In this study,a novel hybrid Water Cycle Moth-Flame Optimization(WCMFO)algorithm is proposed for multilevel thresholding brain image segmentation in Magnetic Resonance(MR)image slices.WCMFO constitutes a hybrid betwee...In this study,a novel hybrid Water Cycle Moth-Flame Optimization(WCMFO)algorithm is proposed for multilevel thresholding brain image segmentation in Magnetic Resonance(MR)image slices.WCMFO constitutes a hybrid between the two techniques,comprising the water cycle and moth-flame optimization algorithms.The optimal thresholds are obtained by maximizing the between class variance(Otsu’s function)of the image.To test the performance of threshold searching process,the proposed algorithm has been evaluated on standard benchmark of ten axial T2-weighted brain MR images for image segmentation.The experimental outcomes infer that it produces better optimal threshold values at a greater and quicker convergence rate.In contrast to other state-of-the-art methods,namely Adaptive Wind Driven Optimization(AWDO),Adaptive Bacterial Foraging(ABF)and Particle Swarm Optimization(PSO),the proposed algorithm has been found to be better at producing the best objective function,Peak Signal-to-Noise Ratio(PSNR),Standard Deviation(STD)and lower computational time values.Further,it was observed thatthe segmented image gives greater detail when the threshold level increases.Moreover,the statistical test result confirms that the best and mean values are almost zero and the average difference between best and mean value 1.86 is obtained through the 30 executions of the proposed algorithm.Thus,these images will lead to better segments of gray,white and cerebrospinal fluid that enable better clinical choices and diagnoses using a proposed algorithm.展开更多
In the traditional pattern classification method,it usually assumes that the object to be classified must lie in one of given(known)classes of the training data set.However,the training data set may not contain the cl...In the traditional pattern classification method,it usually assumes that the object to be classified must lie in one of given(known)classes of the training data set.However,the training data set may not contain the class of some objects in practice,and this is considered as an Open-Set Recognition(OSR)problem.In this paper,we propose a new progressive open-set recognition method with adaptive probability threshold.Both the labeled training data and the test data(objects to be classified)are put into a common data set,and the k-Nearest Neighbors(k-NNs)of each object are sought in this common set.Then,we can determine the probability of object lying in the given classes.If the majority of k-NNs of the object are from labeled training data,this object quite likely belongs to one of the given classes,and the density of the object and its neighbors is taken into account here.However,when most of k-NNs are from the unlabeled test data set,the class of object is considered very uncertain because the class of test data is unknown,and this object cannot be classified in this step.Once the objects belonging to known classes with high probability are all found,we re-calculate the probability of the other uncertain objects belonging to known classes based on the labeled training data and the objects marked with the estimated probability.Such iteration will stop when the probabilities of all the objects belonging to known classes are not changed.Then,a modified Otsu’s method is employed to adaptively seek the probability threshold for the final classification.If the probability of object belonging to known classes is smaller than this threshold,it will be assigned to the ignorant(unknown)class that is not included in training data set.The other objects will be committed to a specific class.The effectiveness of the proposed method has been validated using some experiments.展开更多
The spatial pattern of rice paddies is an essential parameter used for studies of greenhouse gas emissions,agricultural resource management,and environmental monitoring.On large spatial scales,previous studies have us...The spatial pattern of rice paddies is an essential parameter used for studies of greenhouse gas emissions,agricultural resource management,and environmental monitoring.On large spatial scales,previous studies have usually mapped rice paddies using a single vegetation index product based on a traditional classification method,or a combined analysis of various vegetation and water indices derived from the moderate resolution imaging spectroradiometer(MODIS)satellite data.However,different indices increase the computational cost and constrain the satellite data sources,and traditional classification methods(e.g.,maximum likelihood classification)may be time-consuming and difficult to carry out over a large area like China.In this study,we designed an auto-thresholding and single vegetation index(normalized difference vegetation index(NDVI))-based procedure to estimate the spatial distribution of rice paddies in China.The MOD09Q1 product,which was available at MODIS’s highest spatial resolution(250 m),was taken as the input source.An auto-threshold function was also introduced into the change detection process to distinguish rice paddies from other croplands.Our MODIS-derived maps were validated with ground surveys and then compared with China national statistical data of rice paddy areas.The results indicated that the best classification result was achieved for plain regions,and that the accuracy declined for hilly regions,where the complex landscape could lead to an underestimation of the rice paddy area.A comparison between the modeled results and other analyses using 500-m MODIS data suggests that rice paddies may be identified routinely using a single vegetation index with finer resolution on large spatial scales.展开更多
This paper describes the preliminary study results of developing a hypervelocity terminal intercept guidance system of a multiple kinetic-energy impactor vehicle(MKIV).The proposed MKIV system is intended to fragment ...This paper describes the preliminary study results of developing a hypervelocity terminal intercept guidance system of a multiple kinetic-energy impactor vehicle(MKIV).The proposed MKIV system is intended to fragment or pulverize an asteroid of smaller than approximately 150 m in diameter that is detected with a mission lead time of shorter than 10 years,without using nuclear explosive devices.This paper focuses on the development of a new image processing algorithm based on Otsu’s method for the coordinated terminal intercept guidance and control of multiple kinetic-energy impactors employing visual and/or infrared sensors.A scaled polyhedron shape model of asteroid(216)Kleopatra is used as a fictional target asteroid.GPU-based simulation results demonstrate the feasibility of impacting a small irregular-shaped asteroid by using the proposed new image processing algorithm and a classical pulsed TPN(true proportional navigation)terminal guidance law.展开更多
文摘In this study,a novel hybrid Water Cycle Moth-Flame Optimization(WCMFO)algorithm is proposed for multilevel thresholding brain image segmentation in Magnetic Resonance(MR)image slices.WCMFO constitutes a hybrid between the two techniques,comprising the water cycle and moth-flame optimization algorithms.The optimal thresholds are obtained by maximizing the between class variance(Otsu’s function)of the image.To test the performance of threshold searching process,the proposed algorithm has been evaluated on standard benchmark of ten axial T2-weighted brain MR images for image segmentation.The experimental outcomes infer that it produces better optimal threshold values at a greater and quicker convergence rate.In contrast to other state-of-the-art methods,namely Adaptive Wind Driven Optimization(AWDO),Adaptive Bacterial Foraging(ABF)and Particle Swarm Optimization(PSO),the proposed algorithm has been found to be better at producing the best objective function,Peak Signal-to-Noise Ratio(PSNR),Standard Deviation(STD)and lower computational time values.Further,it was observed thatthe segmented image gives greater detail when the threshold level increases.Moreover,the statistical test result confirms that the best and mean values are almost zero and the average difference between best and mean value 1.86 is obtained through the 30 executions of the proposed algorithm.Thus,these images will lead to better segments of gray,white and cerebrospinal fluid that enable better clinical choices and diagnoses using a proposed algorithm.
