Aim Researching the optimal thieshold of image segmentation. M^ethods An adaptiveimages segmentation method based on the entropy of histogram of gray-level picture and genetic. algorithm (GA) was presental. Results ...Aim Researching the optimal thieshold of image segmentation. M^ethods An adaptiveimages segmentation method based on the entropy of histogram of gray-level picture and genetic. algorithm (GA) was presental. Results In our approach, the segmentation problem was formulated as an optimization problem and the fitness of GA which can efficiently search the segmentation parameter space was regarded as the quality criterion. Conclusion The methodcan be adapted for optimal behold segmentation.展开更多
A new two-step framework is proposed for image segmentation. In the first step, the gray-value distribution of the given image is reshaped to have larger inter-class variance and less intra-class variance. In the sec-...A new two-step framework is proposed for image segmentation. In the first step, the gray-value distribution of the given image is reshaped to have larger inter-class variance and less intra-class variance. In the sec- ond step, the discriminant-based methods or clustering-based methods are performed on the reformed distribution. It is focused on the typical clustering methods-Gaussian mixture model （GMM） and its variant to demonstrate the feasibility of the framework. Due to the independence of the first step in its second step, it can be integrated into the pixel-based and the histogram-based methods to improve their segmentation quality. The experiments on artificial and real images show that the framework can achieve effective and robust segmentation results.展开更多
An approach for color image segmentation is proposed based on the contributions of color features to segmentation rather than the choice of a particular color space. The determination of effective color features depen...An approach for color image segmentation is proposed based on the contributions of color features to segmentation rather than the choice of a particular color space. The determination of effective color features depends on the analysis of various color features from each tested color image via the designed feature encoding. It is different from the pervious methods where self organized feature map (SOFM) is used for constructing the feature encoding so that the feature encoding can self organize the effective features for different color images. Fuzzy clustering is applied for the final segmentation when the well suited color features and the initial parameter are available. The proposed method has been applied in segmenting different types of color images and the experimental results show that it outperforms the classical clustering method. The study shows that the feature encoding approach offers great promise in automating and optimizing the segmentation of color images.展开更多
In this paper we first determine three phases of cell images： background, cytoplasm and nucleolus according to the general physical characteristics of cell images, and then develop a variational model, based on these...In this paper we first determine three phases of cell images： background, cytoplasm and nucleolus according to the general physical characteristics of cell images, and then develop a variational model, based on these characteristics, to segment nucleolus and cytoplasm from their relatively complicated backgrounds. In the meantime, the preprocessing obtained information of cell images using the OTSU algorithm is used to initialize the level set function in the model, which can speed up the segmentation and present satisfactory results in cell image processing.展开更多
A fast and effective fuzzy clustering algorithm is proposed. The algorithm splits an image into n × n blocks, and uses block variance to judge whether the block region is homogeneous. Mean and center pixel of eac...A fast and effective fuzzy clustering algorithm is proposed. The algorithm splits an image into n × n blocks, and uses block variance to judge whether the block region is homogeneous. Mean and center pixel of each homogeneous block are extracted for feature. Each inhomogeneous block is split into separate pixels and the mean of neighboring pixels within a window around each pixel and pixel value are extracted for feature. Then cluster of homogeneous blocks and cluster of separate pixels from inhomogeneous blocks are carried out respectively according to different membership functions. In fuzzy clustering stage, the center pixel and center number of the initial clustering are calculated based on histogram by using mean feature. Then different membership functions according to comparative result of block variance are computed. Finally, modified fuzzy c-means with spatial information to complete image segmentation axe used. Experimental results show that the proposed method can achieve better segmental results and has shorter executive time than many well-known methods.展开更多
