A new algorithm is proposed for restoring disocclusion regions in depth-image-based rendering （DIBR） warped images. Current solutions include layered depth image （LDI）, pre-filtering methods, and post-processing m...A new algorithm is proposed for restoring disocclusion regions in depth-image-based rendering （DIBR） warped images. Current solutions include layered depth image （LDI）, pre-filtering methods, and post-processing methods. The LDI is complicated, and pre-filtering of depth images causes noticeable geometrical distortions in cases of large baseline warping. This paper presents a depth-aided inpainting method which inherits merits from Criminisi＇s inpainting algorithm. The proposed method features incorporation of a depth cue into texture estimation. The algorithm efficiently handles depth ambiguity by penalizing larger Lagrange multipliers of flling points closer to the warping position compared with the surrounding existing points. We perform morphological operations on depth images to accelerate the algorithm convergence, and adopt a luma-first strategy to adapt to various color sampling formats. Experiments on test multi-view sequence showed that our method has superiority in depth differentiation and geometrical loyalty in the restoration of warped images. Also, peak signal-to-noise ratio （PSNR） statistics on non-hole regions and whole image comparisons both compare favorably to those obtained by state of the art techniques.展开更多
Based on the recently proposed mirror-assisted multi-view digital image correlation(MV-DIC),we establish a cost-effective and easy-to-implement mirror-assisted multi-view high-speed digital image correlation(MVHS-DIC)...Based on the recently proposed mirror-assisted multi-view digital image correlation(MV-DIC),we establish a cost-effective and easy-to-implement mirror-assisted multi-view high-speed digital image correlation(MVHS-DIC)method and explore its applications for dual-surface full-field dynamic deformation measurement.In contrast to the general requirement of four expensive high-speed cameras for dual-surface dynamic deformation field measurement,the established mirror-assisted MVHS-DIC halves the cost by involving only two synchronized high-speed cameras and two planar mirrors.The two synchronized high-speed cameras can dynamically measure the front and rear surfaces of a sheet sample simultaneously through the reflection of the two mirrors.The results on the two surfaces are then transformed into the same coordinate system,leading to the required dual-surface 3D dynamical deformation fields.The effectiveness and accuracy of the established system are validated through modal tests of a cantilever aluminum sheet.The vibration measurement of a drum and dual-surface transient deformation measurement of a smartphone in the drop-collision process further prove its practicability.Benefiting from the attractive advantages of multi-view dynamic deformation measurement in a cost-efficient way,the established mirror-assisted MVHS-DIC is expected to encourage more comprehensive dynamic mechanical behavior characterization of regular-sized materials and structures in vibration and impact engineering fields.展开更多
The estimation of fish mass is one of the most basic and important tasks in aquaculture.Acquiring the mass of fish at different growth stages is of great significance for feeding,monitoring the health status of fish,a...The estimation of fish mass is one of the most basic and important tasks in aquaculture.Acquiring the mass of fish at different growth stages is of great significance for feeding,monitoring the health status of fish,and making breeding plans to increase production.The existing estimation methods for fish mass often stay in the 2D plane,and it is difficult to obtain the 3D information on fish,which will lead to the error.To solve this problem,a multi-view method was proposed to obtain the 3D information of fish and predict the mass of fish through a two-stage neural network with an edge-sensitive module.In the first stage,the side-and downward-view images of the fish and some 3D information,such as side area,top area,length,deflection angle,and pitch angle,were captured to estimate the size of the fish through two vertically placed cameras.Then the area of the fish at different views was estimated accurately through the pre-trained image segmentation neural network with an edgesensitive module.In the second stage,a fully connected neural network was constructed to regress the fish mass based on the 3D information obtained in the previous stage.The experimental results indicate that the proposed method can accurately estimate the fish mass and outperform the existing estimation methods.展开更多
基金Project supported by the National Natural Science Foundation of China (No 60802013)the Natural Science Foundation of Zhe-jiang Province, China (No Y106574)
文摘A new algorithm is proposed for restoring disocclusion regions in depth-image-based rendering （DIBR） warped images. Current solutions include layered depth image （LDI）, pre-filtering methods, and post-processing methods. The LDI is complicated, and pre-filtering of depth images causes noticeable geometrical distortions in cases of large baseline warping. This paper presents a depth-aided inpainting method which inherits merits from Criminisi＇s inpainting algorithm. The proposed method features incorporation of a depth cue into texture estimation. The algorithm efficiently handles depth ambiguity by penalizing larger Lagrange multipliers of flling points closer to the warping position compared with the surrounding existing points. We perform morphological operations on depth images to accelerate the algorithm convergence, and adopt a luma-first strategy to adapt to various color sampling formats. Experiments on test multi-view sequence showed that our method has superiority in depth differentiation and geometrical loyalty in the restoration of warped images. Also, peak signal-to-noise ratio （PSNR） statistics on non-hole regions and whole image comparisons both compare favorably to those obtained by state of the art techniques.
基金supported by the National Natural Science Foundation of China(Grant Nos.11925202 and 11872009)National Science and Technology Major Project(Grant No.J2019-V-0006-0099)。
文摘Based on the recently proposed mirror-assisted multi-view digital image correlation(MV-DIC),we establish a cost-effective and easy-to-implement mirror-assisted multi-view high-speed digital image correlation(MVHS-DIC)method and explore its applications for dual-surface full-field dynamic deformation measurement.In contrast to the general requirement of four expensive high-speed cameras for dual-surface dynamic deformation field measurement,the established mirror-assisted MVHS-DIC halves the cost by involving only two synchronized high-speed cameras and two planar mirrors.The two synchronized high-speed cameras can dynamically measure the front and rear surfaces of a sheet sample simultaneously through the reflection of the two mirrors.The results on the two surfaces are then transformed into the same coordinate system,leading to the required dual-surface 3D dynamical deformation fields.The effectiveness and accuracy of the established system are validated through modal tests of a cantilever aluminum sheet.The vibration measurement of a drum and dual-surface transient deformation measurement of a smartphone in the drop-collision process further prove its practicability.Benefiting from the attractive advantages of multi-view dynamic deformation measurement in a cost-efficient way,the established mirror-assisted MVHS-DIC is expected to encourage more comprehensive dynamic mechanical behavior characterization of regular-sized materials and structures in vibration and impact engineering fields.
基金funded by Guangdong Provincial Natural Science Foundation General Project(Grant No.2023A1515011700)GuangDong Basic and Applied Basic Research Foundation(Grant No.2022A1515110007)+1 种基金the Guangdong Provincial Natural Science Foundation General Project(Grant No.2023A1515012869)GDAS'Project of Science and Technology Development(Grant No.2022GDASZH-2022010108).
文摘The estimation of fish mass is one of the most basic and important tasks in aquaculture.Acquiring the mass of fish at different growth stages is of great significance for feeding,monitoring the health status of fish,and making breeding plans to increase production.The existing estimation methods for fish mass often stay in the 2D plane,and it is difficult to obtain the 3D information on fish,which will lead to the error.To solve this problem,a multi-view method was proposed to obtain the 3D information of fish and predict the mass of fish through a two-stage neural network with an edge-sensitive module.In the first stage,the side-and downward-view images of the fish and some 3D information,such as side area,top area,length,deflection angle,and pitch angle,were captured to estimate the size of the fish through two vertically placed cameras.Then the area of the fish at different views was estimated accurately through the pre-trained image segmentation neural network with an edgesensitive module.In the second stage,a fully connected neural network was constructed to regress the fish mass based on the 3D information obtained in the previous stage.The experimental results indicate that the proposed method can accurately estimate the fish mass and outperform the existing estimation methods.
