Traditional feature-based image stitching techniques often encounter obstacles when dealing with images lackingunique attributes or suffering from quality degradation. The scarcity of annotated datasets in real-life s...Traditional feature-based image stitching techniques often encounter obstacles when dealing with images lackingunique attributes or suffering from quality degradation. The scarcity of annotated datasets in real-life scenesseverely undermines the reliability of supervised learning methods in image stitching. Furthermore, existing deeplearning architectures designed for image stitching are often too bulky to be deployed on mobile and peripheralcomputing devices. To address these challenges, this study proposes a novel unsupervised image stitching methodbased on the YOLOv8 (You Only Look Once version 8) framework that introduces deep homography networksand attentionmechanisms. Themethodology is partitioned into three distinct stages. The initial stage combines theattention mechanism with a pooling pyramid model to enhance the detection and recognition of compact objectsin images, the task of the deep homography networks module is to estimate the global homography of the inputimages consideringmultiple viewpoints. The second stage involves preliminary stitching of the masks generated inthe initial stage and further enhancement through weighted computation to eliminate common stitching artifacts.The final stage is characterized by adaptive reconstruction and careful refinement of the initial stitching results.Comprehensive experiments acrossmultiple datasets are executed tometiculously assess the proposed model. Ourmethod’s Peak Signal-to-Noise Ratio (PSNR) and Structure Similarity Index Measure (SSIM) improved by 10.6%and 6%. These experimental results confirm the efficacy and utility of the presented model in this paper.展开更多
Stitch density is one of the critical quality parameters of knit fabrics. This parameter is closely related to other physical quality parameters like fabric weight, fabric tightness factor, fiber types, blend ratio, y...Stitch density is one of the critical quality parameters of knit fabrics. This parameter is closely related to other physical quality parameters like fabric weight, fabric tightness factor, fiber types, blend ratio, yarn diameter and linear density, and fabric structure. Selecting stitch density (wales per inch, course per inch) is essential to getting the appropriate fabric weight and desired quality. Usually, no rules or assumptions exist to get the desired stitch density in the finished fabric stage. Fifteen types of blended knit fabrics were prepared to conduct the study. The varying percentages of cotton, polyester, and elastane are incorporated in the blends. Regression analysis and regression ANOVA tests were done to predict the stitch density of finished fabrics. A suitable regression equation is established to get the desired results. The study also found that the stitch density value in the finished stage fabric decreases by approximately 15% compared to the stitch density in the grey fabric stage. This study will help the fabric manufacturers get the finished fabric stitch density in advance by utilizing the grey fabric stitch density data set. The author expects this research to benefit the knitting and dyeing industry, new researchers, and advanced researchers.展开更多
Branch identification technology is a key technology to achieve automated pruning of fruit tree branches, and one of its technical bottlenecks lies in the stitching of branch images. To this end, we propose a set of b...Branch identification technology is a key technology to achieve automated pruning of fruit tree branches, and one of its technical bottlenecks lies in the stitching of branch images. To this end, we propose a set of branch image stitching technology algorithms. The algorithm is based on the grey-scale prime centroid method to determine the detection feature points, and uses the coordinate transformation matrix H of the corresponding points of the image to carry out the image geometric transformation, and realises the feature matching through sample comparison and classification methods. The experimental results show that the matched point images are more correct and less time-consuming.展开更多
二维数字图像相关(two-dimensional digital image correlation,2D-DIC)在测量过程中不可避免地会出现相机光轴与测量表面非垂直,由此产生的离面位移而将导致较大的测量误差,同时在视场受限的环境中难以通过单台相机完成大范围的变形测...二维数字图像相关(two-dimensional digital image correlation,2D-DIC)在测量过程中不可避免地会出现相机光轴与测量表面非垂直,由此产生的离面位移而将导致较大的测量误差,同时在视场受限的环境中难以通过单台相机完成大范围的变形测量。有鉴于此,该文开发了基于双反射镜的2D-DIC变形测量系统,使用双反射镜成像缓解离面运动对2D-DIC的影响,通过可移动相机实现小视场下的图像采集,提出基于频域移位的高精度图像拼接方法,并改进了融合函数,最终获得试样的高分辨率图像。单轴拉伸实验结果表明,轴向应变的平均相对误差相比传统2D-DIC方法降低12.82%,测量分辨率提高约34.92%,验证了测量系统的可行性和有效性。展开更多
基金Science and Technology Research Project of the Henan Province(222102240014).
