speed and accuracy of the process. Finally, we investigate the effect of eyelids detection using a parabolic curve fitting technique. Two data sets of eye images are used to evaluate the proposed techniques. Experimental results show that the proposed segmentation technique is efficient and performs well on both data sets of images.
{"title":"An Efficient and Accurate Iris Segmentation Technique","authors":"Nitin K. Mahadeo, Nandita Bhattacharjee","doi":"10.1109/DICTA.2009.65","DOIUrl":"https://doi.org/10.1109/DICTA.2009.65","url":null,"abstract":"Accurate segmentation is a crucial phase in the implementation of an iris recognition system. In this paper we investigate a novel technique for iris segmentation. Morphological operations and area computation are applied together with other iris segmentation techniques in order to increase the speed and accuracy of the preprocessing phase. A rough approximation of the pupil’s location is first determined in the initial stage, followed by edge detection and circular Hough transform for accurate iris segmentation. The edge image used to localize the outer iris border is modified increasing the speed and accuracy of the process. Finally, we investigate the effect of eyelids detection using a parabolic curve fitting technique. Two data sets of eye images are used to evaluate the proposed techniques. Experimental results show that the proposed segmentation technique is efficient and performs well on both data sets of images.","PeriodicalId":277395,"journal":{"name":"2009 Digital Image Computing: Techniques and Applications","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131981298","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}
Y. Gal, A. Mehnert, A. Bradley, D. Kennedy, S. Crozier
The clinical interpretation of breast MRI remains largely subjective, and the reported findings qualitative. Although the sensitivity of the method for detecting breast cancer is high, its specificity is poor. Computerised interpretation offers the possibility of improving specificity through objective quantitative measurement. This paper reviews the plethora of such features that have been proposed and presents a preliminary study of the most discriminatory features for dynamic contrast-enhanced MRI of the breast. In particular the results of a feature/classifier selection experiment are presented based on 20 lesions (10 malignant and 10 benign) from 20 routine clinical breast MRI examinations. Each lesion was segmented manually by a clinical radiographer and its diagnostic status confirmed by cytopathology or histopathology. The results show that textural and kinetic, rather than morphometric, features are the most important for lesion classification. They also show that the SVM classifier with sigmoid kernel performs better than other well-known classifiers: Fisher's linear discriminant function, Bayes linear classifier, logistic regression, and SVM with other kernels (distance, exponential, and radial).
{"title":"Feature and Classifier Selection for Automatic Classification of Lesions in Dynamic Contrast-Enhanced MRI of the Breast","authors":"Y. Gal, A. Mehnert, A. Bradley, D. Kennedy, S. Crozier","doi":"10.1109/DICTA.2009.29","DOIUrl":"https://doi.org/10.1109/DICTA.2009.29","url":null,"abstract":"The clinical interpretation of breast MRI remains largely subjective, and the reported findings qualitative. Although the sensitivity of the method for detecting breast cancer is high, its specificity is poor. Computerised interpretation offers the possibility of improving specificity through objective quantitative measurement. This paper reviews the plethora of such features that have been proposed and presents a preliminary study of the most discriminatory features for dynamic contrast-enhanced MRI of the breast. In particular the results of a feature/classifier selection experiment are presented based on 20 lesions (10 malignant and 10 benign) from 20 routine clinical breast MRI examinations. Each lesion was segmented manually by a clinical radiographer and its diagnostic status confirmed by cytopathology or histopathology. The results show that textural and kinetic, rather than morphometric, features are the most important for lesion classification. They also show that the SVM classifier with sigmoid kernel performs better than other well-known classifiers: Fisher's linear discriminant function, Bayes linear classifier, logistic regression, and SVM with other kernels (distance, exponential, and radial).","PeriodicalId":277395,"journal":{"name":"2009 Digital Image Computing: Techniques and Applications","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134414454","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}
In public venues, crowd size is a key indicator of crowd safety and stability. Crowding levels can be detected using holistic image features, however this requires a large amount of training data to capture the wide variations in crowd distribution. If a crowd counting algorithm is to be deployed across a large number of cameras, such a large and burdensome training requirement is far from ideal. In this paper we propose an approach that uses local features to count the number of people in each foreground blob segment, so that the total crowd estimate is the sum of the group sizes. This results in an approach that is scalable to crowd volumes not seen in the training data, and can be trained on a very small data set. As a local approach is used, the proposed algorithm can easily be used to estimate crowd density throughout different regions of the scene and be used in a multi-camera environment. A unique localised approach to ground truth annotation reduces the required training data is also presented, as a localised approach to crowd counting has different training requirements to a holistic one. Testing on a large pedestrian database compares the proposed technique to existing holistic techniques and demonstrates improved accuracy, and superior performance when test conditions are unseen in the training set, or a minimal training set is used.
