Point clouds are widely used in Earth surface research but usually exhibit gaps of missing data.Previous point cloud restoration methods used in terrain modelling have not fully considered complex terrain characterist...Point clouds are widely used in Earth surface research but usually exhibit gaps of missing data.Previous point cloud restoration methods used in terrain modelling have not fully considered complex terrain characteristics,which can be summarised as the controlling role of topographic features in shaping terrain surfaces and the inherent similarities observed among these surfaces.This work introduces a novel method that integrates Topographic Features and Patch Matching(TFPM)into point cloud restoration processes for terrain modelling.The method mainly contains three steps.First,identifying gap boundary points.Second,topographic feature points are extracted and subsequently interpolated into the identified gaps.Third,searching other parts of the raw point cloud for patches resembling the gaps,and the identified patches are used as templates to restore the point cloud.The proposed method is benchmarked against three state-of-the-art point cloud restoration methods.The experimental results demonstrate that the TFPM method consistently exhibits superior accuracy in terrain modelling and analysis,as evidenced by low values of the root mean square error,average elevation difference,and average slope difference.This work endeavours to incorporate topographic features into point cloud restoration processes and can benefit future research related to terrain modelling and analysis.展开更多
The video inpainting process helps in several video editing and restoration processes like unwanted object removal,scratch or damage rebuilding,and retargeting.It intends to fill spatio-temporal holes with reasonable ...The video inpainting process helps in several video editing and restoration processes like unwanted object removal,scratch or damage rebuilding,and retargeting.It intends to fill spatio-temporal holes with reasonable content in the video.Inspite of the recent advancements of deep learning for image inpainting,it is challenging to outspread the techniques into the videos owing to the extra time dimensions.In this view,this paper presents an efficient video inpainting approach using beetle antenna search with deep belief network(VIA-BASDBN).The proposed VIA-BASDBN technique initially converts the videos into a set of frames and they are again split into a region of 5*5 blocks.In addition,the VIABASDBN technique involves the design of optimal DBN model,which receives input features from Local Binary Patterns(LBP)to categorize the blocks into smooth or structured regions.Furthermore,the weight vectors of the DBN model are optimally chosen by the use of BAS technique.Finally,the inpainting of the smooth and structured regions takes place using the mean and patch matching approaches respectively.The patch matching process depends upon the minimal Euclidean distance among the extracted SIFT features of the actual and references patches.In order to examine the effective outcome of the VIA-BASDBN technique,a series of simulations take place and the results denoted the promising performance.展开更多
Three dimensional(3D)echocardiogram enables cardiologists to visua-lize suspicious cardiac structures in detail.In recent years,this three-dimensional echocardiogram carries important clinical value in virtual surgica...Three dimensional(3D)echocardiogram enables cardiologists to visua-lize suspicious cardiac structures in detail.In recent years,this three-dimensional echocardiogram carries important clinical value in virtual surgical simulation.However,this 3D echocardiogram involves a trade-off difficulty between accu-racy and efficient computation in clinical diagnosis.This paper presents a novel Flip Directional 3D Volume Reconstruction(FD-3DVR)method for the recon-struction of echocardiogram images.The proposed method consists of two main steps:multiplanar volumetric imaging and 3D volume reconstruction.In the crea-tion of multiplanar volumetric imaging,two-dimensional(2D)image pixels are mapped into voxels of the volumetric grid.As the obtained slices are discontin-uous,there are some missing voxels in the volume data.To restore the structural and textural information of 3D ultrasound volume,the proposed method creates a volume pyramid in parallel with theflip directional texture pyramid.Initially,the nearest neighbors of missing voxels in the multiplanar volumetric imaging are identified by 3D ANN(Approximate Nearest Neighbor)patch matching method.Furthermore,aflip directional texture pyramid is proposed and aggregated with distance in patch matching tofind out the most similar neighbors.In the recon-struction step,structural and textural information obtained from differentflip angle directions can reconstruct 3D volume well with the desired accuracy.Com-pared with existing 3D reconstruction methods,the proposed Flip Directional 3D Volume Reconstruction(FD-3DVR)method provides superior performance for the mean peak signal-to-noise ratio(40.538 for the proposed method I and 39.626 for the proposed method II).Experimental results performed on the cardi-ac datasets demonstrate the efficiency of the proposed method for the reconstruc-tion of echocardiogram images.展开更多
基金supported by the National Natural Science Foundation of China under Grant[41971333,41930102,42371407]Priority Academic Programme Development of Jiangsu Higher Education Institutions under Grant[164320H116]The Priority Academic Program Development of Jiangsu Higher Education Institutions and the Deep-time Digital Earth(DDE)Big Science Program.
