scholarly journals Image Mosaic Method Based on Gaussian Second-order Difference Feature Operator

2015 ◽  
Vol 15 (2) ◽  
pp. 336
Author(s):  
Chen Yong ◽  
Hao Yu-bin Hao ◽  
Zhan Di
Keyword(s):  
Image Feature ◽  
Image Mosaic ◽  
Fusion Algorithm ◽  
Point Matching ◽  
Order Difference ◽  
Sift Algorithm ◽  
Matching Efficiency ◽  
Feature Point Matching ◽  
Interest Point Detector ◽  
Point Detector

<p><em>To compose the wide visual angle and high resolution image from the sequence of images which have overlapping region in the same scene quickly and correctly, an improved SIFT algorithm which is based on D2oG interest point detector was proposed. It extracted the image feature points and generated corresponding feature descriptors by improved SIFT algorithm. Then, using the random consistency (RANSAC) algorithm purified feature point matching pairs and calculating the transformation matrix H. Last, complete the seamless mosaic of images by using the image fusion algorithm of slipping into and out. It respectively process the images which had the four typical transformations with the traditional SIFT and the proposed method. The result indicated that the number of feature pairs is fewer than SIFT algorithm and the mosaic time is shorter, and then the matching efficiency is higher than the later. This proposed method reduces the complexity of operation and improves real-time of image mosaic simultaneously.</em></p>

Unsupervised multi-spectral image feature point matching under different lighting scenes

2021 ◽  
Author(s):  
Junchong Huang ◽  
Wei Tian ◽  
Yongkun Wen ◽  
Zhan Chen ◽  
Yuyao Huang
Keyword(s):  
Feature Point ◽  
Image Feature ◽  
Spectral Image ◽  
Point Matching ◽  
Feature Point Matching

Image Mosaics Algorithm Combined Feature Point Matching with Transformation Parameters

2013 ◽  
Vol 380-384 ◽  
pp. 4136-4139
Author(s):  
Peng Rui Qiu ◽  
Ying Liang ◽  
Hui Rong
Keyword(s):  
Projective Transformation ◽  
Feature Point ◽  
Image Mosaic ◽  
Feature Points ◽  
Geometric Information ◽  
Point Matching ◽  
Feature Point Matching ◽  
Transformation Parameters ◽  
Relationship Of ◽  
The Relationship

To solve the problem of the large amount of calculation, poor robustness and do not well in image mosaic of images who are in different scales in the traditional image mosaic method ,the article arise a mosaic algorithm of different scales images registration and adaptive. Through feature point matching and automatically recognizing of transform geometric parameters between images,It achieves the match and mosaic of different scale and rotated images. First, using SIFT to extract the feature points of the images and matching feature points according to the principal of mutual information maximum. Then based on the geometric information of the matching pairs, automatically recognize the relationship of transformation parameters. In the end, obtain the projective transformation and achieve the image stable mosaic.

scholarly journals Feature Point Matching Method for Aerial Image Based on Recursive Diffusion Algorithm

Symmetry ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 407
Author(s):  
Jiayan Shen ◽  
Xiucheng Guo ◽  
Wenzong Zhou ◽  
Yiming Zhang ◽  
Juchen Li
Keyword(s):  
Large Scale ◽  
High Density ◽  
Feature Point ◽  
Aerial Images ◽  
Image Feature ◽  
Aerial Image ◽  
Experimental Comparison ◽  
Matching Accuracy ◽  
Point Matching ◽  
Feature Point Matching

Aerial images are large-scale and susceptible to light. Traditional image feature point matching algorithms cannot achieve satisfactory matching accuracy for aerial images. This paper proposes a recursive diffusion algorithm, which is scale-invariant and can be used to extract symmetrical areas of different images. This narrows the matching range of feature points by extracting high-density areas of the image and improving the matching accuracy through correlation analysis of high-density areas. Through experimental comparison, it can be found that the recursive diffusion algorithm has more advantages compared to the correlation coefficient method and the mean shift algorithm when matching accuracy of aerial images, especially when the light of aerial images changes greatly.

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