scholarly journals Feature Coverage Indexes for Dual Homography Estimation in Constructing Panorama Image

2016 ◽  
Vol 2016 ◽  
pp. 1-7
Author(s):  
Kyungkoo Jun ◽  
Sijung Kim
Keyword(s):  
Image Sensors ◽  
Image Stitching ◽  
Feature Points ◽  
Feature Detectors ◽  
Panoramic Images ◽  
Homography Estimation

Enlarged images can be obtained by various methods. Stitching is one of the efficient methods. It can produce panoramic images by stitching adjacent images which contain overlapping regions even though they are obtained through separate image sensors. Images that contain multiple different planes are hard to be stitched together because each plane has a different homography matrix for perspective warping. For this, a dual homography was proposed. However its performance varies depending on feature detectors which are used to find matching feature points between images. In this paper, we propose three feature coverage indexes which evaluate the stitching performance of feature detectors and predict the outcomes of the stitching. We evaluate four well-known feature detectors by the proposed indexes by applying them to the image stitching process and show that the prediction by the index values coincides with the stitching results.

scholarly journals Color Balance for Panoramic Images

2015 ◽  
Vol 9 (13) ◽  
pp. 140 ◽  
Author(s):  
Ahmed Bdr Eldeen Ahmed Mohammed ◽  
Fang Ming ◽  
Ren Zhengwei
Keyword(s):  
Weighted Average ◽  
Image Sequences ◽  
Experimental Result ◽  
Image Stitching ◽  
Feature Points ◽  
Weighted Average Method ◽  
Image Mosaicking ◽  
Panoramic Images ◽  
Pixel Value ◽  
Exposure Levels

<p>Color correction or color balancing in multi-view image stitching is the process of correcting the color<br />differences between neighboring views which arise due to different exposure levels and view angles. This paper<br />concerns the problem of color balance for panoramic images. A new algorithm is presented to create visual<br />normalization by using correlated feature points between adjacent image sequences. After image mosaicking<br />directly, a weighted average method is used to calculate the pixel value of panoramic image Experimental result<br />shows that the algorithm can achieve images color balance and good visual performance.</p>

Automatic Registration of Mobile Mapping System Lidar Points and Panoramic-Image Sequences by Relative Orientation Model

2021 ◽  
Vol 87 (12) ◽  
pp. 913-922
Author(s):  
Ningning Zhu ◽  
Bisheng Yang ◽  
Zhen Dong ◽  
Chi Chen ◽  
Xia Huang ◽  
...  
Keyword(s):  
Image Sequence ◽  
Image Sequences ◽  
Relative Orientation ◽  
Panoramic Image ◽  
Feature Points ◽  
Mobile Mapping ◽  
Orientation Model ◽  
Mapping System ◽  
Panoramic Images ◽  
Mobile Mapping System

To register mobile mapping system (MMS) lidar points and panoramic-image sequences, a relative orientation model of panoramic images (PROM) is proposed. The PROM is suitable for cases in which attitude or orientation parameters are unknown in the panoramic-image sequence. First, feature points are extracted and matched from panoramic-image pairs using the SURF algorithm. Second, these matched feature points are used to solve the relative attitude parameters in the PROM. Then, combining the PROM with the absolute position and attitude parameters of the initial panoramic image, the MMS lidar points and panoramic-image sequence are registered. Finally, the registration accuracy of the PROM method is assessed using corresponding points manually selected from the MMSlidar points and panoramic-image sequence. The results show that three types of MMSdata sources are registered accurately based on the proposed registration method. Our method transforms the registration of panoramic images and lidar points into image feature-point matching, which is suitable for diverse road scenes compared with existing methods.

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

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 2007
Author(s):  
Ruizhe Shao ◽  
Chun Du ◽  
Hao Chen ◽  
Jun Li
Keyword(s):  
Image Stitching ◽  
Feature Points ◽  
Point Matching ◽  
Novel Approach ◽  
Aerial Vehicle ◽  
Speed Up ◽  
Anchor Points ◽  
Uav Images ◽  
Uav Image ◽  
Traditional Approaches

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.

A New Algorithm of Fast-Generated Panoramic Images

2011 ◽  
Vol 393-395 ◽  
pp. 539-542
Author(s):  
Wei Cong Na
Keyword(s):  
Random Sampling ◽  
Euclidean Distance ◽  
Feature Matching ◽  
Correction Coefficient ◽  
Feature Points ◽  
Generation Efficiency ◽  
Sift Algorithm ◽  
Panoramic Images ◽  
Space Image ◽  
Quality Guarantee

The new algorithm of fast-generated panoramic images this paper puts forward is to extract the feature points of images by the improved SIFT algorithm, and use Euclidean distance combining the K-D tree structure to realize the rapid initial feature matching. Then, based on these initial matching points and the theory of random sampling consistent algorithm, the purification of feature points is realized. At last, the introduction of correction coefficient makes it possible to eliminate fusion ghosts, and HIS space image fusion is applied in order to eliminate the brightness differences. It is verified by the experiments that on the premise of generation of quality guarantee, the new algorithm greatly improves the generation efficiency of panorama images.