文摘Point clouds are widely used in Earth surface research but usually exhibit gaps of missing data.Previous point cloud restoration methods used in terrain modelling have not fully considered complex terrain characteristics,which can be summarised as the controlling role of topographic features in shaping terrain surfaces and the inherent similarities observed among these surfaces.This work introduces a novel method that integrates Topographic Features and Patch Matching(TFPM)into point cloud restoration processes for terrain modelling.The method mainly contains three steps.First,identifying gap boundary points.Second,topographic feature points are extracted and subsequently interpolated into the identified gaps.Third,searching other parts of the raw point cloud for patches resembling the gaps,and the identified patches are used as templates to restore the point cloud.The proposed method is benchmarked against three state-of-the-art point cloud restoration methods.The experimental results demonstrate that the TFPM method consistently exhibits superior accuracy in terrain modelling and analysis,as evidenced by low values of the root mean square error,average elevation difference,and average slope difference.This work endeavours to incorporate topographic features into point cloud restoration processes and can benefit future research related to terrain modelling and analysis.
文摘The video inpainting process helps in several video editing and restoration processes like unwanted object removal,scratch or damage rebuilding,and retargeting.It intends to fill spatio-temporal holes with reasonable content in the video.Inspite of the recent advancements of deep learning for image inpainting,it is challenging to outspread the techniques into the videos owing to the extra time dimensions.In this view,this paper presents an efficient video inpainting approach using beetle antenna search with deep belief network(VIA-BASDBN).The proposed VIA-BASDBN technique initially converts the videos into a set of frames and they are again split into a region of 5*5 blocks.In addition,the VIABASDBN technique involves the design of optimal DBN model,which receives input features from Local Binary Patterns(LBP)to categorize the blocks into smooth or structured regions.Furthermore,the weight vectors of the DBN model are optimally chosen by the use of BAS technique.Finally,the inpainting of the smooth and structured regions takes place using the mean and patch matching approaches respectively.The patch matching process depends upon the minimal Euclidean distance among the extracted SIFT features of the actual and references patches.In order to examine the effective outcome of the VIA-BASDBN technique,a series of simulations take place and the results denoted the promising performance.
文摘Three dimensional(3D)echocardiogram enables cardiologists to visua-lize suspicious cardiac structures in detail.In recent years,this three-dimensional echocardiogram carries important clinical value in virtual surgical simulation.However,this 3D echocardiogram involves a trade-off difficulty between accu-racy and efficient computation in clinical diagnosis.This paper presents a novel Flip Directional 3D Volume Reconstruction(FD-3DVR)method for the recon-struction of echocardiogram images.The proposed method consists of two main steps:multiplanar volumetric imaging and 3D volume reconstruction.In the crea-tion of multiplanar volumetric imaging,two-dimensional(2D)image pixels are mapped into voxels of the volumetric grid.As the obtained slices are discontin-uous,there are some missing voxels in the volume data.To restore the structural and textural information of 3D ultrasound volume,the proposed method creates a volume pyramid in parallel with theflip directional texture pyramid.Initially,the nearest neighbors of missing voxels in the multiplanar volumetric imaging are identified by 3D ANN(Approximate Nearest Neighbor)patch matching method.Furthermore,aflip directional texture pyramid is proposed and aggregated with distance in patch matching tofind out the most similar neighbors.In the recon-struction step,structural and textural information obtained from differentflip angle directions can reconstruct 3D volume well with the desired accuracy.Com-pared with existing 3D reconstruction methods,the proposed Flip Directional 3D Volume Reconstruction(FD-3DVR)method provides superior performance for the mean peak signal-to-noise ratio(40.538 for the proposed method I and 39.626 for the proposed method II).Experimental results performed on the cardi-ac datasets demonstrate the efficiency of the proposed method for the reconstruc-tion of echocardiogram images.
