scholarly journals Analysis of Feature Point Distributions for Fast Image Mosaicking Algorithms

10.14311/1219 ◽  
2010 ◽  
Vol 50 (4) ◽  
Author(s):  
A. Behrens ◽  
H. Röllinger
Keyword(s):  
Feature Detection ◽  
Feature Point ◽  
Distribution Analysis ◽  
Interest Points ◽  
Point Distribution ◽  
Registration Errors ◽  
Image Mosaicking ◽  
Feature Point Matching ◽  
Surf Features ◽  
Image Pairs

In many algorithms the registration of image pairs is done by feature point matching. After the feature detection is performed, all extracted interest points are usually used for the registration process without further feature point distribution analysis. However, in the case of small and sparse sets of feature points of fixed size, suitable for real-time image mosaicking algorithms, a uniform spatial feature distribution across the image becomes relevant. Thus, in this paper we discuss and analyze algorithms which provide different spatial point distributions from a given set of SURF features. The evaluations show that a more uniform spatial distribution of the point matches results in lower image registration errors, and is thus more beneficial for fast image mosaicking algorithms.

scholarly journals A Robust Invariant Local Feature Matching Method for Changing Scenes

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Di Wang ◽  
Hongying Zhang ◽  
Yanhua Shao
Keyword(s):  
Neural Network ◽  
Feature Detection ◽  
Feature Matching ◽  
Local Features ◽  
Local Feature ◽  
Feature Point ◽  
Visual Localization ◽  
Matching Method ◽  
Joint Training ◽  
Image Pairs

The precise evaluation of camera position and orientation is a momentous procedure of most machine vision tasks, especially visual localization. Aiming at the shortcomings of local features of dealing with changing scenes and the problem of realizing a robust end-to-end network that worked from feature detection to matching, an invariant local feature matching method for changing scene image pairs is proposed, which is a network that integrates feature detection, descriptor constitution, and feature matching. In the feature point detection and descriptor construction stage, joint training is carried out based on a neural network. In the feature point extraction and descriptor construction stage, joint training is carried out based on a neural network. To obtain local features with solid robustness to viewpoint and illumination changes, the Vector of Locally Aggregated Descriptors based on Neural Network (NetVLAD) module is introduced to compute the degree of correlation of description vectors from one image to another counterpart. Then, to enhance the relationship between relevant local features of image pairs, the attentional graph neural network (AGNN) is introduced, and the Sinkhorn algorithm is used to match them; finally, the local feature matching results between image pairs are output. The experimental results show that, compared with the existed algorithms, the proposed method enhances the robustness of local features of varying sights, performs better in terms of homography estimation, matching precision, and recall, and when meeting the requirements of the visual localization system to the environment, the end-to-end network tasks can be realized.

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

scholarly journals An Image Copy-Move Forgery Detection Scheme Based on A-KAZE and SURF Features

Symmetry ◽  
2018 ◽  
Vol 10 (12) ◽  
pp. 706 ◽  
Author(s):  
Chengyou Wang ◽  
Zhi Zhang ◽  
Xiao Zhou
Keyword(s):  
Feature Detection ◽  
Joint Photographic Expert Group ◽  
Social Stability ◽  
Forgery Detection ◽  
Detection Scheme ◽  
Speeded Up Robust Features ◽  
Image Blurring ◽  
Surf Features ◽  
Copy Move Forgery Detection ◽  
Processing Techniques

The popularity of image editing software has made it increasingly easy to alter the content of images. These alterations threaten the authenticity and integrity of images, causing misjudgments and possibly even affecting social stability. The copy-move technique is one of the most commonly used approaches for manipulating images. As a defense, the image forensics technique has become popular for judging whether a picture has been tampered with via copy-move, splicing, or other forgery techniques. In this paper, a scheme based on accelerated-KAZE (A-KAZE) and speeded-up robust features (SURF) is proposed for image copy-move forgery detection (CMFD). It is difficult for most keypoint-based CMFD methods to obtain sufficient points in smooth regions. To remedy this defect, the response thresholds for the A-KAZE and SURF feature detection stages are set to small values in the proposed method. In addition, a new correlation coefficient map is presented, in which the duplicated regions are demarcated, combining filtering and mathematical morphology operations. Numerous experiments are conducted to demonstrate the effectiveness of the proposed method in searching for duplicated regions and its robustness against distortions and post-processing techniques, such as noise addition, rotation, scaling, image blurring, joint photographic expert group (JPEG) compression, and hybrid image manipulation. The experimental results demonstrate that the performance of the proposed scheme is superior to that of other tested CMFD methods.

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