Fast Mosaicking Panoramic Images with Parallax Scene from a PTZ Camera

2012 ◽  
Vol 468-471 ◽  
pp. 1775-1780
Author(s):  
Yu Shuang Zhang ◽  
Shu Xiao Li ◽  
Hong Xing Chang
Keyword(s):  
Video Sequence ◽  
Image Mosaic ◽  
Feature Points ◽  
Interest Points ◽  
New Approach ◽  
Ptz Camera ◽  
Panoramic Images ◽  
Transform Matrix ◽  
The Cost

This paper presents a fast panoramic mosaic algorithm from a video sequence with parallax scene taken by a PTZ camera. A new approach that uses a four-step automatic imaging mosaic, based on interest points, is proposed. The four steps are extraction of interest points, finding corresponding points in the stitching images, deriving the spatial transform matrix then image mosaic. In order to reduce the cost of searching best match of feature points, we employ SIFT-16 descriptor and the LMPs descriptor as index . Our method preserves the efficiency and accuracy of image mosaic.

scholarly journals The Improved Algorithm of Fast Panorama Stitching for Image Sequence and Reducing the Distortion Errors

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Zhong Qu ◽  
Si-Peng Lin ◽  
Fang-Rong Ju ◽  
Ling Liu
Keyword(s):  
Feature Detection ◽  
Image Sequence ◽  
Input Image ◽  
Coordinate Space ◽  
Reference Image ◽  
Image Stitching ◽  
Feature Points ◽  
Improved Method ◽  
Sift Feature ◽  
Improved Algorithm

The traditional image stitching result based on the SIFT feature points extraction, to a certain extent, has distortion errors. The panorama, especially, would get more seriously distorted when compositing a panoramic result using a long image sequence. To achieve the goal of creating a high-quality panorama, the improved algorithm is proposed in this paper, including altering the way of selecting the reference image and putting forward a method that can compute the transformation matrix for any image of the sequence to align with the reference image in the same coordinate space. Additionally, the improved stitching method dynamically selects the next input image based on the number of SIFT matching points. Compared with the traditional stitching process, the improved method increases the number of matching feature points and reduces SIFT feature detection area of the reference image. The experimental results show that the improved method can not only accelerate the efficiency of image stitching processing, but also reduce the panoramic distortion errors, and finally we can obtain a pleasing panoramic result.

Image stitching with positional relationship constraints of feature points and lines

2020 ◽  
Vol 135 ◽  
pp. 431-440
Author(s):  
Xiaoyuan Luo ◽  
Yang Li ◽  
Jing Yan ◽  
Xinping Guan
Keyword(s):  
Image Stitching ◽  
Feature Points

A Fast Underwater Image Stitching Algorithm with Adaptive Adjustment of Attitude Angle

2021 ◽  
Author(s):  
Lizhou Jiang ◽  
Zhijie Tang ◽  
Zhihang Luo ◽  
Chi Wang
Keyword(s):  
Feature Point ◽  
Reference Image ◽  
Image Stitching ◽  
Panoramic Image ◽  
Feature Points ◽  
Image Capture ◽  
Matching Process ◽  
Underwater Image ◽  
Adaptive Adjustment ◽  
The Impact

In underwater image acquisition process, due to the impact of water currents and other disturbances, the movement posture of the underwater machine will be unstable, which could lead to unusual problems such as twisting of underwater image capture. These factors will increase the error rate of feature point matching and lead to the failure of panoramic image mosaic. In this regard, we propose a new, highly applicable underwater image stitching algorithm. Firstly, the posture angle adjustment link is added to the underwater image processing, and the angle deflection problem of the underwater image is effectively improved by using the posture angle information. Secondly, the feature points of underwater images are extracted based on the accelerated robust feature (SURF) algorithm. Then, the reference image is matched with the feature points of the image to be registered, and effective feature point pairs are obtained by screening. Finally, the images are stitched based on OpenCV to obtain a good panoramic image. Afterexperimental analysis and comparison, our method can increase the number of matching feature point pairs between images. In addition, the Euclidean distance is significantly shortened during the matching process, which further makes the matching of feature points more accurate. Our method satisfactorily overcomes the adverse effects of actual underwater operations and has a better application prospect.

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