The S/N of an underwater image is low and has a fuzzy edge.If using traditional methods to process it directly,the result is not satisfying.Though the traditional fuzzy C-means algorithm can sometimes divide the image...The S/N of an underwater image is low and has a fuzzy edge.If using traditional methods to process it directly,the result is not satisfying.Though the traditional fuzzy C-means algorithm can sometimes divide the image into object and background,its time-consuming computation is often an obstacle.The mission of the vision system of an autonomous underwater vehicle (AUV) is to rapidly and exactly deal with the information about the object in a complex environment for the AUV to use the obtained result to execute the next task.So,by using the statistical characteristics of the gray image histogram,a fast and effective fuzzy C-means underwater image segmentation algorithm was presented.With the weighted histogram modifying the fuzzy membership,the above algorithm can not only cut down on a large amount of data processing and storage during the computation process compared with the traditional algorithm,so as to speed up the efficiency of the segmentation,but also improve the quality of underwater image segmentation.Finally,particle swarm optimization (PSO) described by the sine function was introduced to the algorithm mentioned above.It made up for the shortcomings that the FCM algorithm can not get the global optimal solution.Thus,on the one hand,it considers the global impact and achieves the local optimal solution,and on the other hand,further greatly increases the computing speed.Experimental results indicate that the novel algorithm can reach a better segmentation quality and the processing time of each image is reduced.They enhance efficiency and satisfy the requirements of a highly effective,real-time AUV.展开更多
In this paper, radial basis functions are used to obtain the solution of evolution equations which appear in variational level set method based image segmentation. In this method, radial basis functions are used to in...In this paper, radial basis functions are used to obtain the solution of evolution equations which appear in variational level set method based image segmentation. In this method, radial basis functions are used to interpolate the implicit level set function of the evolution equation with a high level of accuracy and smoothness. Then, the original initial value problem is discretized into an interpolation problem. Accordingly, the evolution equation is converted into a set of coupled ordinary differential equations, and a smooth evolution can be retained. Compared with finite difference scheme based level set approaches, the complex and costly re-initialization procedure is unnecessary. Numerical examples are also given to show the efficiency of the method.展开更多
Image segmentation is one of important steps on pattern recognition study in the course of wood across-compression. By comparing and studying processing methods in finding cell space and cell wall, this paper puts for...Image segmentation is one of important steps on pattern recognition study in the course of wood across-compression. By comparing and studying processing methods in finding cell space and cell wall, this paper puts forward some image segmentation methods that are suitable for study of cell images of wood crossgrained compression. The method of spline function fitting was used for linking edges of cell, which perfects the study of pattern recognition in the course of wood across-compression.展开更多
An efficient multi-threshold approach to segment thermal image is given based on wavelet transform. The gray-level histogram of original image is obtained. In order to reduce the effect of noise, the gray-level histog...An efficient multi-threshold approach to segment thermal image is given based on wavelet transform. The gray-level histogram of original image is obtained. In order to reduce the effect of noise, the gray-level histogram is smoothed by Bezier curve and Bezier histogram is obtained. One dimension stationary wavelet transform is done to the curvature curve of Bezier histogram. Positions of peak values of curvature curve in wavelet domain are adjusted from 'fine-to-coarse' at all scales. The gray level values, which are located in adjusted peak values at all scales, are considered as segmentation thresholds. The gray level values of valley between peaks are considered as quantity gray levels. Optimal segmentation scale is obtained by a cost criterion. The results of experiment show that a target can be segmented effectively from complex background in thermal image by new approach.展开更多
To solve the problem that the magnetic resonance(MR)image has weak boundaries,large amount of information,and low signal-to-noise ratio,we propose an image segmentation method based on the multi-resolution Markov rand...To solve the problem that the magnetic resonance(MR)image has weak boundaries,large amount of information,and low signal-to-noise ratio,we propose an image segmentation method based on the multi-resolution Markov random field(MRMRF)model.The algorithm uses undecimated dual-tree complex wavelet transformation to transform the image into multiple scales.The transformed low-frequency scale histogram is used to improve the initial clustering center of the K-means algorithm,and then other cluster centers are selected according to the maximum distance rule to obtain the coarse-scale segmentation.The results are then segmented by the improved MRMRF model.In order to solve the problem of fuzzy edge segmentation caused by the gray level inhomogeneity of MR image segmentation under the MRMRF model,it is proposed to introduce variable weight parameters in the segmentation process of each scale.Furthermore,the final segmentation results are optimized.We name this algorithm the variable-weight multi-resolution Markov random field(VWMRMRF).The simulation and clinical MR image segmentation verification show that the VWMRMRF algorithm has high segmentation accuracy and robustness,and can accurately and stably achieve low signal-to-noise ratio,weak boundary MR image segmentation.展开更多
