With the development of underwater sonar detection technology,simultaneous localization and mapping(SLAM)approach has attracted much attention in underwater navigation field in recent years.But the weak detection abil...With the development of underwater sonar detection technology,simultaneous localization and mapping(SLAM)approach has attracted much attention in underwater navigation field in recent years.But the weak detection ability of a single vehicle limits the SLAM performance in wide areas.Thereby,cooperative SLAM using multiple vehicles has become an important research direction.The key factor of cooperative SLAM is timely and efficient sonar image transmission among underwater vehicles.However,the limited bandwidth of underwater acoustic channels contradicts a large amount of sonar image data.It is essential to compress the images before transmission.Recently,deep neural networks have great value in image compression by virtue of the powerful learning ability of neural networks,but the existing sonar image compression methods based on neural network usually focus on the pixel-level information without the semantic-level information.In this paper,we propose a novel underwater acoustic transmission scheme called UAT-SSIC that includes semantic segmentation-based sonar image compression(SSIC)framework and the joint source-channel codec,to improve the accuracy of the semantic information of the reconstructed sonar image at the receiver.The SSIC framework consists of Auto-Encoder structure-based sonar image compression network,which is measured by a semantic segmentation network's residual.Considering that sonar images have the characteristics of blurred target edges,the semantic segmentation network used a special dilated convolution neural network(DiCNN)to enhance segmentation accuracy by expanding the range of receptive fields.The joint source-channel codec with unequal error protection is proposed that adjusts the power level of the transmitted data,which deal with sonar image transmission error caused by the serious underwater acoustic channel.Experiment results demonstrate that our method preserves more semantic information,with advantages over existing methods at the same compression ratio.It also improves the error tolerance and packet loss resistance of transmission.展开更多
Fuzzy c-means （FCM） algorithm is one of the most popular methods for image segmentation. However, the standard FCM algorithm is sensitive to noise because of not taking into account the spatial information in the im...Fuzzy c-means （FCM） algorithm is one of the most popular methods for image segmentation. However, the standard FCM algorithm is sensitive to noise because of not taking into account the spatial information in the image. An improved FCM algorithm is proposed to improve the antinoise performance of FCM algorithm. The new algorithm is formulated by incorporating the spatial neighborhood information into the membership function for clustering. The distribution statistics of the neighborhood pixels and the prior probability are used to form a new membership func- tion. It is not only effective to remove the noise spots but also can reduce the misclassified pixels. Experimental results indicate that the proposed algorithm is more accurate and robust to noise than the standard FCM algorithm.展开更多
Traditional image segmentation methods based on MRF converge slowly and require pre-defined weight. These disadvantages are addressed, and a fast segmentation approach based on simple Markov random field （MRF） for S...Traditional image segmentation methods based on MRF converge slowly and require pre-defined weight. These disadvantages are addressed, and a fast segmentation approach based on simple Markov random field （MRF） for SAR image is proposed. The approach is firstly used to perform coarse segmentation in blocks. Then the image is modeled with simple MRF and adaptive variable weighting forms are applied in homogeneous and heterogeneous regions. As a result, the convergent speed is accelerated while the segmentation results in homogeneous regions and boarders are improved. Simulations with synthetic and real SAR images demonstrate the effectiveness of the proposed approach.展开更多
In the methods of image thresholding segmentation, such methods based on two-dimensional （2D） histogram and optimal objective functions are important. However, when they are used for infrared image segmentation, the...In the methods of image thresholding segmentation, such methods based on two-dimensional （2D） histogram and optimal objective functions are important. However, when they are used for infrared image segmentation, they are weak in suppressing background noises and worse in segmenting targets with non-uniform gray level. The concept of 2D histogram shape modification is proposed, which is realized by target information prior restraint after enhancing target information using plateau histogram equalization. The formula of 2D minimum Renyi entropy is deduced for image segmentation, then the shape-modified 2D histogram is combined wfth four optimal objective functions （i.e., maximum between-class variance, maximum entropy, maximum correlation and minimum Renyi entropy） respectively for the appli- cation of infrared image segmentation. Simultaneously, F-measure is introduced to evaluate the segmentation effects objectively. The experimental results show that F-measure is an effective evaluation index for image segmentation since its value is fully consistent with the subjective evaluation, and after 2D histogram shape modification, the methods of optimal objective functions can overcome their original forms＇ deficiency and their segmentation effects are more or less improvements, where the best one is the maximum entropy method based on 2D histogram shape modification.展开更多
Mixture model based image segmentation method, which assumes that image pixels are independent and do not consider the position relationship between pixels, is not robust to noise and usually leads to misclassificatio...Mixture model based image segmentation method, which assumes that image pixels are independent and do not consider the position relationship between pixels, is not robust to noise and usually leads to misclassification. A new segmentation method, called multi-resolution Ganssian mixture model method, is proposed. First, an image pyramid is constructed and son-father link relationship is built between each level of pyramid. Then the mixture model segmentation method is applied to the top level. The segmentation result on the top level is passed top-down to the bottom level according to the son-father link relationship between levels. The proposed method considers not only local but also global information of image, it overcomes the effect of noise and can obtain better segmentation result. Experimental result demonstrates its effectiveness.展开更多
