scholarly journals Improved Parallax Image Stitching Algorithm Based on Feature Block

Symmetry ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 348 ◽  
Author(s):  
Huaitao Shi ◽  
Lei Guo ◽  
Shuai Tan ◽  
Gang Li ◽  
Jie Sun
Keyword(s):  
Feature Matching ◽  
Computational Cost ◽  
Reference Image ◽  
Image Stitching ◽  
Feature Points ◽  
Shape Distortion ◽  
Scale Invariant ◽  
Point Matching ◽  
Feature Point Matching ◽  
Scale Invariant Feature

Image stitching aims at generating high-quality panoramas with the lowest computational cost. In this paper, we present an improved parallax image-stitching algorithm using feature blocks (PIFB), which achieves a more accurate alignment and faster calculation speed. First, each image is divided into feature blocks using an improved fuzzy C-Means (FCM) algorithm, and the characteristic descriptor of each feature block is extracted using scale invariant feature transform (SIFT). The feature matching block of the reference image and the target image are matched and then determined, and the image is pre-registered using the homography calculated by the feature points in the feature block. Finally, the overlapping area is optimized to avoid ghosting and shape distortion. The improved algorithm considering pre-blocking and block stitching effectively reduced the iterative process of feature point matching and homography calculation. More importantly, the problem that the calculated homography matrix was not global has been solved. Ghosting and shape warping are significantly eliminated by re-optimizing the overlap of the image. The performance of the proposed approach is demonstrated using several challenging cases.

scholarly journals A Registration Scheme for Multispectral Systems Using Phase Correlation and Scale Invariant Feature Matching

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Hanlun Li ◽  
Aiwu Zhang ◽  
Shaoxing Hu
Keyword(s):  
Phase Correlation ◽  
Feature Point ◽  
Reference Image ◽  
Feature Points ◽  
Scale Invariant ◽  
Matching Problem ◽  
Invariant Feature ◽  
Significant Difference ◽  
Scale Invariant Feature ◽  
Band Image

In the past few years, many multispectral systems which consist of several identical monochrome cameras equipped with different bandpass filters have been developed. However, due to the significant difference in the intensity between different band images, image registration becomes very difficult. Considering the common structural characteristic of the multispectral systems, this paper proposes an effective method for registering different band images. First we use the phase correlation method to calculate the parameters of a coarse-offset relationship between different band images. Then we use the scale invariant feature transform (SIFT) to detect the feature points. For every feature point in a reference image, we can use the coarse-offset parameters to predict the location of its matching point. We only need to compare the feature point in the reference image with the several near feature points from the predicted location instead of the feature points all over the input image. Our experiments show that this method does not only avoid false matches and increase correct matches, but also solve the matching problem between an infrared band image and a visible band image in cases lacking man-made objects.

A Fast Feature Matching Algorithm Based on Multi Scale Spatial Segmentation Technology

2014 ◽  
Vol 490-491 ◽  
pp. 1217-1220
Author(s):  
Shu Rong Li ◽  
Yuan Yuan Huang ◽  
Zuo Jin Hu
Keyword(s):  
Feature Matching ◽  
Multiple Scales ◽  
Feature Space ◽  
Feature Points ◽  
Huge Amount ◽  
Scale Invariant ◽  
Matching Algorithm ◽  
Multi Scale ◽  
Spatial Segmentation ◽  
Scale Invariant Feature

SIFT (Scale invariant feature transform) and correlative algorithms are now widely used in content based image retrieval technology. They compute distance and use neighbor algorithm to look for the optimal matching couples. The disadvantage of such way is high complexity, especially when huge amount of images need to be retrieved or recognized. To solve this problem, a new matching way based on feature space division under multi-scale is proposed. The algorithm will divide the feature space under multiple scales, so that those feature points which are located in somewhere can use a code to represent, and finally realize the matching through the code. Without calculating distance, the algorithm complexity is greatly reduced. Experiments show that, the algorithm keeps the matching accuracy and greatly enhance the efficiency of the matching at the same time.

