Seamless Panoramic Image Research Articles

Seamless Panoramic Image Stitching Based onInvariant Feature Detector and Image Blending

Seamless Panoramic Image Stitching Based onInvariant Feature Detector and Image Blending

Advancing the Robotic Vision Revolution: Development and Evaluation of a Bionic Binocular System for Enhanced Robotic Vision.

This paper describes a novel bionic eye binocular vision system designed to mimic the natural movements of the human eye. The system provides a broader field of view and enhances visual perception in complex environments. Compared with similar bionic binocular cameras, the JEWXON BC200 bionic binocular camera developed in this study is more miniature. It consumes only 2.8 W of power, which makes it ideal for mobile robots. Combining axis and camera rotation enables more seamless panoramic image synthesis and is therefore suitable for self-rotating bionic binocular cameras. In addition, combined with the YOLO-V8 model, the camera can accurately recognize objects such as clocks and keyboards. This research provides new ideas for the development of robotic vision systems.

A method for stitching remote sensing images with Delaunay triangle feature constraints

The process of synthesizing multiple images into a seamless panoramic image is referred to as remote sensing image stitching. Existing studies focus less on the influence of topography on the appearance and texture of images and the perturbation of image spectra by topographic changes. This paper presents a remote sensing image stitching method that considers the impact of topography and geomorphology. First, the feature matching was optimized using the Euclidean distance similarity of texture features and the nearest neighbor distance ratio of feature points in remote sensing images as constraints. Then, the Delaunay triangle mesh of feature points in the image overlapping region was constructed, the geometric features of Delaunay triangles were used to optimize the triangle matching and reduce the matching redundancy, and the affine transformation matrix was solved based on the comprehensive consideration of the geometric features of Delaunay triangles and the texture features of the remote sensing images. Finally, the weighted fusion algorithm was applied to stitch and fuse the images. Three image datasets were selected for the experiments, one in which there were large terrain undulations in the imaging regions, one in which the main body of the imaging regions was water, and one in which the overall terrain of the imaging regions had relatively gentle slopes but obscuring features were also present. The results showed that the average correct rates of method feature matching were 89.78%, 94.99%, and 96.17%, which were the best for each algorithm, and the average feature matching times were 4.13, 8.27 and 7.19 s. These times are much lower than those obtained with the APAP and AANAP algorithms and basically the same as those achieved with the SPHP and SURF algorithms. In terms of visual effect, the AG, and indices of the proposed method were all significantly improved compared to the APAP, SPHP, AANAP, and SURF algorithms, the advantages were most obvious when dealing with datasets with large topographic relief in the imaging regions, with the maximum improvement of the AG, and indices were 85.61%, 95.68%, and 93.12%, respectively. Therefore, it is possible to conclude that the proposed method is more suitable for remote sensing image stitching and fusion of different topographic and geomorphological conditions.

Image Mosaicing

Image mosaicing is a technique for combining many images of the same scene to generate a larger, seamless panoramic image. Image mosaicing can be traced back to the days of manually piecing images. Image-mosaicing techniques have been created to automate the process since the introduction of computer technology. The method consists of five phases, including feature matching and estimation of the homography matrix. Image mosaicing is commonly used in virtual tourism, video games, and photo editing. Despite the fact that the image panoramic feature is accessible in digital cameras, there is still potential for improvement, and more research in this sector is required.

A view-free image stitching network based on global homography

Image stitching is a traditional but challenging computer vision task, aiming to obtain a seamless panoramic image. Recently, researchers begin to study the image stitching task using deep learning. However, the existing learning methods assume a relatively fixed view during the image capturing, thus show a poor generalization ability to flexible view cases. To address the above problem, we present a cascaded view-free image stitching network based on a global homography. This novel image stitching network does not have any restriction on the view of images and it can be implemented in three stages. In particular, we first estimate a global homography between two input images from different views. And then we propose a structure stitching layer to obtain the coarse stitching result using the global homography. In the last stage, we design a content revision network to eliminate ghosting effects and refine the content of the stitching result. To enable efficient learning on various views, we also present a method to generate synthetic datasets for network training. Experimental results demonstrate that our method can achieve almost 100% elimination of artifacts in overlapping areas at the cost of acceptable slight distortions in non-overlapping areas, compared with traditional methods. In addition, the proposed method is view-free and more robust especially in a scene where feature points are difficult to detect.

