In this paper, the positional error curve of point features was extended to an error curves band of line segment features. Firstly, the constitution and shape of the error curves band were analyzed. On this basis, the...In this paper, the positional error curve of point features was extended to an error curves band of line segment features. Firstly, the constitution and shape of the error curves band were analyzed. On this basis, the general boundary curve formula of that band was derived. Secondly, the visualizing error curves bands were realized through three exam- ples. Finally,area index has been examined by comparing numerical results from error curves band and error ellipes band.展开更多
To determine the distribution of positional error of a line segment, Monte Carlo approach is applied to simulate the probability density function of a line segment with the assumption that the error of endpoints in a ...To determine the distribution of positional error of a line segment, Monte Carlo approach is applied to simulate the probability density function of a line segment with the assumption that the error of endpoints in a line segment follows a two-dimensional normal distribution. For such purpose, a stochastic generator used for uncertain endpoints with the two-dimensional normal distribution is presented. This forms the basis of the generation of random line segment for the simulation of the error model of a whole line segment. The error models cover the cases where two endpoints are either independent or dependent to each other, also including a special case that the distance between two random endpoints in a line segment is close enough.展开更多
Medical diagnosis software and computer-assisted surgical systems often use segmented image data to help clinicians make decisions. The segmentation extracts the region of interest from the background, which makes the...Medical diagnosis software and computer-assisted surgical systems often use segmented image data to help clinicians make decisions. The segmentation extracts the region of interest from the background, which makes the visualization clearer. However, no segmentation method can guarantee accurate results under all circumstances. As a result, the clinicians need a solution that enables them to check and validate the segmentation accuracy as well as displaying the segmented area without ambiguities. With the method presented in this paper, the real CT or MR image is displayed within the segmented region and the segmented boundaries can be expanded or contracted interactively. By this way, the clinicians are able to check and validate the segmentation visually and make more reliable decisions. After experiments with real data from a hospital, the presented method is proved to be suitable for efficiently detecting segmentation errors. The new algorithm uses new graphic processing uint （GPU） shading functions recently introduced in graphic cards and is fast enough to interact oil the segmented area, which was not possible with previous methods.展开更多
The mathematic theory for uncertainty model of line segment are summed up to achieve a general conception, and the line error band model of ε_σ is a basic uncertainty model that can depict the line accuracy and qual...The mathematic theory for uncertainty model of line segment are summed up to achieve a general conception, and the line error band model of ε_σ is a basic uncertainty model that can depict the line accuracy and quality efficiently while the model of ε_m and error entropy can be regarded as the supplement of it. The error band model will reflect and describe the influence of line uncertainty on polygon uncertainty. Therefore, the statistical characteristic of the line error is studied deeply by analyzing the probability that the line error falls into a certain range. Moreover, the theory accordance is achieved in the selecting the error buffer for line feature and the error indicator. The relationship of the accuracy of area for a polygon with the error loop for a polygon boundary is deduced and computed.展开更多
Recent convolutional neural networks(CNNs)based deep learning has significantly promoted fire detection.Existing fire detection methods can efficiently recognize and locate the fire.However,the accurate flame boundary...Recent convolutional neural networks(CNNs)based deep learning has significantly promoted fire detection.Existing fire detection methods can efficiently recognize and locate the fire.However,the accurate flame boundary and shape information is hard to obtain by them,which makes it difficult to conduct automated fire region analysis,prediction,and early warning.To this end,we propose a fire semantic segmentation method based on Global Position Guidance(GPG)and Multi-path explicit Edge information Interaction(MEI).Specifically,to solve the problem of local segmentation errors in low-level feature space,a top-down global position guidance module is used to restrain the offset of low-level features.Besides,an MEI module is proposed to explicitly extract and utilize the edge information to refine the coarse fire segmentation results.We compare the proposed method with existing advanced semantic segmentation and salient object detection methods.Experimental results demonstrate that the proposed method achieves 94.1%,93.6%,94.6%,95.3%,and 95.9%Intersection over Union(IoU)on five test sets respectively which outperforms the suboptimal method by a large margin.In addition,in terms of accuracy,our approach also achieves the best score.展开更多
