With the development of computer vision technology,panoramic image stitching has been widely used in fields such as scene reconstruction.A single traditional image cannot fully capture the panoramic view of the iconic...With the development of computer vision technology,panoramic image stitching has been widely used in fields such as scene reconstruction.A single traditional image cannot fully capture the panoramic view of the iconic East Gate of the South Campus of Shaanxi University of Technology.Therefore,this project aims to technically fuse multiple partial images into a complete panoramic image,enabling comprehensive recording and visual presentation of the architectural landscapes and spatial environments in this area.This report first introduces the technical background and application scenarios,clarifying the necessity of panoramic image stitching in campus landscape recording.It then elaborates on the core objectives and practical values,highlighting the role of technical solutions in improving image quality.Technically,a modular system design based on OpenCV is adopted,including modules such as image preprocessing,feature extraction and matching,image registration,fusion,and post-processing.Specifically,the SIFT algorithm is applied for feature extraction,KNN combined with ratio testing is used for feature matching,image registration is achieved by calculating the homography matrix,the fusion process utilizes multiband blending and Laplacian pyramid,and post-processing includes operations such as black area filling and CLAHE contrast enhancement.The experiment was conducted in a specific hardware and software environment using five overlapping images.After preprocessing,stitching,detail enhancement,and black edge repair,a panoramic image was successfully generated.The results show that the panoramic image fully presents the relevant scenery,with concealed seams,balanced exposure differences,and strong hierarchical details.This report provides a systematic description of the project’s technical implementation and achievement application.展开更多
为了提高图像拼接速度并满足高分辨率图像的实时拼接需求,提出了一种基于ORB(Oriented Fast and Rotated Brief)算法和MSAC(M-estimator Sample Consensus)算法的快速图像拼接方法。ORB算法特征匹配速度快,能够满足实时性要求。首先采用...为了提高图像拼接速度并满足高分辨率图像的实时拼接需求,提出了一种基于ORB(Oriented Fast and Rotated Brief)算法和MSAC(M-estimator Sample Consensus)算法的快速图像拼接方法。ORB算法特征匹配速度快,能够满足实时性要求。首先采用ORB算法进行图像特征点提取;然后,采用MSAC算法对匹配点对进行优化,剔除图像拼接中的伪匹配点对,通过正确的匹配点对求解图像变换矩阵;最后,采用双线性插值融合算法消除可见接缝并去除拼接痕迹。实验结果表明,本文方法在保证图像拼接质量的同时具有更快的拼接速度。展开更多
随着图像拼接技术在摄影、医疗影像及虚拟现实等领域的广泛应用,开发高效且精确的拼接算法具有重要实践意义。设计并实现了一种基于特征点匹配的图像拼接系统,利用Gamma校正方法对初始图像进行亮度预处理,以尺度不变特征变换(Scale-Inva...随着图像拼接技术在摄影、医疗影像及虚拟现实等领域的广泛应用,开发高效且精确的拼接算法具有重要实践意义。设计并实现了一种基于特征点匹配的图像拼接系统,利用Gamma校正方法对初始图像进行亮度预处理,以尺度不变特征变换(Scale-Invariont Feature Transfom,SIFT)算法为核心,通过OpenCV视觉库及Python编程语言完成图像特征点提取、匹配及图像拼接。设计了直观友好的用户界面(User Interface,UI)以支持用户操作并保存拼接结果。以结构相似性指数(Structural Similarity Index,SSIM)及峰值信噪比(Peak Signal to Noise Ratio,PSNR)评价指标,在不同光照及环境条件下对系统进行实验验证。结果表明,系统可高效、精准完成不同环境的图像拼接,增强后的拼接图像在PSNR及SSIM值上均表现出提升,验证了系统的实用性及可靠性。展开更多
文章提出了一种基于局部精细拼接算法(Local Fine Stitching Algorithm,LFSA)的无人机图像处理新框架,通过自适应网格化分区、自监督卷积神经网络(Convolutional Neural Network,CNN)匹配与最小二乘法优化,实现了对多视角影像的高精度...文章提出了一种基于局部精细拼接算法(Local Fine Stitching Algorithm,LFSA)的无人机图像处理新框架,通过自适应网格化分区、自监督卷积神经网络(Convolutional Neural Network,CNN)匹配与最小二乘法优化,实现了对多视角影像的高精度局部校正;随后结合图割算法(Graph-cut)无缝融合与多项式色彩校正,有效抑制了光照与几何畸变对拼接质量的影响。该框架在高分辨率正射影像重建、多源异构数据融合与像素级语义分割,以及计算流体力学(Computational Fluid Dynamics,CFD)与有限元分析(Finite Element Analysis,FEA)辅助的动态优化过程中,均表现出兼顾精度与实时性的良好性能,为复杂地形下的实时无人机航测与应用场景提供了可扩展且稳定可靠的技术支撑。展开更多
文摘With the development of computer vision technology,panoramic image stitching has been widely used in fields such as scene reconstruction.A single traditional image cannot fully capture the panoramic view of the iconic East Gate of the South Campus of Shaanxi University of Technology.Therefore,this project aims to technically fuse multiple partial images into a complete panoramic image,enabling comprehensive recording and visual presentation of the architectural landscapes and spatial environments in this area.This report first introduces the technical background and application scenarios,clarifying the necessity of panoramic image stitching in campus landscape recording.It then elaborates on the core objectives and practical values,highlighting the role of technical solutions in improving image quality.Technically,a modular system design based on OpenCV is adopted,including modules such as image preprocessing,feature extraction and matching,image registration,fusion,and post-processing.Specifically,the SIFT algorithm is applied for feature extraction,KNN combined with ratio testing is used for feature matching,image registration is achieved by calculating the homography matrix,the fusion process utilizes multiband blending and Laplacian pyramid,and post-processing includes operations such as black area filling and CLAHE contrast enhancement.The experiment was conducted in a specific hardware and software environment using five overlapping images.After preprocessing,stitching,detail enhancement,and black edge repair,a panoramic image was successfully generated.The results show that the panoramic image fully presents the relevant scenery,with concealed seams,balanced exposure differences,and strong hierarchical details.This report provides a systematic description of the project’s technical implementation and achievement application.