A modified artificial bee colony optimizer(MABC)is proposed for image segmentation by using a pool of optimal foraging strategies to balance the exploration and exploitation tradeoff.The main idea of MABC is to enrich...A modified artificial bee colony optimizer(MABC)is proposed for image segmentation by using a pool of optimal foraging strategies to balance the exploration and exploitation tradeoff.The main idea of MABC is to enrichartificial bee foraging behaviors by combining local search and comprehensive learning using multi-dimensional PSO-based equation.With comprehensive learning,the bees incorporate the information of global best solution into the solution search equation to improve the exploration while the local search enables the bees deeply exploit around the promising area,which provides a proper balance between exploration and exploitation.The experimental results on comparing the MABC to several successful EA and SI algorithms on a set of benchmarks demonstrated the effectiveness of the proposed algorithm.Furthermore,we applied the MABC algorithm to image segmentation problem.Experimental results verify the effectiveness of the proposed algorithm.展开更多
In order to extract froth morphological feature,a bubble image adaptive segmentation method was proposed.Considering the image's low contrast and weak froth edges,froth image was coarsely segmented by using fuzzy ...In order to extract froth morphological feature,a bubble image adaptive segmentation method was proposed.Considering the image's low contrast and weak froth edges,froth image was coarsely segmented by using fuzzy c means(FCM) algorithm. Through the attributes of size and shape pattern spectrum,the optimal morphological structuring element was determined.According to the optimal parameters,some image noises were removed with an improved area opening and closing by reconstruction operation,which consist of image regional markers,and the bubbles were finely separated from each other by watershed transform.The experimental results show that the structural element can be determined adaptively by shape and size pattern spectrum,and the froth image is segmented accurately.Compared with other froth image segmentation method,the proposed method achieves much high accuracy,based on which,the bubble size and shape features are extracted effectively.展开更多
Semantic segmentation is a crucial step for document understanding.In this paper,an NVIDIA Jetson Nano-based platform is applied for implementing semantic segmentation for teaching artificial intelligence concepts and...Semantic segmentation is a crucial step for document understanding.In this paper,an NVIDIA Jetson Nano-based platform is applied for implementing semantic segmentation for teaching artificial intelligence concepts and programming.To extract semantic structures from document images,we present an end-to-end dilated convolution network architecture.Dilated convolutions have well-known advantages for extracting multi-scale context information without losing spatial resolution.Our model utilizes dilated convolutions with residual network to represent the image features and predicting pixel labels.The convolution part works as feature extractor to obtain multidimensional and hierarchical image features.The consecutive deconvolution is used for producing full resolution segmentation prediction.The probability of each pixel decides its predefined semantic class label.To understand segmentation granularity,we compare performances at three different levels.From fine grained class to coarse class levels,the proposed dilated convolution network architecture is evaluated on three document datasets.The experimental results have shown that both semantic data distribution imbalance and network depth are import factors that influence the document’s semantic segmentation performances.The research is aimed at offering an education resource for teaching artificial intelligence concepts and techniques.展开更多
The similarity measure is crucial to the performance of spectral clustering. The Gaussian kernel function based on the Euclidean distance is usual y adopted as the similarity measure. However, the Euclidean distance m...The similarity measure is crucial to the performance of spectral clustering. The Gaussian kernel function based on the Euclidean distance is usual y adopted as the similarity measure. However, the Euclidean distance measure cannot ful y reveal the complex distribution data, and the result of spectral clustering is very sensitive to the scaling parameter. To solve these problems, a new manifold distance measure and a novel simulated anneal-ing spectral clustering （SASC） algorithm based on the manifold distance measure are proposed. The simulated annealing based on genetic algorithm （SAGA）, characterized by its rapid convergence to the global optimum, is used to cluster the sample points in the spectral mapping space. The proposed algorithm can not only reflect local and global consistency better, but also reduce the sensitivity of spectral clustering to the kernel parameter, which improves the algorithm’s clustering performance. To efficiently apply the algorithm to image segmentation, the Nystrom method is used to reduce the computation complexity. Experimental results show that compared with traditional clustering algorithms and those popular spectral clustering algorithms, the proposed algorithm can achieve better clustering performances on several synthetic datasets, texture images and real images.展开更多
To improve the segmentation quality and efficiency of color image,a novel approach which combines the advantages of the mean shift(MS) segmentation and improved ant clustering method is proposed.The regions which can ...To improve the segmentation quality and efficiency of color image,a novel approach which combines the advantages of the mean shift(MS) segmentation and improved ant clustering method is proposed.The regions which can preserve the discontinuity characteristics of an image are segmented by MS algorithm,and then they are represented by a graph in which every region is represented by a node.In order to solve the graph partition problem,an improved ant clustering algorithm,called similarity carrying ant model(SCAM-ant),is proposed,in which a new similarity calculation method is given.Using SCAM-ant,the maximum number of items that each ant can carry will increase,the clustering time will be effectively reduced,and globally optimized clustering can also be realized.Because the graph is not based on the pixels of original image but on the segmentation result of MS algorithm,the computational complexity is greatly reduced.Experiments show that the proposed method can realize color image segmentation efficiently,and compared with the conventional methods based on the image pixels,it improves the image segmentation quality and the anti-interference ability.展开更多