In order to solve the problem of image segmentation with intensity inhomogeneity,a new partial differential equation image segmentation model based on fractional-varying-order differen-tial is proposed.This model intr...In order to solve the problem of image segmentation with intensity inhomogeneity,a new partial differential equation image segmentation model based on fractional-varying-order differen-tial is proposed.This model introduces an adaptive coefficient to set disparate differential order in-tervals for pixel with different gray values and use fractional-varying-order differential to process the input image combined with the CV model,then use a variety of image segmentation evaluation indicators,such as true positive(TP)rate,false positive(FP)rate,precision(P),Jaccard similar-ity(JS)rate,and Dice coefficient(DC)rate to measure the pros and cons of our model.The ex-perimental results show that our method is improved on the original basis,which is more condu-cive to us to obtain more image details and obtain better segmentation results.展开更多
Image segmentation is a necessary step in image analysis. Support vector machine （SVM） approach is proposed to segment images and its segmentation performance is evaluated. Experimental results show that： the effec...Image segmentation is a necessary step in image analysis. Support vector machine （SVM） approach is proposed to segment images and its segmentation performance is evaluated. Experimental results show that： the effects of kernel function and model parameters on the segmentation performance are significant; SVM approach is less sensitive to noise in image segmentation; The segmentation performance of SVM approach is better than that of back-propagation multi-layer perceptron （BP-MLP） approach and fuzzy c-means （FCM） approach.展开更多
The image segmentation difficulties of small objects which are much smaller than their background often occur in target detection and recognition. The existing threshold segmentation methods almost fail under the circ...The image segmentation difficulties of small objects which are much smaller than their background often occur in target detection and recognition. The existing threshold segmentation methods almost fail under the circumstances. Thus, a threshold selection method is proposed on the basis of area difference between background and object and intra-class variance. The threshold selection formulae based on one-dimensional (1-D) histogram, two-dimensional (2-D) histogram vertical segmentation and 2-D histogram oblique segmentation are given. A fast recursive algorithm of threshold selection in 2-D histogram oblique segmentation is derived. The segmented images and processing time of the proposed method are given in experiments. It is compared with some fast algorithms, such as Otsu, maximum entropy and Fisher threshold selection methods. The experimental results show that the proposed method can effectively segment the small object images and has better anti-noise property.展开更多
The segmentation effect of Tsallis entropy method is superior to that of Shannon entropy method, and the computation speed of two-dimensional Shannon cross entropy method can be further improved by optimization. The e...The segmentation effect of Tsallis entropy method is superior to that of Shannon entropy method, and the computation speed of two-dimensional Shannon cross entropy method can be further improved by optimization. The existing two-dimensional Tsallis cross entropy method is not the strict two-dimensional extension. Thus two new methods of image thresholding using two-dimensional Tsallis cross entropy based on either Chaotic Particle Swarm Optimization (CPSO) or decomposition are proposed. The former uses CPSO to find the optimal threshold. The recursive algorithm is adopted to avoid the repetitive computation of fitness function in iterative procedure. The computing speed is improved greatly. The latter converts the two-dimensional computation into two one-dimensional spaces, which makes the computational complexity further reduced from O(L2) to O(L). The experimental results show that, compared with the proposed recently two-dimensional Shannon or Tsallis cross entropy method, the two new methods can achieve superior segmentation results and reduce running time greatly.展开更多
An important index to evaluate the process efficiency of coal preparation is the mineral liberation degree of pulverized coal,which is greatly influenced by the particle size and shape distribution acquired by image s...An important index to evaluate the process efficiency of coal preparation is the mineral liberation degree of pulverized coal,which is greatly influenced by the particle size and shape distribution acquired by image segmentation.However,the agglomeration effect of fine powders and the edge effect of granular images caused by scanning electron microscopy greatly affect the precision of particle image segmentation.In this study,we propose a novel image segmentation method derived from mask regional convolutional neural network based on deep learning for recognizing fine coal powders.Firstly,an atrous convolution is introduced into our network to learn the image feature of multi-sized powders,which can reduce the missing segmentation of small-sized agglomerated particles.Then,a new mask loss function combing focal loss and dice coefficient is used to overcome the false segmentation caused by the edge effect.The final comparative experimental results show that our method achieves the best results of 94.43%and 91.44%on AP50 and AP75 respectively among the comparison algorithms.In addition,in order to provide an effective method for particle size analysis of coal particles,we study the particle size distribution of coal powders based on the proposed image segmentation method and obtain a good curve relationship between cumulative mass fraction and particle size.展开更多