To overcome the shortcoming that the traditional minimum error threshold method can obtain satisfactory image segmentation results only when the object and background of the image strictly obey a certain type of proba...To overcome the shortcoming that the traditional minimum error threshold method can obtain satisfactory image segmentation results only when the object and background of the image strictly obey a certain type of probability distribution,one proposes the regularized minimum error threshold method and treats the traditional minimum error threshold method as its special case.Then one constructs the discrete probability distribution by using the separation between segmentation threshold and the average gray-scale values of the object and background of the image so as to compute the information energy of the probability distribution.The impact of the regularized parameter selection on the optimal segmentation threshold of the regularized minimum error threshold method is investigated.To verify the effectiveness of the proposed regularized minimum error threshold method,one selects typical grey-scale images and performs segmentation tests.The segmentation results obtained by the regularized minimum error threshold method are compared with those obtained with the traditional minimum error threshold method.The segmentation results and their analysis show that the regularized minimum error threshold method is feasible and produces more satisfactory segmentation results than the minimum error threshold method.It does not exert much impact on object acquisition in case of the addition of a certain noise to an image.Therefore,the method can meet the requirements for extracting a real object in the noisy environment.展开更多
A formula to compute the similarity between two audio feature vectors is proposed, which can map arbitrary pair of vectors with equivalent dimension to [0,1）. To fulfill the task of audio segmentation, a self-similar...A formula to compute the similarity between two audio feature vectors is proposed, which can map arbitrary pair of vectors with equivalent dimension to [0,1）. To fulfill the task of audio segmentation, a self-similarity matrix is computed to reveal the inner structure of an audio clip to be segmented. As the final result must be consistent with the subjective evaluation and be adaptive to some special applications, a set of weights is adopted, which can be modified through relevance feedback techniques. Experiments show that satisfactory result can be achieved via the algorithm proposed in this paper.展开更多
This study is going to explore two questions: a) Do Chinese learners have awareness of what kind of pronunciation errors they make; b) What factors will affect Chinese learners' willingness to correct the pronunci...This study is going to explore two questions: a) Do Chinese learners have awareness of what kind of pronunciation errors they make; b) What factors will affect Chinese learners' willingness to correct the pronunciation errors they make. The instrument will consist of 4 questionnaires or 4 parts developed by four researchers(Luk and Sardegna, Lee, Kusey). The whole study will include 6 parts: background information about pronunciation; two research questions; the methodology; the results;the discussion and a conclusion.展开更多
基金Project Supported by the National Natural Science Foundation of China (No.49801016 and 49671063)
文摘In this paper, the positional error curve of point features was extended to an error curves band of line segment features. Firstly, the constitution and shape of the error curves band were analyzed. On this basis, the general boundary curve formula of that band was derived. Secondly, the visualizing error curves bands were realized through three exam- ples. Finally,area index has been examined by comparing numerical results from error curves band and error ellipes band.
基金Funded by the National Natural Science Foundation of China (N0. 40501053), the Open Research Fund Program of LIESMARS (No. WKL040304) and theOpen Research Fund Program of Key Laboratory of Geomatics and Digital Technology, Shandong Province (No. SD040201)
文摘To determine the distribution of positional error of a line segment, Monte Carlo approach is applied to simulate the probability density function of a line segment with the assumption that the error of endpoints in a line segment follows a two-dimensional normal distribution. For such purpose, a stochastic generator used for uncertain endpoints with the two-dimensional normal distribution is presented. This forms the basis of the generation of random line segment for the simulation of the error model of a whole line segment. The error models cover the cases where two endpoints are either independent or dependent to each other, also including a special case that the distance between two random endpoints in a line segment is close enough.
基金Project supported by the National Natural Science Foundation of China （Grant No.60572154）, and the National Basic Research Program of China （Grant No.2003CB716104）Acknowledgment I would like to thank YANG Xin, my tutor, SHANG Yan- feng, SUN Kun of Shanghai Children＇s Medical Center, and all the people in 3D Visualization Laboratory of Shanghai Jiaotong University for their help during my research.
文摘Medical diagnosis software and computer-assisted surgical systems often use segmented image data to help clinicians make decisions. The segmentation extracts the region of interest from the background, which makes the visualization clearer. However, no segmentation method can guarantee accurate results under all circumstances. As a result, the clinicians need a solution that enables them to check and validate the segmentation accuracy as well as displaying the segmented area without ambiguities. With the method presented in this paper, the real CT or MR image is displayed within the segmented region and the segmented boundaries can be expanded or contracted interactively. By this way, the clinicians are able to check and validate the segmentation visually and make more reliable decisions. After experiments with real data from a hospital, the presented method is proved to be suitable for efficiently detecting segmentation errors. The new algorithm uses new graphic processing uint （GPU） shading functions recently introduced in graphic cards and is fast enough to interact oil the segmented area, which was not possible with previous methods.
