An image registration algorithm based on phase correlation and the classical Lucas–Kanade technique

In this research paper, a precision position-measurement system based on the image grating technique is presented. The system offers a better robustness and flexibility for 1D position measurement compared to a conventional optical encoder. It is equipped with an image grating attached to a linear stage as the target feature and a line scan camera as the stationary displacement reader. By measuring the position of the specific feature in the image and applying a subpixel image registration method, the position of the linear stage can be obtained. In order to improve the computational efficiency, the calculations for pattern correlation and subpixel registration are performed in the frequency domain. An error compensation method based on a lens distortion model is investigated and implemented to improve the measurement accuracy of the proposed system. Experimental data confirms the capability of the developed image grating system as ±0.3 µm measurement accuracy within a 50 mm range and ±0.2 µm measurement accuracy within a 25 mm range. By applying different optics, the standoff distance, measurement range, and resolution can be customized to conform to different precision measurement applications.

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Development of an Image Grating Sensor for Position Measurement

Fang

Tjahjowidodo

et al. 2019

“…The Lucas-Kanade optical flow method uses spatial brightness gradient information to obtain better matching position. It has three assumptions [ 22 , 23 ]: firstly, the brightness between two adjacent frames is constant; secondly, in order to solve the aperture problem, there is the same motion in the same integration window; thirdly, the motion of the object between adjacent frames is relatively small. In order to make all kinds of video images generally conform to the hypothesis thirdly, Bouguet proposed pyramid Lucas Kanade optical flow algorithm.…”

Section: Binocular Stereo Panoramic Image Synthesis Algorithmmentioning

confidence: 99%

Panoramic Stereo Imaging of a Bionic Compound-Eye Based on Binocular Vision

Wang

Zhang

2021

With the rapid development of the virtual reality industry, one of the bottlenecks is the scarcity of video resources. How to capture high-definition panoramic video with depth information and real-time stereo display has become a key technical problem to be solved. In this paper, the optical optimization design scheme of panoramic imaging based on binocular stereo vision is proposed. Combined with the real-time processing algorithm of multi detector mosaic panoramic stereo imaging image, a panoramic stereo real-time imaging system is developed. Firstly, the optical optimization design scheme of panoramic imaging based on binocular stereo vision is proposed, and the space coordinate calibration platform of ultra-high precision panoramic camera based on theodolite angle compensation function is constructed. The projection matrix of adjacent cameras is obtained by solving the imaging principle of binocular stereo vision. Then, a real-time registration algorithm of multi-detector mosaic image and Lucas-Kanade optical flow method based on image segmentation are proposed to realize stereo matching and depth information estimation of panoramic imaging, and the estimation results are analyzed effectively. Experimental results show that the stereo matching time of panoramic imaging is 30 ms, the registration accuracy is 0.1 pixel, the edge information of depth map is clearer, and it can meet the imaging requirements of different lighting conditions.

“…It refers to the technique that merges an obtained sequence of two or more UAV images into one wide-field image by finding an appropriative image transformation model [6]. Various methods are proposed to find the model, such as region-based methods, represented by phase correlation [7], however, feature-point-based methods are more widely used [8].…”

Section: Related Workmentioning

confidence: 99%

Fast Anchor Point Matching for Emergency UAV Image Stitching Using Position and Pose Information

Shao

Chen

et al. 2020

With the development of unmanned aerial vehicle (UAV) techniques, UAV images are becoming more widely used. However, as an essential step of UAV image application, the computation of stitching remains time intensive, especially for emergency applications. Addressing this issue, we propose a novel approach to use the position and pose information of UAV images to speed up the process of image stitching, called FUIS (fast UAV image stitching). This stitches images by feature points. However, unlike traditional approaches, our approach rapidly finds several anchor-matches instead of a lot of feature matches to stitch the image. Firstly, from a large number of feature points, we design a method to select a small number of them that are more helpful for stitching as anchor points. Then, a method is proposed to more quickly and accurately match these anchor points, using position and pose information. Experiments show that our method significantly reduces the time consumption compared with the-state-of-art approaches with accuracy guaranteed.