Segmentation of three-dimensional(3D) complicated structures is of great importance for many real applications.In this work we combine graph cut minimization method with a variant of the level set idea for 3D segmenta...Segmentation of three-dimensional(3D) complicated structures is of great importance for many real applications.In this work we combine graph cut minimization method with a variant of the level set idea for 3D segmentation based on the Mumford-Shah model.Compared with the traditional approach for solving the Euler-Lagrange equation we do not need to solve any partial differential equations.Instead,the minimum cut on a special designed graph need to be computed.The method is tested on data with complicated structures.It is rather stable with respect to initial value and the algorithm is nearly parameter free.Experiments show that it can solve large problems much faster than traditional approaches.展开更多
Image dehazing is still an open research topic that has been undergoing a lot of development,especially with the renewed interest in machine learning-based methods.A major challenge of the existing dehazing methods is...Image dehazing is still an open research topic that has been undergoing a lot of development,especially with the renewed interest in machine learning-based methods.A major challenge of the existing dehazing methods is the estimation of transmittance,which is the key element of haze-affected imaging models.Conventional methods are based on a set of assumptions that reduce the solution search space.However,the multiplication of these assumptions tends to restrict the solutions to particular cases that cannot account for the reality of the observed image.In this paper we reduce the number of simplified hypotheses in order to attain a more plausible and realistic solution by exploiting a priori knowledge of the ground truth in the proposed method.The proposed method relies on pixel information between the ground truth and haze image to reduce these assumptions.This is achieved by using ground truth and haze image to find the geometric-pixel information through a guided Convolution Neural Networks(CNNs)with a Parallax Attention Mechanism(PAM).It uses the differential pixel-based variance in order to estimate transmittance.The pixel variance uses local and global patches between the assumed ground truth and haze image to refine the transmission map.The transmission map is also improved based on improved Markov random field(MRF)energy functions.We used different images to test the proposed algorithm.The entropy value of the proposed method was 7.43 and 7.39,a percent increase of4.35%and5.42%,respectively,compared to the best existing results.The increment is similar in other performance quality metrics and this validate its superiority compared to other existing methods in terms of key image quality evaluation metrics.The proposed approach’s drawback,an over-reliance on real ground truth images,is also investigated.The proposed method show more details hence yields better images than those from the existing state-of-the-art-methods.展开更多
A new stereo matching scheme from image pairs based on graph cuts is given,which can solve the problem of large color differences as the result of fusing matching results of graph cuts from different color spaces.This...A new stereo matching scheme from image pairs based on graph cuts is given,which can solve the problem of large color differences as the result of fusing matching results of graph cuts from different color spaces.This scheme builds normalized histogram and reference histogram from matching results,and uses clustering algorithm to process the two histograms.Region histogram statistical method is adopted to retrieve depth data to achieve final matching results.Regular stereo matching library is used to verify this scheme,and experiments reported in this paper support availability of this method for automatic image processing.This scheme renounces the step of manual selection for adaptive color space and can obtain stable matching results.The whole procedure can be executed automatically and improve the integration level of image analysis process.展开更多
基金support from the Centre for Integrated Petroleum Research(CIPR),University of Bergen, Norway,and Singapore MOE Grant T207B2202NRF2007IDMIDM002-010
文摘Segmentation of three-dimensional(3D) complicated structures is of great importance for many real applications.In this work we combine graph cut minimization method with a variant of the level set idea for 3D segmentation based on the Mumford-Shah model.Compared with the traditional approach for solving the Euler-Lagrange equation we do not need to solve any partial differential equations.Instead,the minimum cut on a special designed graph need to be computed.The method is tested on data with complicated structures.It is rather stable with respect to initial value and the algorithm is nearly parameter free.Experiments show that it can solve large problems much faster than traditional approaches.
基金This work was funded by the Deanship of Scientific Research at Jouf University under grant No DSR-2021-02-0398.
文摘Image dehazing is still an open research topic that has been undergoing a lot of development,especially with the renewed interest in machine learning-based methods.A major challenge of the existing dehazing methods is the estimation of transmittance,which is the key element of haze-affected imaging models.Conventional methods are based on a set of assumptions that reduce the solution search space.However,the multiplication of these assumptions tends to restrict the solutions to particular cases that cannot account for the reality of the observed image.In this paper we reduce the number of simplified hypotheses in order to attain a more plausible and realistic solution by exploiting a priori knowledge of the ground truth in the proposed method.The proposed method relies on pixel information between the ground truth and haze image to reduce these assumptions.This is achieved by using ground truth and haze image to find the geometric-pixel information through a guided Convolution Neural Networks(CNNs)with a Parallax Attention Mechanism(PAM).It uses the differential pixel-based variance in order to estimate transmittance.The pixel variance uses local and global patches between the assumed ground truth and haze image to refine the transmission map.The transmission map is also improved based on improved Markov random field(MRF)energy functions.We used different images to test the proposed algorithm.The entropy value of the proposed method was 7.43 and 7.39,a percent increase of4.35%and5.42%,respectively,compared to the best existing results.The increment is similar in other performance quality metrics and this validate its superiority compared to other existing methods in terms of key image quality evaluation metrics.The proposed approach’s drawback,an over-reliance on real ground truth images,is also investigated.The proposed method show more details hence yields better images than those from the existing state-of-the-art-methods.
基金Sponsored by"985"Second Procession Construction of Ministry of Education(3040012040101)
文摘A new stereo matching scheme from image pairs based on graph cuts is given,which can solve the problem of large color differences as the result of fusing matching results of graph cuts from different color spaces.This scheme builds normalized histogram and reference histogram from matching results,and uses clustering algorithm to process the two histograms.Region histogram statistical method is adopted to retrieve depth data to achieve final matching results.Regular stereo matching library is used to verify this scheme,and experiments reported in this paper support availability of this method for automatic image processing.This scheme renounces the step of manual selection for adaptive color space and can obtain stable matching results.The whole procedure can be executed automatically and improve the integration level of image analysis process.