A variational inhomogeneous image segmentation model based on fuzzy membership functions and Retinex theory is proposed by introducing the fuzzy membership function.The existence of the solution of the proposed model ...A variational inhomogeneous image segmentation model based on fuzzy membership functions and Retinex theory is proposed by introducing the fuzzy membership function.The existence of the solution of the proposed model is proved theoretically.A valid algorithm is designed to make numerical solution of the model under the framework of alternating minimization.The last experimental results show that the model can make segmentation of the real image with intensity inhomogeneity effectively.展开更多
Image segment is a primary step in image analysis of unexploded ordnance (UXO) detection by ground p enetrating radar (GPR) sensor which is accompanied with a lot of noises and other elements that affect the recogniti...Image segment is a primary step in image analysis of unexploded ordnance (UXO) detection by ground p enetrating radar (GPR) sensor which is accompanied with a lot of noises and other elements that affect the recognition of real target size. In this paper we bring forward a new theory, that is, we look the weight sets as target vector sets which is the new cues in semi-automatic segmentation to form the final image segmentation. The experiment results show that the measure size of target with our method is much smaller than the size with other methods and close to the real size of target.展开更多
In this paper, elitist reconstruction genetic algorithm （ERGA） based on Markov random field （MRF） is introduced for image segmentation. In this algorithm, a population of possible solutions is maintained at every ...In this paper, elitist reconstruction genetic algorithm （ERGA） based on Markov random field （MRF） is introduced for image segmentation. In this algorithm, a population of possible solutions is maintained at every generation, and for each solution a fitness value is calculated according to a fitness function, which is constructed based on the MRF potential function according to Metropolis function and Bayesian framework. After the improved selection, crossover and mutation, an elitist individual is restructured based on the strategy of restructuring elitist. This procedure is processed to select the location that denotes the largest MRF potential function value in the same location of all individuals. The algorithm is stopped when the change of fitness functions between two sequent generations is less than a specified value. Experiments show that the performance of the hybrid algorithm is better than that of some traditional algorithms.展开更多
This paper proposes an improved method to segment tree image based on color and texture feature and amends the segmented result by mathematical morphology. The crown and trunk of one tree have been successfully segmen...This paper proposes an improved method to segment tree image based on color and texture feature and amends the segmented result by mathematical morphology. The crown and trunk of one tree have been successfully segmented and the experimental result is deemed effective. The authors conclude that building a standard data base for a range of species, featuring color and texture is a necessary condition and constitutes the essential groundwork for tree image segmentation in order to insure its quality.展开更多
The quantum theory application is a hot research area in recent years, especially the theory of quantum mechanics. In this paper, we focus on the research of image segmentation based on quantum mechanics. Firstly,the ...The quantum theory application is a hot research area in recent years, especially the theory of quantum mechanics. In this paper, we focus on the research of image segmentation based on quantum mechanics. Firstly,the theory of quantum mechanics is introduced; afterwards, a review of image segmentation methods based on quantum mechanics is presented; and finally, the characteristics about the quantum mechanics applied to image processing are concluded. Two main research topics are discussed in this paper. One is to emphasize that quantum mechanics can be applied in different research areas, such as image segmentation, and the second is to conclude some methods in image segmentation and give some suggestions for possible novel methods by applying quantum mechanics theory. As a summary, this is a review paper which presents some methods based on the feasible theory in quantum mechanics aiming at achieving a better performance in image segmentation.展开更多
文摘Aim Researching the optimal thieshold of image segmentation. M^ethods An adaptiveimages segmentation method based on the entropy of histogram of gray-level picture and genetic. algorithm (GA) was presental. Results In our approach, the segmentation problem was formulated as an optimization problem and the fitness of GA which can efficiently search the segmentation parameter space was regarded as the quality criterion. Conclusion The methodcan be adapted for optimal behold segmentation.