A fast interactive segmentation algorithm of image-sequences based on relative fuzzy connectedness is presented. In comparison with the original algorithm, the proposed one, with the same accuracy, accelerates the seg...A fast interactive segmentation algorithm of image-sequences based on relative fuzzy connectedness is presented. In comparison with the original algorithm, the proposed one, with the same accuracy, accelerates the segmentation speed by three times for single image. Meanwhile, this fast segmentation algorithm is extended from single object to multiple objects and from single-image to image-sequences. Thus the segmentation of multiple objects from complex hackground and batch segmentation of image-sequences can be achieved. In addition, a post-processing scheme is incorporated in this algorithm, which extracts smooth edge with one-pixel-width for each segmented object. The experimental results illustrate that the proposed algorithm can obtain the object regions of interest from medical image or image-sequences as well as man-made images quickly and reliably with only a little interaction.展开更多
To solve the problem of poor anti-noise performance of the traditional fuzzy C-means (FCM) algorithm in image segmentation, a novel two-dimensional FCM clustering algorithm for image segmentation was proposed. In this...To solve the problem of poor anti-noise performance of the traditional fuzzy C-means (FCM) algorithm in image segmentation, a novel two-dimensional FCM clustering algorithm for image segmentation was proposed. In this method, the image segmentation was converted into an optimization problem. The fitness function containing neighbor information was set up based on the gray information and the neighbor relations between the pixels described by the improved two-dimensional histogram. By making use of the global searching ability of the predator-prey particle swarm optimization, the optimal cluster center could be obtained by iterative optimization, and the image segmentation could be accomplished. The simulation results show that the segmentation accuracy ratio of the proposed method is above 99%. The proposed algorithm has strong anti-noise capability, high clustering accuracy and good segment effect, indicating that it is an effective algorithm for image segmentation.展开更多
Segmentation is the key step in auto-interpretation of high-resolution spaceborne synthetic aperture radar（SAR） images. A novel method is proposed based on integrating the geometric active contour（GAC） and the sup...Segmentation is the key step in auto-interpretation of high-resolution spaceborne synthetic aperture radar（SAR） images. A novel method is proposed based on integrating the geometric active contour（GAC） and the support vector machine（SVM）models. First, the images are segmented by using SVM and textural statistics. A likelihood measurement for every pixel is derived by using the initial segmentation. The Chan-Vese model then is modified by adding two items： the likelihood and the distance between the initial segmentation and the evolving contour. Experimental results using real SAR images demonstrate the good performance of the proposed method compared to several classic GAC models.展开更多
An image segmentation algorithm of the restrained fuzzy Kohonen clustering network （RFKCN） based on high- dimension fuzzy character is proposed. The algorithm includes two steps. The first step is the fuzzification ...An image segmentation algorithm of the restrained fuzzy Kohonen clustering network （RFKCN） based on high- dimension fuzzy character is proposed. The algorithm includes two steps. The first step is the fuzzification of pixels in which two redundant images are built by fuzzy mean value and fuzzy median value. The second step is to construct a three-dimensional （3-D） feature vector of redundant images and their original images and cluster the feature vector through RFKCN, to realize image seg- mentation. The proposed algorithm fully takes into account not only gray distribution information of pixels, but also relevant information and fuzzy information among neighboring pixels in constructing 3- D character space. Based on the combination of competitiveness, redundancy and complementary of the information, the proposed algorithm improves the accuracy of clustering. Theoretical anal- yses and experimental results demonstrate that the proposed algorithm has a good segmentation performance.展开更多
The active contour model based on local image fitting （LIF） energy is an effective method to deal with intensity inhomo- geneities, but it always conflicts with the local minimum problem because LIF has a nonconvex ...The active contour model based on local image fitting （LIF） energy is an effective method to deal with intensity inhomo- geneities, but it always conflicts with the local minimum problem because LIF has a nonconvex energy function form. At the same time, the parameters of LIF are hard to be chosen for better per- formance. A global minimization of the adaptive LIF energy model is proposed. The regularized length term which constrains the zero level set is introduced to improve the accuracy of the bound- aries, and a global minimization of the active contour model is presented, in addition, based on the statistical information of the intensity histogram, the standard deviation σ with respect to the truncated Gaussian window is automatically computed according to images. Consequently, the proposed method improves the performance and adaptivity to deal with the intensity inhomo- geneities. Experimental results for synthetic and real images show desirable performance and efficiency of the proposed method.展开更多