基金Project supported by the National Natural Science Foundation of China (No.40301043) .
文摘The mathematic theory for uncertainty model of line segment are summed up to achieve a general conception, and the line error band model of ε_σ is a basic uncertainty model that can depict the line accuracy and quality efficiently while the model of ε_m and error entropy can be regarded as the supplement of it. The error band model will reflect and describe the influence of line uncertainty on polygon uncertainty. Therefore, the statistical characteristic of the line error is studied deeply by analyzing the probability that the line error falls into a certain range. Moreover, the theory accordance is achieved in the selecting the error buffer for line feature and the error indicator. The relationship of the accuracy of area for a polygon with the error loop for a polygon boundary is deduced and computed.
基金This work was supported in part by the Important Science and Technology Project of Hainan Province under Grant ZDKJ2020010in part by Frontier Exploration Project Independently Deployed by Institute of Acoustics,Chinese Academy of Sciences under Grant QYTS202015 and Grant QYTS202115.
文摘Recent convolutional neural networks(CNNs)based deep learning has significantly promoted fire detection.Existing fire detection methods can efficiently recognize and locate the fire.However,the accurate flame boundary and shape information is hard to obtain by them,which makes it difficult to conduct automated fire region analysis,prediction,and early warning.To this end,we propose a fire semantic segmentation method based on Global Position Guidance(GPG)and Multi-path explicit Edge information Interaction(MEI).Specifically,to solve the problem of local segmentation errors in low-level feature space,a top-down global position guidance module is used to restrain the offset of low-level features.Besides,an MEI module is proposed to explicitly extract and utilize the edge information to refine the coarse fire segmentation results.We compare the proposed method with existing advanced semantic segmentation and salient object detection methods.Experimental results demonstrate that the proposed method achieves 94.1%,93.6%,94.6%,95.3%,and 95.9%Intersection over Union(IoU)on five test sets respectively which outperforms the suboptimal method by a large margin.In addition,in terms of accuracy,our approach also achieves the best score.
基金supported by the National Natural Science Foundations of China(Nos.61136002,61472324)the Natural Science Foundation of Shanxi Province(No.2014JM8331)
文摘To overcome the shortcoming that the traditional minimum error threshold method can obtain satisfactory image segmentation results only when the object and background of the image strictly obey a certain type of probability distribution,one proposes the regularized minimum error threshold method and treats the traditional minimum error threshold method as its special case.Then one constructs the discrete probability distribution by using the separation between segmentation threshold and the average gray-scale values of the object and background of the image so as to compute the information energy of the probability distribution.The impact of the regularized parameter selection on the optimal segmentation threshold of the regularized minimum error threshold method is investigated.To verify the effectiveness of the proposed regularized minimum error threshold method,one selects typical grey-scale images and performs segmentation tests.The segmentation results obtained by the regularized minimum error threshold method are compared with those obtained with the traditional minimum error threshold method.The segmentation results and their analysis show that the regularized minimum error threshold method is feasible and produces more satisfactory segmentation results than the minimum error threshold method.It does not exert much impact on object acquisition in case of the addition of a certain noise to an image.Therefore,the method can meet the requirements for extracting a real object in the noisy environment.
文摘A formula to compute the similarity between two audio feature vectors is proposed, which can map arbitrary pair of vectors with equivalent dimension to [0,1）. To fulfill the task of audio segmentation, a self-similarity matrix is computed to reveal the inner structure of an audio clip to be segmented. As the final result must be consistent with the subjective evaluation and be adaptive to some special applications, a set of weights is adopted, which can be modified through relevance feedback techniques. Experiments show that satisfactory result can be achieved via the algorithm proposed in this paper.
文摘This study is going to explore two questions: a) Do Chinese learners have awareness of what kind of pronunciation errors they make; b) What factors will affect Chinese learners' willingness to correct the pronunciation errors they make. The instrument will consist of 4 questionnaires or 4 parts developed by four researchers(Luk and Sardegna, Lee, Kusey). The whole study will include 6 parts: background information about pronunciation; two research questions; the methodology; the results;the discussion and a conclusion.