基金Supported by the National Natural Science Foundation of China(60505004,60773061)~~
文摘A new two-step framework is proposed for image segmentation. In the first step, the gray-value distribution of the given image is reshaped to have larger inter-class variance and less intra-class variance. In the sec- ond step, the discriminant-based methods or clustering-based methods are performed on the reformed distribution. It is focused on the typical clustering methods-Gaussian mixture model （GMM） and its variant to demonstrate the feasibility of the framework. Due to the independence of the first step in its second step, it can be integrated into the pixel-based and the histogram-based methods to improve their segmentation quality. The experiments on artificial and real images show that the framework can achieve effective and robust segmentation results.
文摘An approach for color image segmentation is proposed based on the contributions of color features to segmentation rather than the choice of a particular color space. The determination of effective color features depends on the analysis of various color features from each tested color image via the designed feature encoding. It is different from the pervious methods where self organized feature map (SOFM) is used for constructing the feature encoding so that the feature encoding can self organize the effective features for different color images. Fuzzy clustering is applied for the final segmentation when the well suited color features and the initial parameter are available. The proposed method has been applied in segmenting different types of color images and the experimental results show that it outperforms the classical clustering method. The study shows that the feature encoding approach offers great promise in automating and optimizing the segmentation of color images.
基金supported by the National Basic Research Program of China (Grant No. 2011CB707701)the National Natural Science Foundation of China (Grant No. 60873124)+2 种基金the Joint Research Foundation of Beijing Education Committee (GrantNo. JD100010607)the International Science and Technology Supporting Programme (Grant No. 2008BAH26B00)the Zhejiang Service Robot Key Laboratory (Grant No. 2008E10004)
文摘In this paper we first determine three phases of cell images： background, cytoplasm and nucleolus according to the general physical characteristics of cell images, and then develop a variational model, based on these characteristics, to segment nucleolus and cytoplasm from their relatively complicated backgrounds. In the meantime, the preprocessing obtained information of cell images using the OTSU algorithm is used to initialize the level set function in the model, which can speed up the segmentation and present satisfactory results in cell image processing.
文摘A fast and effective fuzzy clustering algorithm is proposed. The algorithm splits an image into n × n blocks, and uses block variance to judge whether the block region is homogeneous. Mean and center pixel of each homogeneous block are extracted for feature. Each inhomogeneous block is split into separate pixels and the mean of neighboring pixels within a window around each pixel and pixel value are extracted for feature. Then cluster of homogeneous blocks and cluster of separate pixels from inhomogeneous blocks are carried out respectively according to different membership functions. In fuzzy clustering stage, the center pixel and center number of the initial clustering are calculated based on histogram by using mean feature. Then different membership functions according to comparative result of block variance are computed. Finally, modified fuzzy c-means with spatial information to complete image segmentation axe used. Experimental results show that the proposed method can achieve better segmental results and has shorter executive time than many well-known methods.