文摘Traditional feature-based image stitching techniques often encounter obstacles when dealing with images lackingunique attributes or suffering from quality degradation. The scarcity of annotated datasets in real-life scenesseverely undermines the reliability of supervised learning methods in image stitching. Furthermore, existing deeplearning architectures designed for image stitching are often too bulky to be deployed on mobile and peripheralcomputing devices. To address these challenges, this study proposes a novel unsupervised image stitching methodbased on the YOLOv8 (You Only Look Once version 8) framework that introduces deep homography networksand attentionmechanisms. Themethodology is partitioned into three distinct stages. The initial stage combines theattention mechanism with a pooling pyramid model to enhance the detection and recognition of compact objectsin images, the task of the deep homography networks module is to estimate the global homography of the inputimages consideringmultiple viewpoints. The second stage involves preliminary stitching of the masks generated inthe initial stage and further enhancement through weighted computation to eliminate common stitching artifacts.The final stage is characterized by adaptive reconstruction and careful refinement of the initial stitching results.Comprehensive experiments acrossmultiple datasets are executed tometiculously assess the proposed model. Ourmethod’s Peak Signal-to-Noise Ratio (PSNR) and Structure Similarity Index Measure (SSIM) improved by 10.6%and 6%. These experimental results confirm the efficacy and utility of the presented model in this paper.
文摘Stitch density is one of the critical quality parameters of knit fabrics. This parameter is closely related to other physical quality parameters like fabric weight, fabric tightness factor, fiber types, blend ratio, yarn diameter and linear density, and fabric structure. Selecting stitch density (wales per inch, course per inch) is essential to getting the appropriate fabric weight and desired quality. Usually, no rules or assumptions exist to get the desired stitch density in the finished fabric stage. Fifteen types of blended knit fabrics were prepared to conduct the study. The varying percentages of cotton, polyester, and elastane are incorporated in the blends. Regression analysis and regression ANOVA tests were done to predict the stitch density of finished fabrics. A suitable regression equation is established to get the desired results. The study also found that the stitch density value in the finished stage fabric decreases by approximately 15% compared to the stitch density in the grey fabric stage. This study will help the fabric manufacturers get the finished fabric stitch density in advance by utilizing the grey fabric stitch density data set. The author expects this research to benefit the knitting and dyeing industry, new researchers, and advanced researchers.
文摘Branch identification technology is a key technology to achieve automated pruning of fruit tree branches, and one of its technical bottlenecks lies in the stitching of branch images. To this end, we propose a set of branch image stitching technology algorithms. The algorithm is based on the grey-scale prime centroid method to determine the detection feature points, and uses the coordinate transformation matrix H of the corresponding points of the image to carry out the image geometric transformation, and realises the feature matching through sample comparison and classification methods. The experimental results show that the matched point images are more correct and less time-consuming.
文摘二维数字图像相关(two-dimensional digital image correlation,2D-DIC)在测量过程中不可避免地会出现相机光轴与测量表面非垂直,由此产生的离面位移而将导致较大的测量误差,同时在视场受限的环境中难以通过单台相机完成大范围的变形测量。有鉴于此,该文开发了基于双反射镜的2D-DIC变形测量系统,使用双反射镜成像缓解离面运动对2D-DIC的影响,通过可移动相机实现小视场下的图像采集,提出基于频域移位的高精度图像拼接方法,并改进了融合函数,最终获得试样的高分辨率图像。单轴拉伸实验结果表明,轴向应变的平均相对误差相比传统2D-DIC方法降低12.82%,测量分辨率提高约34.92%,验证了测量系统的可行性和有效性。