{"title":"Crowd Counting Using Multiple Local Features","authors":"D. Ryan, S. Denman, C. Fookes, S. Sridharan","doi":"10.1109/DICTA.2009.22","DOIUrl":"https://doi.org/10.1109/DICTA.2009.22","url":null,"abstract":"In public venues, crowd size is a key indicator of crowd safety and stability. Crowding levels can be detected using holistic image features, however this requires a large amount of training data to capture the wide variations in crowd distribution. If a crowd counting algorithm is to be deployed across a large number of cameras, such a large and burdensome training requirement is far from ideal. In this paper we propose an approach that uses local features to count the number of people in each foreground blob segment, so that the total crowd estimate is the sum of the group sizes. This results in an approach that is scalable to crowd volumes not seen in the training data, and can be trained on a very small data set. As a local approach is used, the proposed algorithm can easily be used to estimate crowd density throughout different regions of the scene and be used in a multi-camera environment. A unique localised approach to ground truth annotation reduces the required training data is also presented, as a localised approach to crowd counting has different training requirements to a holistic one. Testing on a large pedestrian database compares the proposed technique to existing holistic techniques and demonstrates improved accuracy, and superior performance when test conditions are unseen in the training set, or a minimal training set is used.","PeriodicalId":277395,"journal":{"name":"2009 Digital Image Computing: Techniques and Applications","volume":"237 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115070553","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}
Wen-Shan Wang, Ya-Fei Hung, Jen-Kuei Yang, S. Tseng
Connected component labeling is an indispensable and one of most time consuming tasks of the applications in computer vision. Many labeling algorithms have been introduced, such as scan plus connection table, scan plus union-find, and contour tracing etc. They would rather use byte data than bit data to represent the binary pixel, which is either 1 or 0, due to the heavy cost of bitwise operations. This paper will propose a mechanism employing bit data to stand for the binary image pixels and labeling multiple pixels in one labeling process so that it can turn the weakness of bit data into the strength. According to the test results run in ARM926EJ-S, this new mechanism can double the speed of the scanning and analysis phases of an array based scan plus union-find algorithm. Besides, the much smaller binary image buffer needed by this mechanism is critical for the limited hardware-resource embedded devices, which are implemented in the field of computer vision gradually.
{"title":"The Dynamic Decision Switch for Multiple Pixel Connected Component Labeling Algorithm","authors":"Wen-Shan Wang, Ya-Fei Hung, Jen-Kuei Yang, S. Tseng","doi":"10.1109/DICTA.2009.30","DOIUrl":"https://doi.org/10.1109/DICTA.2009.30","url":null,"abstract":"Connected component labeling is an indispensable and one of most time consuming tasks of the applications in computer vision. Many labeling algorithms have been introduced, such as scan plus connection table, scan plus union-find, and contour tracing etc. They would rather use byte data than bit data to represent the binary pixel, which is either 1 or 0, due to the heavy cost of bitwise operations. This paper will propose a mechanism employing bit data to stand for the binary image pixels and labeling multiple pixels in one labeling process so that it can turn the weakness of bit data into the strength. According to the test results run in ARM926EJ-S, this new mechanism can double the speed of the scanning and analysis phases of an array based scan plus union-find algorithm. Besides, the much smaller binary image buffer needed by this mechanism is critical for the limited hardware-resource embedded devices, which are implemented in the field of computer vision gradually.","PeriodicalId":277395,"journal":{"name":"2009 Digital Image Computing: Techniques and Applications","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124976365","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}
Automatic cell segmentation and dead cell detection in microscopic images play a very important role in the study of the behaviour of lymphocytes. In this paper, a distance and watershed transforms based cell segmentation algorithm has been proposed to segment cells by using CFSE image, and a dead cell detection algorithm is also proposed to detect cell dead event. Experimental results have shown that the proposed algorithms are pretty robust to variable contrast microscopy image data, and variable cell densities, and the average cell detection rate has reached 93% with the average miss detection rate about 7%, and extremely low average false detection rate of 0.7%, and the dead cell rate is about 11%.
{"title":"Microscopic Cell Segmentation and Dead Cell Detection Based on CFSE and PI Images by Using Distance and Watershed Transforms","authors":"E. Cheng, S. Challa, R. Chakravorty","doi":"10.1109/DICTA.2009.16","DOIUrl":"https://doi.org/10.1109/DICTA.2009.16","url":null,"abstract":"Automatic cell segmentation and dead cell detection in microscopic images play a very important role in the study of the behaviour of lymphocytes. In this paper, a distance and watershed transforms based cell segmentation algorithm has been proposed to segment cells by using CFSE image, and a dead cell detection algorithm is also proposed to detect cell dead event. Experimental results have shown that the proposed algorithms are pretty robust to variable contrast microscopy image data, and variable cell densities, and the average cell detection rate has reached 93% with the average miss detection rate about 7%, and extremely low average false detection rate of 0.7%, and the dead cell rate is about 11%.","PeriodicalId":277395,"journal":{"name":"2009 Digital Image Computing: Techniques and Applications","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130031916","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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