Two-dimensional endoscopic images are susceptible to interferences such as specular reflections and monotonous texture illumination,hindering accurate three-dimensional lesion reconstruction by surgical robots.This st...Two-dimensional endoscopic images are susceptible to interferences such as specular reflections and monotonous texture illumination,hindering accurate three-dimensional lesion reconstruction by surgical robots.This study proposes a novel end-to-end disparity estimation model to address these challenges.Our approach combines a Pseudo-Siamese neural network architecture with pyramid dilated convolutions,integrating multi-scale image information to enhance robustness against lighting interferences.This study introduces a Pseudo-Siamese structure-based disparity regression model that simplifies left-right image comparison,improving accuracy and efficiency.The model was evaluated using a dataset of stereo endoscopic videos captured by the Da Vinci surgical robot,comprising simulated silicone heart sequences and real heart video data.Experimental results demonstrate significant improvement in the network’s resistance to lighting interference without substantially increasing parameters.Moreover,the model exhibited faster convergence during training,contributing to overall performance enhancement.This study advances endoscopic image processing accuracy and has potential implications for surgical robot applications in complex environments.展开更多
An improved block matching approach to fast disparity estimation in machine vision applications is proposed, where the matching criterion is the sum of the absolute difference（SAD）.By evaluating the lower bounds, wh...An improved block matching approach to fast disparity estimation in machine vision applications is proposed, where the matching criterion is the sum of the absolute difference（SAD）.By evaluating the lower bounds, which become increasingly tighter for the matching criteria, the method tries to successively terminate unnecessary computations of the matching criteria between the reference block in one image and the ineligible candidate blocks in another image.It also eliminates the ineligible blocks as early as possible, while ensuring the optimal disparity of each pixel.Also, the proposed method can further speed up the elimination of ineligible candidate blocks by efficiently using the continuous constraint of disparity to predict the initial disparity of each pixel.The performance of the new algorithm is evaluated by carrying out a theoretical analysis, and by comparing its performance with the disparity estimation method based on the standard block matching.Simulated results demonstrate that the proposed algorithm achieves a computational cost reduction of over 50.5% in comparision with the standard block matching method.展开更多
Existing stereo matching methods cannot guarantee both the computational accuracy and efficiency for ihe disparity estimation of large-scale or multi-view images.Hybrid tree method can obtain a disparity estimation fa...Existing stereo matching methods cannot guarantee both the computational accuracy and efficiency for ihe disparity estimation of large-scale or multi-view images.Hybrid tree method can obtain a disparity estimation fast with relatively low accuracy,while PatchMatch can give high-precision disparity value with relatively high computational cost.In this work,we propose the Hybrid Tree Guided PatchMatch which can calculate the disparity fast and accurate.Firstly,an initial disparity map is estimated by employing hybrid tree cost aggregation,which is used to constrain the label searching range of the PatchMatch.Furthermore,a reliable normal searching range for each current normal vector defined on the initial disparity map is calculated to refine the PatchMatch.Finally,an effective quantizing acceleration strategy is designed to decrease the matching computational cost of continuous disparity.Experimental results demonstrate that the disparity estimation based on our algorithm is better in binocular image benchmarks such as Middlebury and KITTI.We also provide the disparity estimation results for multi-view stereo in real scenes.展开更多
Disparity is the geometrical difference between images of a stereoscopic pair. In this paper we give a comprehensive analysis of the statistical characteristics of disparity. Based on experiments, we discuss the rela...Disparity is the geometrical difference between images of a stereoscopic pair. In this paper we give a comprehensive analysis of the statistical characteristics of disparity. Based on experiments, we discuss the relations between disparity, depth and object relation between block size and disparity estimation, and the influence of error criteria on disparity estimation.展开更多
In this letter,capacity estimation for Mobile Ad hoc NETworks (MANETs) using direc- tional antennas are studied.Two Matrix-based Fast Calculation Algorithms (MFCAs) are proposed to estimate the network capacity in a n...In this letter,capacity estimation for Mobile Ad hoc NETworks (MANETs) using direc- tional antennas are studied.Two Matrix-based Fast Calculation Algorithms (MFCAs) are proposed to estimate the network capacity in a network scenario in which there is no channel sharing among multiple sessions and traffic is sensitive to delay with an end-to-end delay constraint.The first algo- rithm MFCA-1 is used to estimate network capacity in a situation where all links have the same delay. It estimates the maximum number of k-hop sessions in a network based on the k-hop adjacency matrix of the network.The second algorithm MFCA-2 is used to estimate network capacity in a situation where different links may have different delays.It calculates the maximum number of sessions in a network with an end-to-end delay constraint based on the adjacency matrix and the link-delay matrix of the network.Numerical and simulation results show that both MFCA-1 and MFCA-2 can calculate network capacity much faster than the well-known Brute-Force Search Algorithm (BFSA) but with the same accuracy.展开更多