Drone Image Stitching Using Local Mesh-Based Bundle Adjustment and Shape-Preserving Transform

This article proposes a strategy for drone image stitching using local mesh-based bundle adjustment and shape-preserving transform, which aims to effectively stitch multiple overlapping drone images into a natural panoramic image. Existing traditional methods using a simple homography cannot handle the situation that the input drone images have parallax effect, and the image mosaic result always suffers from artifacts. In order to achieve natural-looking stitching results without the above limitation, we divide the proposed method into the following steps. Starting from initial feature sets obtained by off-the-shelf feature extraction methods, we incorporate the parallax errors into an energy minimum framework and construct a robust alignment energy. This energy can be minimized efficiently based on local bundle adjustment and robust <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3\sigma $ </tex-math></inline-formula> principle, which could eliminate parallax effects and achieve accurate alignment. Then the seamless panoramic image is obtained by warping the target image and the source images onto the mesh plane directly. An image patch can be transformed by projective transformation (e.g., homography), which provides good alignment but may cause distortions. Consequently, combined with mesh-based shape-preserving transform, our proposed strategy can improve the naturalness of the results flexibly. Experiments show that our stitching strategy can eliminate parallax effects more effectively and achieve natural-looking results compared to other state-of-the-art methods.

Panoramic Image Generation Using Centerline- Constrained Mesh Parameterization for Arbitrarily Shaped Tunnel Lining

Three-dimensional (3D) triangular meshes has been widely used in tunnel engineering. This paper proposes an algorithm for unwrapping 3D tunnel lining meshes into straight two-dimensional (2D) meshes to generate a 2D seamless panoramic image of the tunnel lining. The proposed algorithm is divided into three steps. In the first step, the centerline is extracted and the L1-median is used to represent the centerline of the tunnel; in the second step, a centerline constraint, which is linear to ensure that the equations to be solved are also linear, is added to least squares conformal mapping (LSCM) to unwrap the tunnel mesh; and in the third step, a panoramic image of the tunnel lining is obtained through texture projection or image correction. Adding a centerline constraint allows the panoramic image to retain mileage information and global shape. The strict geometric relationship between the 3D mesh and panorama guarantees the panorama's accuracy and realizes the two-way mapping of 2D and 3D data analyses. Moreover, a projection strategy is proposed to efficiently process the multiple tiles and non-manifold tunnel meshes which cannot be unwrapped directly using mesh parameterization algorithms. The experimental results show that centerline-constrained LSCM (CLSCM), which is suitable for any arbitrarily shaped tunnel lining, can preserve the global shape and local details better than the conventional method and can be extended to unwrap any strip-shaped structured meshes.

Panoramic Image System Design in the Nearly Dark Environment

Aiming at the need of borehole and ruins exploration in the nearly dark environment, a Panoramic Image System is proposed in this paper. consisting of a reciprocating motor, a steering gear with complete rotation, a miniature camera, a depth transducer and radio frequency wireless module. The camera was pushed to the specified location of the pipe by the motor and then the steering gear was rotated for panorama image acquisition. In order to achieve a seamless panoramic image without distortion, a fusion algorithm based on Principal Component Analysis (PCA) and an image mosaic algorithm for image edge extracting based on Canny operator were proposed. The imaging system has good usability and applicability.

Quantitative quality assessment of stitched panoramic images

In this study, the authors present quantitative quality assessment indices for measuring geometric and photometric qualities of stitched panoramic images, since both geometric and photometric qualities are important for obtaining a seamless panoramic image. For geometric quality assessment, our approach is based on the use of structural similarity (SSIM) index between the high-frequency information (HFI) of both geometrically corrected unstitched images and the stitched panoramic image. This measure is called ‘HFI_SSIM’. The complementary low-frequency information from the same pair of images is used for assessing the photometric quality of the stitched image using the spectral angle mapper and intensity magnitude ratio measures. The proposed quality metrics are tested on both synthetic and true datasets comprising of indoor and outdoor scenes. The results obtained, highlight the interest of using the proposed quantitative measures for assessing the quality of stitched panoramic images.

Rover Localization and Landing-Site Mapping Technology for the 2003 Mars Exploration Rover Mission

SUMMARY The above introduced the technology and experiments for rover localization and landing sitemapping in the 2003 MER mission. At first, Mars global and landing site local reference systemsare elucidated. The initial rover position will be obtained through a triangulation usingobservations on orbital images and the very first set of surface images if common landmarks canbe found in these images. This location can then be improved and verified by UHF two-wayDoppler tracking and VLBI technology. As more ground images are acquired, landmarks seen inboth ground images and orbital images can be used to update landing site locations in the globalMars body-fixed reference system. Onboard rover localization techniques will perform roverlocalization tasks in real-time. The application of visual odometry will improve localization byovercoming problems associated with wheel odometry such as slippage and low accuracy.Finally, the bundle adjustment based rover localization method will build an image networkacquired by Pancam, Navcam and Hazcam cameras, as well as orbital images (such as Viking,MOC NA, and THEMIS images). The developed incremental and integrated bundle adjustmentmodels will supply improved rover locations and image orientation parameters, which are criticalfor generation of high quality landing site topographic mapping products. Based on the field testsperformed on Earth and Mars (MPF mission data), we expect that a 1 percent or better roverlocalization accuracy can be achieved during this mission. In addition, the bundle adjustmentresults will also enable us to produce high precision landing site topographic mapping productsincluding seamless panoramic image mosaics, DTM, and orthophotos.