Scale‐invariant feature matching based on pairs of feature points

2015 ◽  
Vol 9 (6) ◽  
pp. 789-796 ◽  
Author(s):  
Zhiheng Wang ◽  
Zhifei Wang ◽  
Hongmin Liu ◽  
Zhanqiang Huo
Keyword(s):  
Feature Matching ◽  
Feature Points ◽  
Scale Invariant ◽  
Invariant Feature ◽  
Scale Invariant Feature

scholarly journals An Interband Registration Method for Hyperspectral Images Based on Adaptive Iterative Clustering

2021 ◽  
Vol 13 (8) ◽  
pp. 1491
Author(s):  
Shiyong Wu ◽  
Ruofei Zhong ◽  
Qingyang Li ◽  
Ke Qiao ◽  
Qing Zhu
Keyword(s):  
Feature Matching ◽  
Adaptive Strategy ◽  
Hyperspectral Images ◽  
Feature Points ◽  
Scale Invariant ◽  
Registration Method ◽  
Perform Cluster Analysis ◽  
Blurred Image ◽  
Scale Invariant Feature ◽  
Better Than

In the context of the problem of image blur and nonlinear reflectance difference between bands in the registration of hyperspectral images, the conventional method has a large registration error and is even unable to complete the registration. This paper proposes a robust and efficient registration algorithm based on iterative clustering for interband registration of hyperspectral images. The algorithm starts by extracting feature points using the scale-invariant feature transform (SIFT) to achieve initial putative matching. Subsequently, feature matching is performed using four-dimensional descriptors based on the geometric, radiometric, and feature properties of the data. An efficient iterative clustering method is proposed to perform cluster analysis on the proposed descriptors and extract the correct matching points. In addition, we use an adaptive strategy to analyze the key parameters and extract values automatically during the iterative process. We designed four experiments to prove that our method solves the problem of blurred image registration and multi-modal registration of hyperspectral images. It has high robustness to multiple scenes, multiple satellites, and multiple transformations, and it is better than other similar feature matching algorithms.

Medical Image Feature Matching Based on Wavelet Transform and SIFT Algorithm

2011 ◽  
Vol 65 ◽  
pp. 497-502
Author(s):  
Yan Wei Wang ◽  
Hui Li Yu
Keyword(s):  
Wavelet Transform ◽  
Wavelet Transforms ◽  
Medical Image ◽  
Feature Matching ◽  
Image Feature ◽  
Scale Invariant ◽  
Sift Algorithm ◽  
Matching Algorithm ◽  
Invariant Feature ◽  
Scale Invariant Feature

A feature matching algorithm based on wavelet transform and SIFT is proposed in this paper, Firstly, Biorthogonal wavelet transforms algorithm is used for medical image to delaminating, and restoration the processed image. Then the SIFT (Scale Invariant Feature Transform) applied in this paper to abstracting key point. Experimental results show that our algorithm compares favorably in high-compressive ratio, the rapid matching speed and low storage of the image, especially for the tilt and rotation conditions.

scholarly journals Pose detection of parallel robot based on improved RANSAC algorithm

2019 ◽  
Vol 52 (7-8) ◽  
pp. 855-868 ◽  
Author(s):  
Guo-Qin Gao ◽  
Qian Zhang ◽  
Shu Zhang
Keyword(s):  
Real Time ◽  
Parallel Robot ◽  
Feature Points ◽  
Scale Invariant ◽  
Time Performance ◽  
Invariant Feature ◽  
Pose Detection ◽  
Feature Transform ◽  
Scale Invariant Feature ◽  
Ransac Algorithm

For the factors of complex image background, unobvious end-effector characteristics and uneven illumination in the pose detection of parallel robot based on binocular vision, the detection speed, and accuracy cannot meet the requirement of the closed-loop control. So a pose detection method based on improved RANSAC algorithm is presented. First, considering that the image of parallel robot is rigid and has multiple corner points, the Harris–Scale Invariant Feature Transform algorithm is adopted to realize image prematching. The feature points are extracted by Harris and matched by Scale Invariant Feature Transform to realize good accuracy and real-time performance. Second, for the mismatching from prematching, an improved RANSAC algorithm is proposed to refine the prematching results. This improved algorithm can overcome the disadvantages of mismatching and time-consuming of the conventional RANSAC algorithm by selecting feature points in separated grids of the images and predetecting to validate provisional model. The improved RANSAC algorithm was applied to a self-developed novel 3-degrees of freedom parallel robot to verify the validity. The experiment results show that, compared with the conventional algorithm, the average matching time decreases by 63.45%, the average matching accuracy increases by 15.66%, the average deviations of pose detection in Y direction, Z direction, and roll angle [Formula: see text] decrease by 0.871 mm, 0.82 mm, and 0.704°, respectively, using improved algorithm to refine the prematching results. The real-time performance and accuracy of pose detection of parallel robot can be improved.

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.

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