Aiming at measurement of granularity size of nonmetal grain, an algorithm of image segmentation and parameter calculation for microscopic overlapping grain image was studied. This algorithm presents some new attribute...Aiming at measurement of granularity size of nonmetal grain, an algorithm of image segmentation and parameter calculation for microscopic overlapping grain image was studied. This algorithm presents some new attributes of graph sequence from discrete attribute of graph,and consequently achieves the geometrical characteristics from input graph, and the new graph sequence in favor of image segmentation is recombined. The conception that image edge denoted with “twin-point” is put forward, base on geometrical characters of point, image edge is transformed into serial edge, and on recombined serial image edge, based on direction vector definition of line and some additional restricted conditions, the segmentation twin-points are searched with, thus image segmentation is accomplished. Serial image edge is transformed into twin-point pattern, to realize calculation of area and granularity size of nonmetal grain. The inkling and uncertainty on selection of structure element which base on mathematical morphology are avoided in this algorithm, and image segmentation and parameter calculation are realized without changing grain’s self statistical characters.展开更多
The dynamic transmission characteristics and the sensitivities of the three stage idler gear system of the new NC power turret are studied in the paper. Considering the strongly nonlinear factors such as the periodica...The dynamic transmission characteristics and the sensitivities of the three stage idler gear system of the new NC power turret are studied in the paper. Considering the strongly nonlinear factors such as the periodically time-varying mesh stiffness, the nonlinear tooth backlash, the lump-parameter model of the gear system is developed with one rotational and two translational freedoms of each gear. The eigen-values and eigenvectors are derived and analyzed on the basis of the real modal theory. The sensitivities of natural frequencies to design parameters including supporting and meshing stiffnesses, gear masses, and moments of inertia by the direct differential method are also calculated. The results show the quantitative and qualitative impact of the parameters to the natural characteristics of the gear system. Furthermore, the periodic steady state solutions are obtained by the numerical approach based on the nonlinear model. These results are employed to gain insights into the primary controlling parameters, to forecast the severity of the dynamic response, and to assess the acceptability of the gear design.展开更多
Background Cardiovascular diseases are closely associated with atherosclerotic plaque development and rupture.Traditional medical imaging techniques such as magnetic resonance imaging(MRI)and intravascular ultrasound(...Background Cardiovascular diseases are closely associated with atherosclerotic plaque development and rupture.Traditional medical imaging techniques such as magnetic resonance imaging(MRI)and intravascular ultrasound(IVUS)were unable to identify vulnerable plaques due to their limited resolution.Fortunately,optical coherence tomography(OCT)is an advanced intravascular imaging technique developed in recent years which has high resolution approximately 10 microns and could provide more accurate morphology of coronary plaque.In particular,it has the ability to identify plaques with fibrous cap thickness<65μm,an accepted threshold value for vulnerable plaques.However,segmentation of OCT images in clinic is still mainly performed manually by physicians which is time consuming and subjective.To overcome time consumption,several methodologies have been proposed for automatic segmentation of OCT images but most of these methods were still limited by intricate image preprocessing and expensive computation.In this research,two automatic segmentation methods for intracoronary OCT image based on support vector machine(SVM)and convolutional neural network(CNN)were performed to identify the plaque region and characterize plaque components.Methods In vivo IVUS and OCT coronary plaque data from 5 patients were acquired at Emory University with patient’s consent obtained.OCT were obtained from ILUMIEN OPTIS System(St.Jude,Minnesota,MN).The OCT catheter was traversed to the segment of interest and the catheter pullback was limited at a rate of 20 mm/sec.Following the OCT image acquisition,the IVUS catheter was traversed distally though the artery to the same coronary segment(Volcano Therapeutics,Rancho Cordova)and the catheter pullback speed was at a standard rate of 0.5 mm/sec.Seventy-seven matched IVUS and OCT slices with good image quality and lipid cores were selected for our segmentation study.Manual OCT segmentation was performed by experts and used as gold standard in the automatic segmentations.VH-IVUS was used as references and guide by the experts in the manual segmentation process.Three plaque component tissue classes were identified from OCT images in this work:lipid tissue(LT),fibrous tissue(FT)and background(BG).Procedures using two machine learning methods(CNN and SVM)were developed to segment OCT images,respectively.For CNN method,the U-Net architecture was selected due to its good performance in very different biomedical segmentation and very few annotated images.For SVM method,local binary patterns(LBPs),gray level co-occurrence matrices(GLCMs)which contains contrast,correlation,energy and homogeneity,entropy and mean value were calculated as features and assembled to feed SVM classifier.The accuracies of two segmentation methods were evaluated and compared using the OCT dataset.Segmentation accuracy is defined as the ratio of the number of pixels correctly classified over the total number of pixels.Results The overall classification accuracy based CNN method reached 95.8%,and the accuracies for LT,FT and BG were 86.8%,83.4%,and 98.2%,respectively.The overall classification accuracy based SVM was 71.9%,and per-class accuracy for LT,FT and BG was 75.4%,78.3%,and67.0%,respectively.Conclusions The two methods proposed can automatically identify plaque region and characterize plaque compositions for OCT images and potentially reduce the time spent by doctors in segmenting and evaluating coronary plaque OCT images.CNN provided better segmentation accuracies compared to those achieved by SVM.展开更多
