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.

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.

An Improved SIFT Matching Algorithm

2012 ◽  
Vol 239-240 ◽  
pp. 1232-1237 ◽  
Author(s):  
Can Ding ◽  
Chang Wen Qu ◽  
Feng Su
Keyword(s):  
High Dimension ◽  
Gradient Method ◽  
Feature Matching ◽  
Feature Descriptor ◽  
Scale Invariant ◽  
Matching Algorithm ◽  
Invariant Feature ◽  
Feature Transform ◽  
New Feature ◽  
Scale Invariant Feature

The high dimension and complexity of feature descriptor of Scale Invariant Feature Transform (SIFT), not only occupy the memory spaces, but also influence the speed of feature matching. We adopt the statistic feature point’s neighbor gradient method, the local statistic area is constructed by 8 concentric square ring feature of points-centered, compute gradient of these pixels, and statistic gradient accumulated value of 8 directions, and then descending sort them, at last normalize them. The new feature descriptor descend dimension of feature from 128 to 64, the proposed method can improve matching speed and keep matching precision at the same time.

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.

The Application of SIFT Algorithm in Blind Road Environmental Image Matching

2012 ◽  
Vol 155-156 ◽  
pp. 1137-1141
Author(s):  
Shuo Shi ◽  
Ming Yu ◽  
Cui Hong Xue ◽  
Ying Zhou
Keyword(s):  
Feature Extraction ◽  
Image Matching ◽  
Feature Points ◽  
Scale Invariant ◽  
Sift Algorithm ◽  
Matching Algorithm ◽  
Invariant Feature ◽  
Extraction Algorithm ◽  
Environmental Image ◽  
Scale Invariant Feature

Image Matching is a key technology in the intelligent navigation system for the blind, which is based on the computer video. The images of moving blind people, collected at real time, have variety of changes in light, rotation, scaling, etc. Against this feature, we propose a practical matching algorithm, which is based on the SIFT (Scale Invariant, Feature Transform). That is the image matching algorithm. We focus on the algorithm of the SIFT feature extraction and matching, and obtain the feature points of the image through feature extraction algorithm. We verify the effect of the algorithm by selecting practical images with rotation, scaling and different light. The result is that this method can get better matches for the blind road environmental image.

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.

scholarly journals Bidirectional scale-invariant feature transform feature matching algorithms based on priority k-d tree search

2016 ◽  
Vol 14 (1) ◽  
pp. 172988141668270
Author(s):  
XiangShao Liu ◽  
Shangbo Zhou ◽  
Hua Li ◽  
Kun Li
Keyword(s):  
Image Registration ◽  
Feature Matching ◽  
Scale Invariant Feature Transform ◽  
Tree Search ◽  
Scale Invariant ◽  
Matching Algorithm ◽  
Invariant Feature ◽  
Feature Transform ◽  
Key Steps ◽  
Scale Invariant Feature

In this article, a bidirectional feature matching algorithm and two extended algorithms based on the priority k-d tree search are presented for the image registration using scale-invariant feature transform features. When matching precision of image registration is below 50%, the discarding wrong match performance of many robust fitting methods like Random Sample Consensus (RANSAC) is poor. Therefore, improving matching precision is a significant work. Generally, a feature matching algorithm is used once in the image registration system. We propose a bidirectional algorithm that utilizes the priority k-d tree search twice to improve matching precision. There are two key steps in the bidirectional algorithm. According to the case of adopting the ratio restriction of distances in the two key steps, we further propose two extended bidirectional algorithms. Experiments demonstrate that there are some special properties of these three bidirectional algorithms, and the two extended algorithms can achieve higher precisions than previous feature matching algorithms.

A Comparison of Two Typical Local Feature Matching Algorithm: SIFT and MSER

2014 ◽  
Vol 687-691 ◽  
pp. 4119-4122
Author(s):  
Xiao Cun Jiang ◽  
Xiao Liu ◽  
Tao Tang ◽  
Xiao Hu Fan ◽  
Xiao Cui
Keyword(s):  
Feature Matching ◽  
Local Feature ◽  
Scale Invariant Feature Transform ◽  
Scale Invariant ◽  
Matching Algorithm ◽  
Invariant Feature ◽  
Distance Difference ◽  
Feature Transform ◽  
Scale Invariant Feature ◽  
Shooting Distance

Scale invariant feature transform matching algorithm and Maximally Stable Extremal Regions matching algorithm have been widely used because of their good performance. The two local feature matching algorithms were compared through numbers of experiments in this paper. The experiment results showed that SIFT is good at dealing with the image distortion from shooting distance difference and small shooting viewpoint deviation; MSER is good at handling the complicated affine distortion from big shooting viewpoint deviation. From the aspect of scene types, the performance of SIFT is good both to structure images and texture images. MSER is suitable for the matching of structure images, but not so successful to that of texture images.

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