The recent development of light field cameras has received growing interest, as their rich angular information has potential benefits for many computer vision tasks. In this paper, we introduce a novel method to obtai...The recent development of light field cameras has received growing interest, as their rich angular information has potential benefits for many computer vision tasks. In this paper, we introduce a novel method to obtain a dense disparity map by use of ground control points(GCPs) in the light field.Previous work optimizes the disparity map by local estimation which includes both reliable points and unreliable points. To reduce the negative effect of the unreliable points, we predict the disparity at non-GCPs from GCPs. Our method performs more robustly in shadow areas than previous methods based on GCP work, since we combine color information and local disparity. Experiments and comparisons on a public dataset demonstrate the effectiveness of our proposed method.展开更多
Balanced regional development is essential to China's economic stability and efficiency and achievement of the goal to build a moderately prosperous society in all respects. Based on the DMSP/OLS nighttime lights ...Balanced regional development is essential to China's economic stability and efficiency and achievement of the goal to build a moderately prosperous society in all respects. Based on the DMSP/OLS nighttime lights data of 291 cities at or above prefecture level during 1992-2013, this paper examines the regional disparities and trends of Chinese mainland's economic development. The findings are as follows:(1) During sample observation period, China's overall regional disparities generally declined despite some volatility; China's intra-regional disparities have been curbed yet a consistent framework for inter-regional economic coordination is lacking.(2) Southern coastal region contributes a significant share to China's overall regional disparities as the developed cities of Guangdong Province did not create a significant spatial spillover effect on neighboring regions.(3) According to the result of spatial Markov transition probability estimation, spatial factor has played a remarkable role in the evolution of China's regional economy and proximity to high-level regions will accelerate a region's transition toward higher levels.展开更多
In this study,an end-to-end deep learning method is proposed to improve the accuracy of continuum estimation in low-resolution gamma-ray spectra.A novel process for generating the theoretical continuum of a simulated ...In this study,an end-to-end deep learning method is proposed to improve the accuracy of continuum estimation in low-resolution gamma-ray spectra.A novel process for generating the theoretical continuum of a simulated spectrum is established,and a convolutional neural network consisting of 51 layers and more than 105 parameters is constructed to directly predict the entire continuum from the extracted global spectrum features.For testing,an in-house NaI-type whole-body counter is used,and 106 training spectrum samples(20%of which are reserved for testing)are generated using Monte Carlo simulations.In addition,the existing fitting,step-type,and peak erosion methods are selected for comparison.The proposed method exhibits excellent performance,as evidenced by its activity error distribution and the smallest mean activity error of 1.5%among the evaluated methods.Additionally,a validation experiment is performed using a whole-body counter to analyze a human physical phantom containing four radionuclides.The largest activity error of the proposed method is−5.1%,which is considerably smaller than those of the comparative methods,confirming the test results.The multiscale feature extraction and nonlinear relation modeling in the proposed method establish a novel approach for accurate and convenient continuum estimation in a low-resolution gamma-ray spectrum.Thus,the proposed method is promising for accurate quantitative radioactivity analysis in practical applications.展开更多
基金Supported by Sichuan Science and Technology Program(2023YFSY0026,2023YFH0004)Supported by the Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korean government(MSIT)(No.RS-2022-00155885,Artificial Intelligence Convergence Innovation Human Resources Development(Hanyang University ERICA)).
文摘Two-dimensional endoscopic images are susceptible to interferences such as specular reflections and monotonous texture illumination,hindering accurate three-dimensional lesion reconstruction by surgical robots.This study proposes a novel end-to-end disparity estimation model to address these challenges.Our approach combines a Pseudo-Siamese neural network architecture with pyramid dilated convolutions,integrating multi-scale image information to enhance robustness against lighting interferences.This study introduces a Pseudo-Siamese structure-based disparity regression model that simplifies left-right image comparison,improving accuracy and efficiency.The model was evaluated using a dataset of stereo endoscopic videos captured by the Da Vinci surgical robot,comprising simulated silicone heart sequences and real heart video data.Experimental results demonstrate significant improvement in the network’s resistance to lighting interference without substantially increasing parameters.Moreover,the model exhibited faster convergence during training,contributing to overall performance enhancement.This study advances endoscopic image processing accuracy and has potential implications for surgical robot applications in complex environments.
基金supported by the Opening Project of State Key Laboratory for Manufacturing Systems EngineeringFoundation for Youth Teacher of School of Mechanical Engineering, Xi’an Jiaotong University Brain Korea 21(BK21) Program of Ministry of Education and Human Resources Development
文摘An improved block matching approach to fast disparity estimation in machine vision applications is proposed, where the matching criterion is the sum of the absolute difference（SAD）.By evaluating the lower bounds, which become increasingly tighter for the matching criteria, the method tries to successively terminate unnecessary computations of the matching criteria between the reference block in one image and the ineligible candidate blocks in another image.It also eliminates the ineligible blocks as early as possible, while ensuring the optimal disparity of each pixel.Also, the proposed method can further speed up the elimination of ineligible candidate blocks by efficiently using the continuous constraint of disparity to predict the initial disparity of each pixel.The performance of the new algorithm is evaluated by carrying out a theoretical analysis, and by comparing its performance with the disparity estimation method based on the standard block matching.Simulated results demonstrate that the proposed algorithm achieves a computational cost reduction of over 50.5% in comparision with the standard block matching method.