基金supported by the National Natural Science Foundation of China(Nos.U20B2067).
文摘In the traditional pattern classification method,it usually assumes that the object to be classified must lie in one of given(known)classes of the training data set.However,the training data set may not contain the class of some objects in practice,and this is considered as an Open-Set Recognition(OSR)problem.In this paper,we propose a new progressive open-set recognition method with adaptive probability threshold.Both the labeled training data and the test data(objects to be classified)are put into a common data set,and the k-Nearest Neighbors(k-NNs)of each object are sought in this common set.Then,we can determine the probability of object lying in the given classes.If the majority of k-NNs of the object are from labeled training data,this object quite likely belongs to one of the given classes,and the density of the object and its neighbors is taken into account here.However,when most of k-NNs are from the unlabeled test data set,the class of object is considered very uncertain because the class of test data is unknown,and this object cannot be classified in this step.Once the objects belonging to known classes with high probability are all found,we re-calculate the probability of the other uncertain objects belonging to known classes based on the labeled training data and the objects marked with the estimated probability.Such iteration will stop when the probabilities of all the objects belonging to known classes are not changed.Then,a modified Otsu’s method is employed to adaptively seek the probability threshold for the final classification.If the probability of object belonging to known classes is smaller than this threshold,it will be assigned to the ignorant(unknown)class that is not included in training data set.The other objects will be committed to a specific class.The effectiveness of the proposed method has been validated using some experiments.
基金financially supported by the Strategic Priority Research Program of Chinese Academy of Sciences—Climate Change:Carbon Budget and Relevant Issues(No.XDA05020200)the Startup Foundation for Introducing Talent of Nanjing University of Information Science and Technology(NUIST),China(No.2016r036)the Innovation and Entrepreneurship Training Program for College Students of Jiangsu Provincial Department of Education,China(No.2017103000165)
文摘The spatial pattern of rice paddies is an essential parameter used for studies of greenhouse gas emissions,agricultural resource management,and environmental monitoring.On large spatial scales,previous studies have usually mapped rice paddies using a single vegetation index product based on a traditional classification method,or a combined analysis of various vegetation and water indices derived from the moderate resolution imaging spectroradiometer(MODIS)satellite data.However,different indices increase the computational cost and constrain the satellite data sources,and traditional classification methods(e.g.,maximum likelihood classification)may be time-consuming and difficult to carry out over a large area like China.In this study,we designed an auto-thresholding and single vegetation index(normalized difference vegetation index(NDVI))-based procedure to estimate the spatial distribution of rice paddies in China.The MOD09Q1 product,which was available at MODIS’s highest spatial resolution(250 m),was taken as the input source.An auto-threshold function was also introduced into the change detection process to distinguish rice paddies from other croplands.Our MODIS-derived maps were validated with ground surveys and then compared with China national statistical data of rice paddy areas.The results indicated that the best classification result was achieved for plain regions,and that the accuracy declined for hilly regions,where the complex landscape could lead to an underestimation of the rice paddy area.A comparison between the modeled results and other analyses using 500-m MODIS data suggests that rice paddies may be identified routinely using a single vegetation index with finer resolution on large spatial scales.
文摘This paper describes the preliminary study results of developing a hypervelocity terminal intercept guidance system of a multiple kinetic-energy impactor vehicle(MKIV).The proposed MKIV system is intended to fragment or pulverize an asteroid of smaller than approximately 150 m in diameter that is detected with a mission lead time of shorter than 10 years,without using nuclear explosive devices.This paper focuses on the development of a new image processing algorithm based on Otsu’s method for the coordinated terminal intercept guidance and control of multiple kinetic-energy impactors employing visual and/or infrared sensors.A scaled polyhedron shape model of asteroid(216)Kleopatra is used as a fictional target asteroid.GPU-based simulation results demonstrate the feasibility of impacting a small irregular-shaped asteroid by using the proposed new image processing algorithm and a classical pulsed TPN(true proportional navigation)terminal guidance law.
