Improving Image Matching Accuracy with Keypoint Features and Feature Fusion

2021 ◽  
Author(s):  
Tongyu Lu
Keyword(s):  
Image Matching ◽  
Feature Fusion ◽  
Matching Accuracy

Image Matching Optimization Based on Taguchi Method and Adaptive Spatial Clustering with SIFT Features

2017 ◽  
Vol 31 (11) ◽  
pp. 1755014 ◽  
Author(s):  
Yuan Xu ◽  
Hehui Lu ◽  
Defu Zhou ◽  
Jiongbin Zheng ◽  
Jianguo Zhang
Keyword(s):  
Taguchi Method ◽  
Image Matching ◽  
Spatial Clustering ◽  
Signal To Noise Ratio ◽  
Matching Accuracy ◽  
Scale Invariant ◽  
Effect Factor ◽  
Optimum Parameters ◽  
Scale Invariant Feature ◽  
Selection Of

A novel image matching algorithm based on both Taguchi method and spatial clustering is proposed to optimize the Scale Invariant Feature Transform (SIFT) matching results. To improve the matching accuracy, adaptive spatial clustering is used. What is more, in order to get the fitting parameters to balance matching accuracy and quantity, Taguchi method is adopted to optimize the key parameter combination including the ratio threshold of Euclidean distance and the constrain parameters in the process of adaptive spatial clustering. Moreover, signal-to-noise ratio (SNR) results are analyzed by variance to get the effect factor which is taken as the basis for the selection of optimized parameters. The optimum parameters combination is obtained eventually. The final experimental results show that the matching quality based on SIFT feature are improved significantly.

An Effective Image Matching Algorithm Based on Rotation Independence

2013 ◽  
Vol 347-350 ◽  
pp. 3685-3690
Author(s):  
Xian Ying Huang ◽  
Wei Wei Chen
Keyword(s):  
Image Matching ◽  
Irregular Shape ◽  
Matching Accuracy ◽  
Similarity Comparison ◽  
Matching Algorithm ◽  
Irregular Shapes ◽  
Irregular Region ◽  
Histogram Intersection ◽  
Intersection Method ◽  
Traditional Image

Traditional image matching algorithms has poor accuracy in image comparing, such as histogram intersection method. A new image matching algorithm based on the similarity comparison of irregular shape is presented in this paper, which divides the image into a number of irregular regions according to different colors, and extracts the boundary points of the irregular region to compose an irregular shape. The direction and distance is used to comparing the two irregular shapes if the rotation of the image is not considered, otherwise circular list is used to ignore the image rotation. It can be used widely. If two irregular shapes are similar, the two images are considered similar. Experiment proves that this method can effectively improve the image matching accuracy.

A New Algorithm of Image Matching Combining Sift and Shape Context

2014 ◽  
Vol 644-650 ◽  
pp. 4307-4313
Author(s):  
Yan Lu Xu ◽  
Yan Ma ◽  
Shun Bao Li ◽  
Ning Li Zhang ◽  
Xiang Fen Zhang ◽  
...  
Keyword(s):  
Image Matching ◽  
Experimental Results ◽  
Feature Points ◽  
Shape Context ◽  
Matching Accuracy ◽  
Conventional Methods ◽  
Distance Formula

This paper presents a new algorithm of image matching via combining sift and shape context for improving image matching accuracy. A joint descriptor is applied to describe feature points. The initial matching is obtained by a proposed distance formula. Furthermore, PLS is introduced to eliminate mismatched points. Experimental results demonstrate the proposed algorithm can achieve better performance compared to conventional methods.

Research of Stereo Matching Based on HALCON

2013 ◽  
Vol 415 ◽  
pp. 361-364
Author(s):  
Hui Yu Xiang ◽  
Zhe Li ◽  
Jia Jun Huang ◽  
Baoan Han
Keyword(s):  
Image Matching ◽  
Stereo Matching ◽  
Vision System ◽  
Difficult Problem ◽  
Matching Method ◽  
Matching Accuracy ◽  
Matching Algorithm ◽  
Binocular Stereo Vision ◽  
Binocular Stereo ◽  
Matching Window

Binocular stereo matching is a hot and difficult problem in machine vision. In this paper, based on the matching method of Halcon which is visual software perform image matching. First, performing binocular stereo vision system calibration, based on the calibration results acquired the epipolar standard geometric structure. Then, image matching researched under this structure. At last, using ncc matching algorithm, through comparing the different parameters matching window obtain ideal match results. Experiments prove that this method not only can effectively shorten matching time, but also can achieve higher matching accuracy.