The document image segmentation is very useful for printing, faxing and data processing. An algorithm is developed for segmenting and classifying document image. Feature used for classification is based on the histogr...The document image segmentation is very useful for printing, faxing and data processing. An algorithm is developed for segmenting and classifying document image. Feature used for classification is based on the histogram distribution pattern of different image classes. The important attribute of the algorithm is using wavelet correlation image to enhance raw image's pattern, so the classification accuracy is improved. In this paper document image is divided into four types; background, photo, text and graph. Firstly, the document image background has been distingusished easily by former normally method;secondly, three image types will be distinguished by their typical histograms, in order to make histograms feature clearer, each resolution's HH wavelet subimage is used to add to the raw image at their resolution. At last, the photo, text and praph have been devided according to how the feature fit to the Laplacian distrbution by 2 and L . Simulations show that classification accuracy is significantly improved. The comparison with related shows that our algorithm provides both lower classification error rates and better visual results.展开更多
基金supported in part by the Tianjin Technology Innovation Guidance Special Fund Project under Grant No.21YDTPJC00850in part by the National Natural Science Foundation of China under Grant No.41906161in part by the Natural Science Foundation of Tianjin under Grant No.21JCQNJC00650。
文摘With the development of underwater sonar detection technology,simultaneous localization and mapping(SLAM)approach has attracted much attention in underwater navigation field in recent years.But the weak detection ability of a single vehicle limits the SLAM performance in wide areas.Thereby,cooperative SLAM using multiple vehicles has become an important research direction.The key factor of cooperative SLAM is timely and efficient sonar image transmission among underwater vehicles.However,the limited bandwidth of underwater acoustic channels contradicts a large amount of sonar image data.It is essential to compress the images before transmission.Recently,deep neural networks have great value in image compression by virtue of the powerful learning ability of neural networks,but the existing sonar image compression methods based on neural network usually focus on the pixel-level information without the semantic-level information.In this paper,we propose a novel underwater acoustic transmission scheme called UAT-SSIC that includes semantic segmentation-based sonar image compression(SSIC)framework and the joint source-channel codec,to improve the accuracy of the semantic information of the reconstructed sonar image at the receiver.The SSIC framework consists of Auto-Encoder structure-based sonar image compression network,which is measured by a semantic segmentation network's residual.Considering that sonar images have the characteristics of blurred target edges,the semantic segmentation network used a special dilated convolution neural network(DiCNN)to enhance segmentation accuracy by expanding the range of receptive fields.The joint source-channel codec with unequal error protection is proposed that adjusts the power level of the transmitted data,which deal with sonar image transmission error caused by the serious underwater acoustic channel.Experiment results demonstrate that our method preserves more semantic information,with advantages over existing methods at the same compression ratio.It also improves the error tolerance and packet loss resistance of transmission.
基金supported by the National Natural Science Foundation of China(6087403160740430664)
文摘Fuzzy c-means （FCM） algorithm is one of the most popular methods for image segmentation. However, the standard FCM algorithm is sensitive to noise because of not taking into account the spatial information in the image. An improved FCM algorithm is proposed to improve the antinoise performance of FCM algorithm. The new algorithm is formulated by incorporating the spatial neighborhood information into the membership function for clustering. The distribution statistics of the neighborhood pixels and the prior probability are used to form a new membership func- tion. It is not only effective to remove the noise spots but also can reduce the misclassified pixels. Experimental results indicate that the proposed algorithm is more accurate and robust to noise than the standard FCM algorithm.
基金supported by the Specialized Research Found for the Doctoral Program of Higher Education (20070699013)the Natural Science Foundation of Shaanxi Province (2006F05)the Aeronautical Science Foundation (05I53076)
文摘Traditional image segmentation methods based on MRF converge slowly and require pre-defined weight. These disadvantages are addressed, and a fast segmentation approach based on simple Markov random field （MRF） for SAR image is proposed. The approach is firstly used to perform coarse segmentation in blocks. Then the image is modeled with simple MRF and adaptive variable weighting forms are applied in homogeneous and heterogeneous regions. As a result, the convergent speed is accelerated while the segmentation results in homogeneous regions and boarders are improved. Simulations with synthetic and real SAR images demonstrate the effectiveness of the proposed approach.
基金supported by the China Postdoctoral Science Foundation(20100471451)the Science and Technology Foundation of State Key Laboratory of Underwater Measurement&Control Technology(9140C2603051003)
文摘In the methods of image thresholding segmentation, such methods based on two-dimensional （2D） histogram and optimal objective functions are important. However, when they are used for infrared image segmentation, they are weak in suppressing background noises and worse in segmenting targets with non-uniform gray level. The concept of 2D histogram shape modification is proposed, which is realized by target information prior restraint after enhancing target information using plateau histogram equalization. The formula of 2D minimum Renyi entropy is deduced for image segmentation, then the shape-modified 2D histogram is combined wfth four optimal objective functions （i.e., maximum between-class variance, maximum entropy, maximum correlation and minimum Renyi entropy） respectively for the appli- cation of infrared image segmentation. Simultaneously, F-measure is introduced to evaluate the segmentation effects objectively. The experimental results show that F-measure is an effective evaluation index for image segmentation since its value is fully consistent with the subjective evaluation, and after 2D histogram shape modification, the methods of optimal objective functions can overcome their original forms＇ deficiency and their segmentation effects are more or less improvements, where the best one is the maximum entropy method based on 2D histogram shape modification.
基金This project was supported by the National Natural Foundation of China (60404022) and the Foundation of Department ofEducation of Hebei Province (2002209).
