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
Image/video stitching is a technology for solving the field of view(FOV)limitation of images/videos.It stitches multiple overlapping images/videos to generate a wide-FOV image/video,and has been used in various fields...Image/video stitching is a technology for solving the field of view(FOV)limitation of images/videos.It stitches multiple overlapping images/videos to generate a wide-FOV image/video,and has been used in various fields such as sports broadcasting,video surveillance,street view,and entertainment.This survey reviews image/video stitching algorithms,with a particular focus on those developed in recent years.Image stitching first calculates the corresponding relationships between multiple overlapping images,deforms and aligns the matched images,and then blends the aligned images to generate a wide-FOV image.A seamless method is always adopted to eliminate such potential flaws as ghosting and blurring caused by parallax or objects moving across the overlapping regions.Video stitching is the further extension of image stitching.It usually stitches selected frames of original videos to generate a stitching template by performing image stitching algorithms,and the subsequent frames can then be stitched according to the template.Video stitching is more complicated with moving objects or violent camera movement,because these factors introduce jitter,shakiness,ghosting,and blurring.Foreground detection technique is usually combined into stitching to eliminate ghosting and blurring,while video stabilization algorithms are adopted to solve the jitter and shakiness.This paper further discusses panoramic stitching as a special-extension of image/video stitching.Panoramic stitching is currently the most widely used application in stitching.This survey reviews the latest image/video stitching methods,and introduces the fundamental principles/advantages/weaknesses of image/video stitching algorithms.Image/video stitching faces long-term challenges such as wide baseline,large parallax,and low-texture problem in the overlapping region.New technologies may present new opportunities to address these issues,such as deep learning-based semantic correspondence,and 3D image stitching.Finally,this survey discusses the challenges of image/video stitching and proposes potential solutions.展开更多
This paper designs and implements a single-camera 360°panoramic imaging system based on motor-driven fisheye rotation.The system utilizes a stepper motor for precise angular control,enabling the camera to rotate ...This paper designs and implements a single-camera 360°panoramic imaging system based on motor-driven fisheye rotation.The system utilizes a stepper motor for precise angular control,enabling the camera to rotate around its optical center to capture multi-view images,thereby avoiding the parallax and geometric mismatch problems inherent in traditional multi-camera configurations.To address the strong distortion characteristics of fisheye images,an equidistant projection model is adopted for distortion correction.On this basis,a brightness normalization method combining global linear brightness correction and local illumination compensation is proposed to enhance stitching consistency.By establishing a geometry model constrained by camera rotation and integrating cylindrical projection with cosine-weighted blending,the system achieves high-precision panoramic stitching and seamless visual transitions.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.
基金the National Natural Science Foundation of China(61872023).
文摘Image/video stitching is a technology for solving the field of view(FOV)limitation of images/videos.It stitches multiple overlapping images/videos to generate a wide-FOV image/video,and has been used in various fields such as sports broadcasting,video surveillance,street view,and entertainment.This survey reviews image/video stitching algorithms,with a particular focus on those developed in recent years.Image stitching first calculates the corresponding relationships between multiple overlapping images,deforms and aligns the matched images,and then blends the aligned images to generate a wide-FOV image.A seamless method is always adopted to eliminate such potential flaws as ghosting and blurring caused by parallax or objects moving across the overlapping regions.Video stitching is the further extension of image stitching.It usually stitches selected frames of original videos to generate a stitching template by performing image stitching algorithms,and the subsequent frames can then be stitched according to the template.Video stitching is more complicated with moving objects or violent camera movement,because these factors introduce jitter,shakiness,ghosting,and blurring.Foreground detection technique is usually combined into stitching to eliminate ghosting and blurring,while video stabilization algorithms are adopted to solve the jitter and shakiness.This paper further discusses panoramic stitching as a special-extension of image/video stitching.Panoramic stitching is currently the most widely used application in stitching.This survey reviews the latest image/video stitching methods,and introduces the fundamental principles/advantages/weaknesses of image/video stitching algorithms.Image/video stitching faces long-term challenges such as wide baseline,large parallax,and low-texture problem in the overlapping region.New technologies may present new opportunities to address these issues,such as deep learning-based semantic correspondence,and 3D image stitching.Finally,this survey discusses the challenges of image/video stitching and proposes potential solutions.
基金Graduate Innovation Ability Training Program of the Hebei Provincial Department of Education,2025(Project No.:CXZZSS2025095)。
文摘This paper designs and implements a single-camera 360°panoramic imaging system based on motor-driven fisheye rotation.The system utilizes a stepper motor for precise angular control,enabling the camera to rotate around its optical center to capture multi-view images,thereby avoiding the parallax and geometric mismatch problems inherent in traditional multi-camera configurations.To address the strong distortion characteristics of fisheye images,an equidistant projection model is adopted for distortion correction.On this basis,a brightness normalization method combining global linear brightness correction and local illumination compensation is proposed to enhance stitching consistency.By establishing a geometry model constrained by camera rotation and integrating cylindrical projection with cosine-weighted blending,the system achieves high-precision panoramic stitching and seamless visual transitions.