Study on Image Matching Based on Speed up Robust Features Method

2014 ◽  
Vol 1044-1045 ◽  
pp. 1352-1356
Author(s):  
Shu Guang Wu ◽  
Shu He ◽  
Xia Yang
Keyword(s):  
Image Processing ◽  
Image Registration ◽  
Digital Image Processing ◽  
Digital Image ◽  
Image Matching ◽  
Comprehensive Analysis ◽  
The Other ◽  
Matching Method ◽  
Matching Accuracy ◽  
Speed Up

Image registration is one of the fundamental problems in digital image processing, which is a prerequisite and key step for further comprehensive analysis,considering the advantages of the algorithm in speed and its disadvantage of more false matching points,a image matching method based on RANSAC and surf isproposed.The experiments results show that compared with the other algorithms,the surf algorithm improves the matching speed，as well as the matching accuracy，and exhibits good performance in terms of resisting rotation，noise,and brightness changes．

Image Matching Based on Analysis of Reliability in Log-Polar Field

2012 ◽  
Vol 532-533 ◽  
pp. 954-958
Author(s):  
Yi Ping Xu ◽  
Jing Tan ◽  
Hong Ping Li ◽  
Yan Tian
Keyword(s):  
Target Detection ◽  
Image Matching ◽  
Polar Field ◽  
Data Reliability ◽  
Matching Method ◽  
Matching Accuracy ◽  
Matching Performance

Due to the invariance to scale and rotation, image matching method based on Log-Polar transformation has been extensively applied in target detection and location. This paper analyses the effect on image matching accuracy from variation of scale and rotation, and proposes a modified image matching method based on analysis of data reliability for matching, this method can eliminate the interference from invalid data and improve the image matching performance. Experiments show that this method has better performance in accuracy.

Robot View Navigation Based on Level-Divided Strategy

2013 ◽  
Vol 753-755 ◽  
pp. 3108-3111
Author(s):  
Yin Bing Li
Keyword(s):  
Genetic Algorithm ◽  
Real Time ◽  
Image Matching ◽  
Search Strategy ◽  
Detection Algorithm ◽  
Adaptive Genetic Algorithm ◽  
Exact Matching ◽  
Matching Accuracy ◽  
Similarity Detection ◽  
Time Requirements

In allusion to the colored image matching characteristic in the system of robot view navigation, SSDA (the sequential similarity detection algorithm) is improved and adaptive genetic algorithm is brought in; meanwhile, level-divided search strategy connective with rough and exact matching. The improved algorithm can enhance the image matching speed with no matching accuracy reduced, so that real-time requirements of robot view navigation can be met and robot view navigation will be of preferable robustness.

scholarly journals Retinal Biometric System using Electromagnetism-like Optimization Algorithm

2019 ◽  
Vol 8 (2S5) ◽  
pp. 7-12
Keyword(s):  
Optimization Algorithm ◽  
Image Matching ◽  
Distance Measure ◽  
Biometric System ◽  
Matching Accuracy ◽  
Segmented Image ◽  
Behavioural Characteristics ◽  
Segmentation Accuracy ◽  
The Individual ◽  
Retinal Fundus

Biometric system is the technology used for the purpose of identifying the physiological and behavioural characteristics of an individual as input, analyzes it and identifies the individual as a genuine or imposter. Among all biometrics, retina based identification is perceived as a robust, unforgeable and reliable form of biometric solution. The blood vasculatures of retina are unique and used as features for retinal biometric system. In this work, an attempt has been made to employ an Electromagnetism-like Optimization Algorithm (EMOA) with Otsu Multilevel Thresholding (MLT) for segmentation of vascular pattern from the retinal fundus images for retinal biometric system. Retinal images are taken from the publicly available database such as DRIVE, STARE and HRF. The original images are subjected to preprocessing. Segmentation is carried out on the preprocessed images using EMOA Based Otsu MLT. This method provides comparatively better segmentation accuracy of 0.974 than other existing methods. Texture and vessel features are extracted from the segmented image. Matching is done between query and enrolled images using Euclidian distance measure. Decision is made using best matched image. This biometric system shows matching accuracy of 97%. Hence, this method could be recommended for retinal biometric system.

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