文摘Mixture model based image segmentation method, which assumes that image pixels are independent and do not consider the position relationship between pixels, is not robust to noise and usually leads to misclassification. A new segmentation method, called multi-resolution Ganssian mixture model method, is proposed. First, an image pyramid is constructed and son-father link relationship is built between each level of pyramid. Then the mixture model segmentation method is applied to the top level. The segmentation result on the top level is passed top-down to the bottom level according to the son-father link relationship between levels. The proposed method considers not only local but also global information of image, it overcomes the effect of noise and can obtain better segmentation result. Experimental result demonstrates its effectiveness.
基金Projects(6177021519,61503373)supported by National Natural Science Foundation of ChinaProject(N161705001)supported by Fundamental Research Funds for the Central University,China
文摘A modified artificial bee colony optimizer(MABC)is proposed for image segmentation by using a pool of optimal foraging strategies to balance the exploration and exploitation tradeoff.The main idea of MABC is to enrichartificial bee foraging behaviors by combining local search and comprehensive learning using multi-dimensional PSO-based equation.With comprehensive learning,the bees incorporate the information of global best solution into the solution search equation to improve the exploration while the local search enables the bees deeply exploit around the promising area,which provides a proper balance between exploration and exploitation.The experimental results on comparing the MABC to several successful EA and SI algorithms on a set of benchmarks demonstrated the effectiveness of the proposed algorithm.Furthermore,we applied the MABC algorithm to image segmentation problem.Experimental results verify the effectiveness of the proposed algorithm.
基金Projects(60634020,60874069) supported by the National Natural Science Foundation of ChinaProject(2009AA04Z137) supported by the National High-Tech Research and Development Program of China
文摘In order to extract froth morphological feature,a bubble image adaptive segmentation method was proposed.Considering the image's low contrast and weak froth edges,froth image was coarsely segmented by using fuzzy c means(FCM) algorithm. Through the attributes of size and shape pattern spectrum,the optimal morphological structuring element was determined.According to the optimal parameters,some image noises were removed with an improved area opening and closing by reconstruction operation,which consist of image regional markers,and the bubbles were finely separated from each other by watershed transform.The experimental results show that the structural element can be determined adaptively by shape and size pattern spectrum,and the froth image is segmented accurately.Compared with other froth image segmentation method,the proposed method achieves much high accuracy,based on which,the bubble size and shape features are extracted effectively.
基金Project(61806107)supported by the National Natural Science Foundation of ChinaProject supported by the Shandong Key Laboratory of Wisdom Mine Information Technology,ChinaProject supported by the Opening Project of State Key Laboratory of Digital Publishing Technology,China。
文摘Semantic segmentation is a crucial step for document understanding.In this paper,an NVIDIA Jetson Nano-based platform is applied for implementing semantic segmentation for teaching artificial intelligence concepts and programming.To extract semantic structures from document images,we present an end-to-end dilated convolution network architecture.Dilated convolutions have well-known advantages for extracting multi-scale context information without losing spatial resolution.Our model utilizes dilated convolutions with residual network to represent the image features and predicting pixel labels.The convolution part works as feature extractor to obtain multidimensional and hierarchical image features.The consecutive deconvolution is used for producing full resolution segmentation prediction.The probability of each pixel decides its predefined semantic class label.To understand segmentation granularity,we compare performances at three different levels.From fine grained class to coarse class levels,the proposed dilated convolution network architecture is evaluated on three document datasets.The experimental results have shown that both semantic data distribution imbalance and network depth are import factors that influence the document’s semantic segmentation performances.The research is aimed at offering an education resource for teaching artificial intelligence concepts and techniques.
基金supported by the National Natural Science Foundationof China(61272119)
文摘The similarity measure is crucial to the performance of spectral clustering. The Gaussian kernel function based on the Euclidean distance is usual y adopted as the similarity measure. However, the Euclidean distance measure cannot ful y reveal the complex distribution data, and the result of spectral clustering is very sensitive to the scaling parameter. To solve these problems, a new manifold distance measure and a novel simulated anneal-ing spectral clustering （SASC） algorithm based on the manifold distance measure are proposed. The simulated annealing based on genetic algorithm （SAGA）, characterized by its rapid convergence to the global optimum, is used to cluster the sample points in the spectral mapping space. The proposed algorithm can not only reflect local and global consistency better, but also reduce the sensitivity of spectral clustering to the kernel parameter, which improves the algorithm’s clustering performance. To efficiently apply the algorithm to image segmentation, the Nystrom method is used to reduce the computation complexity. Experimental results show that compared with traditional clustering algorithms and those popular spectral clustering algorithms, the proposed algorithm can achieve better clustering performances on several synthetic datasets, texture images and real images.
基金Project(60874070) supported by the National Natural Science Foundation of China
文摘To improve the segmentation quality and efficiency of color image,a novel approach which combines the advantages of the mean shift(MS) segmentation and improved ant clustering method is proposed.The regions which can preserve the discontinuity characteristics of an image are segmented by MS algorithm,and then they are represented by a graph in which every region is represented by a node.In order to solve the graph partition problem,an improved ant clustering algorithm,called similarity carrying ant model(SCAM-ant),is proposed,in which a new similarity calculation method is given.Using SCAM-ant,the maximum number of items that each ant can carry will increase,the clustering time will be effectively reduced,and globally optimized clustering can also be realized.Because the graph is not based on the pixels of original image but on the segmentation result of MS algorithm,the computational complexity is greatly reduced.Experiments show that the proposed method can realize color image segmentation efficiently,and compared with the conventional methods based on the image pixels,it improves the image segmentation quality and the anti-interference ability.
