The effect of rainfall on feature points extraction and image stitching

2014 ◽  
Author(s):  
Wai Chong Chia ◽  
Lee Seng Yeong ◽  
Sue Inn Ch'ng ◽  
Kah Phooi Seng ◽  
Li-Minn Ang
Keyword(s):  
Image Stitching ◽  
Feature Points

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.

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>

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.

Image Registration of Blown and Swayed Trees

2011 ◽  
Vol 474-476 ◽  
pp. 2183-2188
Author(s):  
Dian Yuan Han ◽  
Xin Yuan Huang
Keyword(s):  
Image Processing ◽  
Image Registration ◽  
Image Stitching ◽  
Feature Points ◽  
Tree Leaves ◽  
Processing Methods ◽  
Morphological Image Processing ◽  
Tree Image ◽  
Morphological Image

This paper concerns the problem of blown and swayed tree image registration. The swayed tree leaves and little branches contain plenty of details or feature points, but matching with these points may result in error image registration. In this work, we delete some details of leaves and little and thin branches by using of morphological image processing methods, then extract the relatively invariant crotch features of trees from thinning images, lastly we adjusted the matching points with L-M algorithm. Results show our method is insensitive to the ordering, rotation and scale of the input images so it can be used in image stitching and retrieval of images & videos.

scholarly journals A Novel Fast Image Stitching Method Based on the Combination of SURF and Cell

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Qing An ◽  
Xijiang Chen ◽  
Shusen Wu
Keyword(s):  
Image Fusion ◽  
Power Function ◽  
Chromatic Aberration ◽  
Image Stitching ◽  
Feature Points ◽  
Matching Accuracy ◽  
Fusion Algorithm ◽  
Weighted Fusion ◽  
The Mean ◽  
Traditional Image

The traditional image stitching method has some shortcomings such as double shadow, chromatic aberration, and stitching. In view of this, this paper proposes a power function-weighted image stitching method that combines SURF optimization and improved cell acceleration. First, the method uses the cosine similarity to preliminarily judge the similarity of the feature points and then uses the two-way consistency mutual selection to filter the feature point pairs again. Simultaneously, some incorrect matching points in the reverse matching are eliminated. Finally, the method uses the MSAC algorithm to perform fine matching. Then, the power function-weighted fusion algorithm is used to calculate the weight of the center point. The power function weight of the accelerated cell is used to perform the final image fusion. The experimental results show that the matching accuracy rate of the proposed method is about 11 percentage points higher than the traditional SURF algorithm, and the time is reduced by about 1.6 s. In the image fusion stage, this paper first selects images with different brightness, angles, resolutions, and scales to verify the effectiveness of the proposed method. The results show that the proposed method effectively solves the ghosting and stitching seams. Comparing with the traditional fusion algorithm, the time consumption is reduced by at least 2 s, the mean square error is reduced by about 1.32%∼1.48%, and the information entropy is improved by about 0.98%∼1.70%. The proposed method has better performance in matching accuracy and fusion effect and has better stitching quality.

scholarly journals The Feature based Image Stitching Techniques

2019 ◽  
Vol 8 (2) ◽  
pp. 5543-5547
Keyword(s):  
Computer Software ◽  
Vital Role ◽  
Image Stitching ◽  
Feature Points ◽  
Local Descriptor ◽  
Digital Maps ◽  
Feature Based ◽  
Stitching Techniques ◽  
The Given ◽  
Different Sources

The photographic images taken from different sources are integrated into one to form a new panoramic image which has high resolution when compared to the original image. Usually, image stitching can be done using any computer software. Image stitching helps us in extracting the actual information from shredded data. The torned paper can be reconstructed by means of image stitching process which is applied in forensic department. Digital maps and satellite photos can be reconstructed through image stitching algorithms which is also known as Image mosics. In recent days, many researchers introduced various algorithms to address the issues in Image stitching technique. Feature based Image stitching technique plays a vital role in most of these algorithms. In feature-based technique, local descriptor is used to compare an image’s feature points with another image’s feature points to predict the feature points for the given pair of image. In this paper different image stitching techniques, comparisons between various features descriptor and the steps involved in Feature extraction in Image stitching method are discussed.

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