基金Supported by the National Natural Science Foundation of China under Grant No.50909025/E091002the Open Research Foundation of SKLab AUV, HEU under Grant No.2008003
文摘The S/N of an underwater image is low and has a fuzzy edge.If using traditional methods to process it directly,the result is not satisfying.Though the traditional fuzzy C-means algorithm can sometimes divide the image into object and background,its time-consuming computation is often an obstacle.The mission of the vision system of an autonomous underwater vehicle (AUV) is to rapidly and exactly deal with the information about the object in a complex environment for the AUV to use the obtained result to execute the next task.So,by using the statistical characteristics of the gray image histogram,a fast and effective fuzzy C-means underwater image segmentation algorithm was presented.With the weighted histogram modifying the fuzzy membership,the above algorithm can not only cut down on a large amount of data processing and storage during the computation process compared with the traditional algorithm,so as to speed up the efficiency of the segmentation,but also improve the quality of underwater image segmentation.Finally,particle swarm optimization (PSO) described by the sine function was introduced to the algorithm mentioned above.It made up for the shortcomings that the FCM algorithm can not get the global optimal solution.Thus,on the one hand,it considers the global impact and achieves the local optimal solution,and on the other hand,further greatly increases the computing speed.Experimental results indicate that the novel algorithm can reach a better segmentation quality and the processing time of each image is reduced.They enhance efficiency and satisfy the requirements of a highly effective,real-time AUV.
基金Project supported by the National Natural Science Foundation of China (Grant No.11101454)the Educational Commission Foundation of Chongqing City,China (Grant No.KJ130626)the Program of Innovation Team Project in University of Chongqing City,China (Grant No.KJTD201308)
文摘In this paper, radial basis functions are used to obtain the solution of evolution equations which appear in variational level set method based image segmentation. In this method, radial basis functions are used to interpolate the implicit level set function of the evolution equation with a high level of accuracy and smoothness. Then, the original initial value problem is discretized into an interpolation problem. Accordingly, the evolution equation is converted into a set of coupled ordinary differential equations, and a smooth evolution can be retained. Compared with finite difference scheme based level set approaches, the complex and costly re-initialization procedure is unnecessary. Numerical examples are also given to show the efficiency of the method.
文摘Image segmentation is one of important steps on pattern recognition study in the course of wood across-compression. By comparing and studying processing methods in finding cell space and cell wall, this paper puts forward some image segmentation methods that are suitable for study of cell images of wood crossgrained compression. The method of spline function fitting was used for linking edges of cell, which perfects the study of pattern recognition in the course of wood across-compression.
文摘An efficient multi-threshold approach to segment thermal image is given based on wavelet transform. The gray-level histogram of original image is obtained. In order to reduce the effect of noise, the gray-level histogram is smoothed by Bezier curve and Bezier histogram is obtained. One dimension stationary wavelet transform is done to the curvature curve of Bezier histogram. Positions of peak values of curvature curve in wavelet domain are adjusted from 'fine-to-coarse' at all scales. The gray level values, which are located in adjusted peak values at all scales, are considered as segmentation thresholds. The gray level values of valley between peaks are considered as quantity gray levels. Optimal segmentation scale is obtained by a cost criterion. The results of experiment show that a target can be segmented effectively from complex background in thermal image by new approach.
基金the National Natural Science Foundation of China(Grant No.11471004)the Key Research and Development Program of Shaanxi Province,China(Grant No.2018SF-251)。
文摘To solve the problem that the magnetic resonance(MR)image has weak boundaries,large amount of information,and low signal-to-noise ratio,we propose an image segmentation method based on the multi-resolution Markov random field(MRMRF)model.The algorithm uses undecimated dual-tree complex wavelet transformation to transform the image into multiple scales.The transformed low-frequency scale histogram is used to improve the initial clustering center of the K-means algorithm,and then other cluster centers are selected according to the maximum distance rule to obtain the coarse-scale segmentation.The results are then segmented by the improved MRMRF model.In order to solve the problem of fuzzy edge segmentation caused by the gray level inhomogeneity of MR image segmentation under the MRMRF model,it is proposed to introduce variable weight parameters in the segmentation process of each scale.Furthermore,the final segmentation results are optimized.We name this algorithm the variable-weight multi-resolution Markov random field(VWMRMRF).The simulation and clinical MR image segmentation verification show that the VWMRMRF algorithm has high segmentation accuracy and robustness,and can accurately and stably achieve low signal-to-noise ratio,weak boundary MR image segmentation.
