Application of Image Mosaic Technology in Tai Chi Animation Creation

Panorama can reflect the image seen at any angle of view at a certain point of view. How to improve the quality of panorama stitching and use it as a data foundation in the “smart tourism” system has become a research hotspot in recent years. Image stitching means to use the overlapping area between the images to be stitched for registration and fusion to generate a new image with a wider viewing angle. This article takes the production of “Tai Chi” animation as an example to apply image stitching technology to the production of realistic 3D model textures to simplify the production of animation textures. A handheld camera is used to collect images in a certain overlapping area. After cylindrical projection, the Harris algorithm based on scale space is adopted to detect image feature points, the two-way normalized cross-correlation algorithm matches the feature points, and the algorithm to extract the threshold T iteratively removes mismatches. The transformation parameter model is quickly estimated through the improved RANSAC algorithm, and the spliced image is projected and transformed. The Szeliski grayscale fusion method directly calculates the grayscale average of the matching points to fuse the image, and finally, the best stitching method is used to eliminate the ghosting at the image mosaic. Data experiments based on Matlab show that the proposed image splicing technology has the advantages of high efficiency and clear spliced images and a more satisfactory panoramic image visual effect can be achieved.

Optimal Region Gradated Fusion Based Automatic Image Stitching for Industrial Systems

In the process of equipment production in large manufacturing, the continuity of production is becoming more significant. Timely detection of equipment operation faults can ensure production continuity and greatly reduce loss. In this study, the purpose is to use multi-equipment and image stitching algorithm to obtain the complete image of a large-scale production line. An improved image stitching method based on image fusion is proposed in this paper, which mainly solves the technical problems of stitching seams, unnatural effects, and distortion after image transformation in the existing stitching technique. In the image stitching algorithm, the improved fusion algorithm based on the optimal seam and gradated in and out fusion algorithm is used to realize image fusion, including the use of dynamic programming to find the optimal seam and limit the range of fusion based on the optimal seam found. Finally, the gradated in and out fusion algorithm is used to perform fusion calculation within the limited fusion range to complete image stitching. In the end, through the comparison of different dimensional image fusion indicators with the effect of the existing fusion algorithm, the experimental results show that the method in this paper solves the problem of unnatural image stitching effect, enhances the image stitching result, and has the great fusion effect. Therefore, the panorama processed by the image stitching algorithm proposed in this paper can be efficiently processed through the industrial detection module.

Real-time and automatic map stitching through aerial images from UAV

Real-time aerial map stitching through aerial images had been done through many different methods. One of the popular methods was a features-based algorithm to detect features and to match the features of two and more images to produce a map. There are several feature-based methods such as ORB, SIFT, SURF, KAZE, AKAZE and BRISK. These methods detect features and compute homography matrix from matched features to stitch images. The aim for this project is to further optimize the existing image stitching algorithm such that it will be possible to run in real-time as the UAV capture images while airborne. First, we propose to use a matrix multiplication method to replace a singular value decomposition method in the RANSAC algorithm. Next, we propose to change the workflow to detect the image features to increase the map stitching rate. The proposed algorithm was implemented and tested with an online aerial image dataset which contain 100 images with the resolution of 640 × 480. We have successfully achieved the result of 1.45 Hz update rate compared to original image stitching algorithm that runs at 0.69 Hz. The improvement shown in our proposed improved algorithm are more than two folds in terms of computational resources. The method introduced in this paper was successful speed up the process time for the program to process map stitching.

Semi-Automatic Spectral Image Stitching for a Compact Hybrid Linescan Hyperspectral Camera towards Near Field Remote Monitoring of Potato Crop Leaves

The miniaturization of hyperspectral cameras has opened a new path to capture spectral information. One such camera, called the hybrid linescan camera, requires accurate control of its movement. Contrary to classical linescan cameras, where one line is available for every band in one shot, the latter asks for multiple shots to fill a line with multiple bands. Unfortunately, the reconstruction is corrupted by a parallax effect, which affects each band differently. In this article, we propose a two-step procedure, which first reconstructs an approximate datacube in two different ways, and second, performs a corrective warping on each band based on a multiple homography framework. The second step combines different stitching methods to perform this reconstruction. A complete synthetic and experimental comparison is performed by using geometric indicators of reference points. It appears throughout the course of our experimentation that misalignment is significantly reduced but remains non-negligible at the potato leaf scale.