文摘Existing stereo matching methods cannot guarantee both the computational accuracy and efficiency for ihe disparity estimation of large-scale or multi-view images.Hybrid tree method can obtain a disparity estimation fast with relatively low accuracy,while PatchMatch can give high-precision disparity value with relatively high computational cost.In this work,we propose the Hybrid Tree Guided PatchMatch which can calculate the disparity fast and accurate.Firstly,an initial disparity map is estimated by employing hybrid tree cost aggregation,which is used to constrain the label searching range of the PatchMatch.Furthermore,a reliable normal searching range for each current normal vector defined on the initial disparity map is calculated to refine the PatchMatch.Finally,an effective quantizing acceleration strategy is designed to decrease the matching computational cost of continuous disparity.Experimental results demonstrate that the disparity estimation based on our algorithm is better in binocular image benchmarks such as Middlebury and KITTI.We also provide the disparity estimation results for multi-view stereo in real scenes.
基金the National Natural Science Foundation of China(69972027)
文摘Disparity is the geometrical difference between images of a stereoscopic pair. In this paper we give a comprehensive analysis of the statistical characteristics of disparity. Based on experiments, we discuss the relations between disparity, depth and object relation between block size and disparity estimation, and the influence of error criteria on disparity estimation.
基金Supported by the National Natural Science Foundation of China (No.60402005).
文摘In this letter,capacity estimation for Mobile Ad hoc NETworks (MANETs) using direc- tional antennas are studied.Two Matrix-based Fast Calculation Algorithms (MFCAs) are proposed to estimate the network capacity in a network scenario in which there is no channel sharing among multiple sessions and traffic is sensitive to delay with an end-to-end delay constraint.The first algo- rithm MFCA-1 is used to estimate network capacity in a situation where all links have the same delay. It estimates the maximum number of k-hop sessions in a network based on the k-hop adjacency matrix of the network.The second algorithm MFCA-2 is used to estimate network capacity in a situation where different links may have different delays.It calculates the maximum number of sessions in a network with an end-to-end delay constraint based on the adjacency matrix and the link-delay matrix of the network.Numerical and simulation results show that both MFCA-1 and MFCA-2 can calculate network capacity much faster than the well-known Brute-Force Search Algorithm (BFSA) but with the same accuracy.
基金supported by National Natural Science Foundation of China (Nos. 61272287, 61531014)the State Key Laboratory of Virtual Reality Technology and Systems (No. BUAA-VR-15KF-10)
文摘The recent development of light field cameras has received growing interest, as their rich angular information has potential benefits for many computer vision tasks. In this paper, we introduce a novel method to obtain a dense disparity map by use of ground control points(GCPs) in the light field.Previous work optimizes the disparity map by local estimation which includes both reliable points and unreliable points. To reduce the negative effect of the unreliable points, we predict the disparity at non-GCPs from GCPs. Our method performs more robustly in shadow areas than previous methods based on GCP work, since we combine color information and local disparity. Experiments and comparisons on a public dataset demonstrate the effectiveness of our proposed method.
文摘Balanced regional development is essential to China's economic stability and efficiency and achievement of the goal to build a moderately prosperous society in all respects. Based on the DMSP/OLS nighttime lights data of 291 cities at or above prefecture level during 1992-2013, this paper examines the regional disparities and trends of Chinese mainland's economic development. The findings are as follows:(1) During sample observation period, China's overall regional disparities generally declined despite some volatility; China's intra-regional disparities have been curbed yet a consistent framework for inter-regional economic coordination is lacking.(2) Southern coastal region contributes a significant share to China's overall regional disparities as the developed cities of Guangdong Province did not create a significant spatial spillover effect on neighboring regions.(3) According to the result of spatial Markov transition probability estimation, spatial factor has played a remarkable role in the evolution of China's regional economy and proximity to high-level regions will accelerate a region's transition toward higher levels.
基金supported by the National Natural Science Foundation of China(No.12005198).
文摘In this study,an end-to-end deep learning method is proposed to improve the accuracy of continuum estimation in low-resolution gamma-ray spectra.A novel process for generating the theoretical continuum of a simulated spectrum is established,and a convolutional neural network consisting of 51 layers and more than 105 parameters is constructed to directly predict the entire continuum from the extracted global spectrum features.For testing,an in-house NaI-type whole-body counter is used,and 106 training spectrum samples(20%of which are reserved for testing)are generated using Monte Carlo simulations.In addition,the existing fitting,step-type,and peak erosion methods are selected for comparison.The proposed method exhibits excellent performance,as evidenced by its activity error distribution and the smallest mean activity error of 1.5%among the evaluated methods.Additionally,a validation experiment is performed using a whole-body counter to analyze a human physical phantom containing four radionuclides.The largest activity error of the proposed method is−5.1%,which is considerably smaller than those of the comparative methods,confirming the test results.The multiscale feature extraction and nonlinear relation modeling in the proposed method establish a novel approach for accurate and convenient continuum estimation in a low-resolution gamma-ray spectrum.Thus,the proposed method is promising for accurate quantitative radioactivity analysis in practical applications.
