Spectral matching algorithm based on nonsubsampled contourlet transform and scale-invariant feature transform

2012 ◽  
Vol 23 (3) ◽  
pp. 453-459 ◽  
Author(s):  
Dong Liang ◽  
Pu Yan ◽  
Ming Zhu ◽  
Yizheng Fan ◽  
Kui Wang
Keyword(s):  
Contourlet Transform ◽  
Scale Invariant Feature Transform ◽  
Nonsubsampled Contourlet Transform ◽  
Scale Invariant ◽  
Spectral Matching ◽  
Matching Algorithm ◽  
Invariant Feature ◽  
Feature Transform ◽  
Scale Invariant Feature

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.

Implementation of SIFT for detection of electronic waste

2018 ◽  
Vol 7 (2.8) ◽  
pp. 353
Author(s):  
A Roshna Meeran ◽  
V Nithya
Keyword(s):  
Image Processing ◽  
Electronic Waste ◽  
Scale Invariant Feature Transform ◽  
Recycling Process ◽  
Scale Invariant ◽  
Object Matching ◽  
Invariant Feature ◽  
Detection And Identification ◽  
Feature Transform ◽  
Scale Invariant Feature

The paper focuses on the investigation of image processing of Electronic waste detection and identification in recycling process of all Electronic items. Some of actually collected images of E-wastes would be combined with other wastes. For object matching with scale in-variance the SIFT (Scale -Invariant- Feature Transform) is applied. This method detects the electronic waste found among other wastes and also estimates the amount of electronic waste detected the give set of wastes. The detection of electronics waste by this method is most efficient ways to detect automatically without any manual means.

Sign in / Sign up

Export Citation Format

Share Document