基金supported by the Key Research Project of the Henan Provincial Higher Education(No.20A510011).
文摘In order to solve the problem of image segmentation with intensity inhomogeneity,a new partial differential equation image segmentation model based on fractional-varying-order differen-tial is proposed.This model introduces an adaptive coefficient to set disparate differential order in-tervals for pixel with different gray values and use fractional-varying-order differential to process the input image combined with the CV model,then use a variety of image segmentation evaluation indicators,such as true positive(TP)rate,false positive(FP)rate,precision(P),Jaccard similar-ity(JS)rate,and Dice coefficient(DC)rate to measure the pros and cons of our model.The ex-perimental results show that our method is improved on the original basis,which is more condu-cive to us to obtain more image details and obtain better segmentation results.
基金Supported by the National Natural Science Foundation of China (No. 60475024)
文摘Image segmentation is a necessary step in image analysis. Support vector machine （SVM） approach is proposed to segment images and its segmentation performance is evaluated. Experimental results show that： the effects of kernel function and model parameters on the segmentation performance are significant; SVM approach is less sensitive to noise in image segmentation; The segmentation performance of SVM approach is better than that of back-propagation multi-layer perceptron （BP-MLP） approach and fuzzy c-means （FCM） approach.
基金Sponsored by The National Natural Science Foundation of China(60872065)Science and Technology on Electro-optic Control Laboratory and Aviation Science Foundation(20105152026)State Key Laboratory Open Fund of Novel Software Technology,Nanjing University(KFKT2010B17)
文摘The image segmentation difficulties of small objects which are much smaller than their background often occur in target detection and recognition. The existing threshold segmentation methods almost fail under the circumstances. Thus, a threshold selection method is proposed on the basis of area difference between background and object and intra-class variance. The threshold selection formulae based on one-dimensional (1-D) histogram, two-dimensional (2-D) histogram vertical segmentation and 2-D histogram oblique segmentation are given. A fast recursive algorithm of threshold selection in 2-D histogram oblique segmentation is derived. The segmented images and processing time of the proposed method are given in experiments. It is compared with some fast algorithms, such as Otsu, maximum entropy and Fisher threshold selection methods. The experimental results show that the proposed method can effectively segment the small object images and has better anti-noise property.
基金supported by National Natural Science Foundation of China under Grant No.60872065Open Foundation of State Key Laboratory for Novel Software Technology at Nanjing University under Grant No.KFKT2010B17
文摘The segmentation effect of Tsallis entropy method is superior to that of Shannon entropy method, and the computation speed of two-dimensional Shannon cross entropy method can be further improved by optimization. The existing two-dimensional Tsallis cross entropy method is not the strict two-dimensional extension. Thus two new methods of image thresholding using two-dimensional Tsallis cross entropy based on either Chaotic Particle Swarm Optimization (CPSO) or decomposition are proposed. The former uses CPSO to find the optimal threshold. The recursive algorithm is adopted to avoid the repetitive computation of fitness function in iterative procedure. The computing speed is improved greatly. The latter converts the two-dimensional computation into two one-dimensional spaces, which makes the computational complexity further reduced from O(L2) to O(L). The experimental results show that, compared with the proposed recently two-dimensional Shannon or Tsallis cross entropy method, the two new methods can achieve superior segmentation results and reduce running time greatly.
基金Supported by the Research and Development Project of Experimental Technology,China University of Mining and Technology(Study on mineral occurrence in coal based on SEM and EDS,S2023Y018)the National Natural Science Foundations of China under Grant 62371451.