文摘A fast interactive segmentation algorithm of image-sequences based on relative fuzzy connectedness is presented. In comparison with the original algorithm, the proposed one, with the same accuracy, accelerates the segmentation speed by three times for single image. Meanwhile, this fast segmentation algorithm is extended from single object to multiple objects and from single-image to image-sequences. Thus the segmentation of multiple objects from complex hackground and batch segmentation of image-sequences can be achieved. In addition, a post-processing scheme is incorporated in this algorithm, which extracts smooth edge with one-pixel-width for each segmented object. The experimental results illustrate that the proposed algorithm can obtain the object regions of interest from medical image or image-sequences as well as man-made images quickly and reliably with only a little interaction.
基金Project(06JJ50110) supported by the Natural Science Foundation of Hunan Province, China
文摘To solve the problem of poor anti-noise performance of the traditional fuzzy C-means (FCM) algorithm in image segmentation, a novel two-dimensional FCM clustering algorithm for image segmentation was proposed. In this method, the image segmentation was converted into an optimization problem. The fitness function containing neighbor information was set up based on the gray information and the neighbor relations between the pixels described by the improved two-dimensional histogram. By making use of the global searching ability of the predator-prey particle swarm optimization, the optimal cluster center could be obtained by iterative optimization, and the image segmentation could be accomplished. The simulation results show that the segmentation accuracy ratio of the proposed method is above 99%. The proposed algorithm has strong anti-noise capability, high clustering accuracy and good segment effect, indicating that it is an effective algorithm for image segmentation.
基金supported by the National Natural Science Foundation of China(4117132741301361)+2 种基金the National Key Basic Research Program of China(973 Program)(2012CB719903)the Science and Technology Project of Ministry of Transport of People’s Republic of China(2012-364-X11-803)the Shanghai Municipal Natural Science Foundation(12ZR1433200)
文摘Segmentation is the key step in auto-interpretation of high-resolution spaceborne synthetic aperture radar（SAR） images. A novel method is proposed based on integrating the geometric active contour（GAC） and the support vector machine（SVM）models. First, the images are segmented by using SVM and textural statistics. A likelihood measurement for every pixel is derived by using the initial segmentation. The Chan-Vese model then is modified by adding two items： the likelihood and the distance between the initial segmentation and the evolving contour. Experimental results using real SAR images demonstrate the good performance of the proposed method compared to several classic GAC models.
基金supported by the National Natural Science Foundation of China(61073106)the Aerospace Science and Technology Innovation Fund(CASC201105)
文摘An image segmentation algorithm of the restrained fuzzy Kohonen clustering network （RFKCN） based on high- dimension fuzzy character is proposed. The algorithm includes two steps. The first step is the fuzzification of pixels in which two redundant images are built by fuzzy mean value and fuzzy median value. The second step is to construct a three-dimensional （3-D） feature vector of redundant images and their original images and cluster the feature vector through RFKCN, to realize image seg- mentation. The proposed algorithm fully takes into account not only gray distribution information of pixels, but also relevant information and fuzzy information among neighboring pixels in constructing 3- D character space. Based on the combination of competitiveness, redundancy and complementary of the information, the proposed algorithm improves the accuracy of clustering. Theoretical anal- yses and experimental results demonstrate that the proposed algorithm has a good segmentation performance.
基金supported by the National Natural Science Foundation of China(6100317061372142+2 种基金61103121)the Fundamental Research Funds for the Central Universities SCUT(2014ZG0037)the China Postdoctoral Science Foundation(2012M511561)
文摘The active contour model based on local image fitting （LIF） energy is an effective method to deal with intensity inhomo- geneities, but it always conflicts with the local minimum problem because LIF has a nonconvex energy function form. At the same time, the parameters of LIF are hard to be chosen for better per- formance. A global minimization of the adaptive LIF energy model is proposed. The regularized length term which constrains the zero level set is introduced to improve the accuracy of the bound- aries, and a global minimization of the active contour model is presented, in addition, based on the statistical information of the intensity histogram, the standard deviation σ with respect to the truncated Gaussian window is automatically computed according to images. Consequently, the proposed method improves the performance and adaptivity to deal with the intensity inhomo- geneities. Experimental results for synthetic and real images show desirable performance and efficiency of the proposed method.
文摘Aiming at measurement of granularity size of nonmetal grain, an algorithm of image segmentation and parameter calculation for microscopic overlapping grain image was studied. This algorithm presents some new attributes of graph sequence from discrete attribute of graph,and consequently achieves the geometrical characteristics from input graph, and the new graph sequence in favor of image segmentation is recombined. The conception that image edge denoted with “twin-point” is put forward, base on geometrical characters of point, image edge is transformed into serial edge, and on recombined serial image edge, based on direction vector definition of line and some additional restricted conditions, the segmentation twin-points are searched with, thus image segmentation is accomplished. Serial image edge is transformed into twin-point pattern, to realize calculation of area and granularity size of nonmetal grain. The inkling and uncertainty on selection of structure element which base on mathematical morphology are avoided in this algorithm, and image segmentation and parameter calculation are realized without changing grain’s self statistical characters.
