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Extraction of Acoustic Normal Mode Depth Functions Using Range-Difference Method with Vertical Linear Array Data
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作者 GAO Siyu LI Weilu +2 位作者 ZHANG Yinquan LI Xiaolei WANG Ning 《Journal of Ocean University of China》 SCIE CAS CSCD 2024年第4期871-882,共12页
Data-derived normal mode extraction is an effective method for extracting normal mode depth functions in the absence of marine environmental data.However,when the corresponding singular vectors become nonunique when t... Data-derived normal mode extraction is an effective method for extracting normal mode depth functions in the absence of marine environmental data.However,when the corresponding singular vectors become nonunique when two or more singular values obtained from the cross-spectral density matrix diagonalization are nearly equal,this results in unsatisfactory extraction outcomes for the normal mode depth functions.To address this issue,we introduced in this paper a range-difference singular value decomposition method for the extraction of normal mode depth functions.We performed the mode extraction by conducting singular value decomposition on the individual frequency components of the signal's cross-spectral density matrix.This was achieved by using pressure and its range-difference matrices constructed from vertical line array data.The proposed method was validated using simulated data.In addition,modes were successfully extracted from ambient noise. 展开更多
关键词 range difference depth function extraction normal mode
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DGConv: A Novel Convolutional Neural Network Approach for Weld Seam Depth Image Detection
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作者 Pengchao Li Fang Xu +3 位作者 Jintao Wang Haibing Guo Mingmin Liu Zhenjun Du 《Computers, Materials & Continua》 SCIE EI 2024年第2期1755-1771,共17页
We propose a novel image segmentation algorithm to tackle the challenge of limited recognition and segmentation performance in identifying welding seam images during robotic intelligent operations.Initially,to enhance... We propose a novel image segmentation algorithm to tackle the challenge of limited recognition and segmentation performance in identifying welding seam images during robotic intelligent operations.Initially,to enhance the capability of deep neural networks in extracting geometric attributes from depth images,we developed a novel deep geometric convolution operator(DGConv).DGConv is utilized to construct a deep local geometric feature extraction module,facilitating a more comprehensive exploration of the intrinsic geometric information within depth images.Secondly,we integrate the newly proposed deep geometric feature module with the Fully Convolutional Network(FCN8)to establish a high-performance deep neural network algorithm tailored for depth image segmentation.Concurrently,we enhance the FCN8 detection head by separating the segmentation and classification processes.This enhancement significantly boosts the network’s overall detection capability.Thirdly,for a comprehensive assessment of our proposed algorithm and its applicability in real-world industrial settings,we curated a line-scan image dataset featuring weld seams.This dataset,named the Standardized Linear Depth Profile(SLDP)dataset,was collected from actual industrial sites where autonomous robots are in operation.Ultimately,we conducted experiments utilizing the SLDP dataset,achieving an average accuracy of 92.7%.Our proposed approach exhibited a remarkable performance improvement over the prior method on the identical dataset.Moreover,we have successfully deployed the proposed algorithm in genuine industrial environments,fulfilling the prerequisites of unmanned robot operations. 展开更多
关键词 Weld image detection deep learning semantic segmentation depth map geometric feature extraction
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An effective graph and depth layer based RGB-D image foreground object extraction method
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作者 Zhiguang Xiao Hui Chen +1 位作者 Changhe Tu Reinhard Klette 《Computational Visual Media》 CSCD 2017年第4期387-393,共7页
We consider the extraction of accurate silhouettes of foreground objects in combined color image and depth map data.This is of relevance for applications such as altering the contents of a scene,or changing the depths... We consider the extraction of accurate silhouettes of foreground objects in combined color image and depth map data.This is of relevance for applications such as altering the contents of a scene,or changing the depths of contents for display purposes in 3DTV,object detection,or scene understanding.To 展开更多
关键词 RGB An effective graph and depth layer based RGB-D image foreground object extraction method
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Depth extraction method with high accuracy in integral imaging based on moving array lenslet technique
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作者 王尧尧 张娟 +3 位作者 赵雪微 宋丽培 张勃 赵星 《Optoelectronics Letters》 EI 2018年第2期148-151,共4页
In order to improve depth extraction accuracy, a method using moving array lenslet technique(MALT) in pickup stage is proposed, which can decrease the depth interval caused by pixelation. In this method, the lenslet a... In order to improve depth extraction accuracy, a method using moving array lenslet technique(MALT) in pickup stage is proposed, which can decrease the depth interval caused by pixelation. In this method, the lenslet array is moved along the horizontal and vertical directions simultaneously for N times in a pitch to get N sets of elemental images. Computational integral imaging reconstruction method for MALT is taken to obtain the slice images of the 3 D scene, and the sum modulus(SMD) blur metric is taken on these slice images to achieve the depth information of the 3 D scene. Simulation and optical experiments are carried out to verify the feasibility of this method. 展开更多
关键词 depth extraction method with high accuracy in integral imaging based on moving array lenslet technique
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