文摘An important index to evaluate the process efficiency of coal preparation is the mineral liberation degree of pulverized coal,which is greatly influenced by the particle size and shape distribution acquired by image segmentation.However,the agglomeration effect of fine powders and the edge effect of granular images caused by scanning electron microscopy greatly affect the precision of particle image segmentation.In this study,we propose a novel image segmentation method derived from mask regional convolutional neural network based on deep learning for recognizing fine coal powders.Firstly,an atrous convolution is introduced into our network to learn the image feature of multi-sized powders,which can reduce the missing segmentation of small-sized agglomerated particles.Then,a new mask loss function combing focal loss and dice coefficient is used to overcome the false segmentation caused by the edge effect.The final comparative experimental results show that our method achieves the best results of 94.43%and 91.44%on AP50 and AP75 respectively among the comparison algorithms.In addition,in order to provide an effective method for particle size analysis of coal particles,we study the particle size distribution of coal powders based on the proposed image segmentation method and obtain a good curve relationship between cumulative mass fraction and particle size.
文摘A variational inhomogeneous image segmentation model based on fuzzy membership functions and Retinex theory is proposed by introducing the fuzzy membership function.The existence of the solution of the proposed model is proved theoretically.A valid algorithm is designed to make numerical solution of the model under the framework of alternating minimization.The last experimental results show that the model can make segmentation of the real image with intensity inhomogeneity effectively.
基金Supported by the Natural Science Foundation of Heilongjiang Province (F0201)
文摘Image segment is a primary step in image analysis of unexploded ordnance (UXO) detection by ground p enetrating radar (GPR) sensor which is accompanied with a lot of noises and other elements that affect the recognition of real target size. In this paper we bring forward a new theory, that is, we look the weight sets as target vector sets which is the new cues in semi-automatic segmentation to form the final image segmentation. The experiment results show that the measure size of target with our method is much smaller than the size with other methods and close to the real size of target.
文摘In this paper, elitist reconstruction genetic algorithm （ERGA） based on Markov random field （MRF） is introduced for image segmentation. In this algorithm, a population of possible solutions is maintained at every generation, and for each solution a fitness value is calculated according to a fitness function, which is constructed based on the MRF potential function according to Metropolis function and Bayesian framework. After the improved selection, crossover and mutation, an elitist individual is restructured based on the strategy of restructuring elitist. This procedure is processed to select the location that denotes the largest MRF potential function value in the same location of all individuals. The algorithm is stopped when the change of fitness functions between two sequent generations is less than a specified value. Experiments show that the performance of the hybrid algorithm is better than that of some traditional algorithms.
基金Supported by the National Natural Science Foundation of China (Grant No. 30271079) and Graduate Cultivation Foundation of Beijing Forestry University
文摘This paper proposes an improved method to segment tree image based on color and texture feature and amends the segmented result by mathematical morphology. The crown and trunk of one tree have been successfully segmented and the experimental result is deemed effective. The authors conclude that building a standard data base for a range of species, featuring color and texture is a necessary condition and constitutes the essential groundwork for tree image segmentation in order to insure its quality.
基金supported by the National Natural Science Foundation of China under Grant No.51679058the China Higher Specialized Research Fund(Ph.D.supervisor category) under Grant No.20132304110018
文摘The quantum theory application is a hot research area in recent years, especially the theory of quantum mechanics. In this paper, we focus on the research of image segmentation based on quantum mechanics. Firstly,the theory of quantum mechanics is introduced; afterwards, a review of image segmentation methods based on quantum mechanics is presented; and finally, the characteristics about the quantum mechanics applied to image processing are concluded. Two main research topics are discussed in this paper. One is to emphasize that quantum mechanics can be applied in different research areas, such as image segmentation, and the second is to conclude some methods in image segmentation and give some suggestions for possible novel methods by applying quantum mechanics theory. As a summary, this is a review paper which presents some methods based on the feasible theory in quantum mechanics aiming at achieving a better performance in image segmentation.