文摘为了提高图像拼接速度并满足高分辨率图像的实时拼接需求,提出了一种基于ORB(Oriented Fast and Rotated Brief)算法和MSAC(M-estimator Sample Consensus)算法的快速图像拼接方法。ORB算法特征匹配速度快,能够满足实时性要求。首先采用ORB算法进行图像特征点提取;然后,采用MSAC算法对匹配点对进行优化,剔除图像拼接中的伪匹配点对,通过正确的匹配点对求解图像变换矩阵;最后,采用双线性插值融合算法消除可见接缝并去除拼接痕迹。实验结果表明,本文方法在保证图像拼接质量的同时具有更快的拼接速度。
文摘针对非物质文化遗产蓝印花布数字化生成技术发展较慢的问题,提出了一种基于图像拼接技术的蓝印花布边缘纹样快速生成算法,实现了对边缘纹样的拼接延展.对于蓝印花布样本存在颜色和噪点问题,提出了一种预处理算法,可统一待拼接图像样本的颜色并消除噪点.在拼接算法设计中,通过对特征提取、匹配、提纯及融合等关键环节的算法进行对比实验,系统性优化各环节的算法组合,形成高效的拼接算法架构.实验结果表明,该算法可以实现蓝印花布边缘纹样的快速拼接;采用基于FAST算法的纹样特征点的检测时间比SIFT(Scale-Invariant Feature Transform)和SURF(Speeded Up Robust Features)算法时间分别减少了74.6%和89.8%;采用基于BF算法的纹样特征点的平均匹配时间比FLANN(Fast Library for Approximate Nearest Neighbors)算法时间减少了88.6%;采用基于PROSAC算法的纹样匹配特征点的提纯时间平均比RANSAC(Random Sample Consensus)算法时间减少了20%;总体拼接时间平均比传统算法时间减少了1.0718 s.
文摘随着图像拼接技术在摄影、医疗影像及虚拟现实等领域的广泛应用,开发高效且精确的拼接算法具有重要实践意义。设计并实现了一种基于特征点匹配的图像拼接系统,利用Gamma校正方法对初始图像进行亮度预处理,以尺度不变特征变换(Scale-Invariont Feature Transfom,SIFT)算法为核心,通过OpenCV视觉库及Python编程语言完成图像特征点提取、匹配及图像拼接。设计了直观友好的用户界面(User Interface,UI)以支持用户操作并保存拼接结果。以结构相似性指数(Structural Similarity Index,SSIM)及峰值信噪比(Peak Signal to Noise Ratio,PSNR)评价指标,在不同光照及环境条件下对系统进行实验验证。结果表明,系统可高效、精准完成不同环境的图像拼接,增强后的拼接图像在PSNR及SSIM值上均表现出提升,验证了系统的实用性及可靠性。
文摘文章提出了一种基于局部精细拼接算法(Local Fine Stitching Algorithm,LFSA)的无人机图像处理新框架,通过自适应网格化分区、自监督卷积神经网络(Convolutional Neural Network,CNN)匹配与最小二乘法优化,实现了对多视角影像的高精度局部校正;随后结合图割算法(Graph-cut)无缝融合与多项式色彩校正,有效抑制了光照与几何畸变对拼接质量的影响。该框架在高分辨率正射影像重建、多源异构数据融合与像素级语义分割,以及计算流体力学(Computational Fluid Dynamics,CFD)与有限元分析(Finite Element Analysis,FEA)辅助的动态优化过程中,均表现出兼顾精度与实时性的良好性能,为复杂地形下的实时无人机航测与应用场景提供了可扩展且稳定可靠的技术支撑。