基金Supported by National Basic Research Development Program of China（973 Program）（2007CB311006） National Natural Science Foundation of China（60602026）,Acknowledgement The authors would like to thank ESA （http：//earth.esa. int/polsarpro/datasets.html） for providing the data.
文摘The dynamic transmission characteristics and the sensitivities of the three stage idler gear system of the new NC power turret are studied in the paper. Considering the strongly nonlinear factors such as the periodically time-varying mesh stiffness, the nonlinear tooth backlash, the lump-parameter model of the gear system is developed with one rotational and two translational freedoms of each gear. The eigen-values and eigenvectors are derived and analyzed on the basis of the real modal theory. The sensitivities of natural frequencies to design parameters including supporting and meshing stiffnesses, gear masses, and moments of inertia by the direct differential method are also calculated. The results show the quantitative and qualitative impact of the parameters to the natural characteristics of the gear system. Furthermore, the periodic steady state solutions are obtained by the numerical approach based on the nonlinear model. These results are employed to gain insights into the primary controlling parameters, to forecast the severity of the dynamic response, and to assess the acceptability of the gear design.
基金supported in part by National Sciences Foundation of China grant ( 11672001)Jiangsu Province Science and Technology Agency grant ( BE2016785)
文摘Background Cardiovascular diseases are closely associated with atherosclerotic plaque development and rupture.Traditional medical imaging techniques such as magnetic resonance imaging(MRI)and intravascular ultrasound(IVUS)were unable to identify vulnerable plaques due to their limited resolution.Fortunately,optical coherence tomography(OCT)is an advanced intravascular imaging technique developed in recent years which has high resolution approximately 10 microns and could provide more accurate morphology of coronary plaque.In particular,it has the ability to identify plaques with fibrous cap thickness<65μm,an accepted threshold value for vulnerable plaques.However,segmentation of OCT images in clinic is still mainly performed manually by physicians which is time consuming and subjective.To overcome time consumption,several methodologies have been proposed for automatic segmentation of OCT images but most of these methods were still limited by intricate image preprocessing and expensive computation.In this research,two automatic segmentation methods for intracoronary OCT image based on support vector machine(SVM)and convolutional neural network(CNN)were performed to identify the plaque region and characterize plaque components.Methods In vivo IVUS and OCT coronary plaque data from 5 patients were acquired at Emory University with patient’s consent obtained.OCT were obtained from ILUMIEN OPTIS System(St.Jude,Minnesota,MN).The OCT catheter was traversed to the segment of interest and the catheter pullback was limited at a rate of 20 mm/sec.Following the OCT image acquisition,the IVUS catheter was traversed distally though the artery to the same coronary segment(Volcano Therapeutics,Rancho Cordova)and the catheter pullback speed was at a standard rate of 0.5 mm/sec.Seventy-seven matched IVUS and OCT slices with good image quality and lipid cores were selected for our segmentation study.Manual OCT segmentation was performed by experts and used as gold standard in the automatic segmentations.VH-IVUS was used as references and guide by the experts in the manual segmentation process.Three plaque component tissue classes were identified from OCT images in this work:lipid tissue(LT),fibrous tissue(FT)and background(BG).Procedures using two machine learning methods(CNN and SVM)were developed to segment OCT images,respectively.For CNN method,the U-Net architecture was selected due to its good performance in very different biomedical segmentation and very few annotated images.For SVM method,local binary patterns(LBPs),gray level co-occurrence matrices(GLCMs)which contains contrast,correlation,energy and homogeneity,entropy and mean value were calculated as features and assembled to feed SVM classifier.The accuracies of two segmentation methods were evaluated and compared using the OCT dataset.Segmentation accuracy is defined as the ratio of the number of pixels correctly classified over the total number of pixels.Results The overall classification accuracy based CNN method reached 95.8%,and the accuracies for LT,FT and BG were 86.8%,83.4%,and 98.2%,respectively.The overall classification accuracy based SVM was 71.9%,and per-class accuracy for LT,FT and BG was 75.4%,78.3%,and67.0%,respectively.Conclusions The two methods proposed can automatically identify plaque region and characterize plaque compositions for OCT images and potentially reduce the time spent by doctors in segmenting and evaluating coronary plaque OCT images.CNN provided better segmentation accuracies compared to those achieved by SVM.
文摘The document image segmentation is very useful for printing, faxing and data processing. An algorithm is developed for segmenting and classifying document image. Feature used for classification is based on the histogram distribution pattern of different image classes. The important attribute of the algorithm is using wavelet correlation image to enhance raw image's pattern, so the classification accuracy is improved. In this paper document image is divided into four types; background, photo, text and graph. Firstly, the document image background has been distingusished easily by former normally method;secondly, three image types will be distinguished by their typical histograms, in order to make histograms feature clearer, each resolution's HH wavelet subimage is used to add to the raw image at their resolution. At last, the photo, text and praph have been devided according to how the feature fit to the Laplacian distrbution by 2 and L . Simulations show that classification accuracy is significantly improved. The comparison with related shows that our algorithm provides both lower classification error rates and better visual results.
