A Rapid Matching Algorithm Based on Filtering Feature Points

2011 ◽  
Vol 66-68 ◽  
pp. 1954-1959
Author(s):  
Hong Bo Zhu ◽  
Xue Jun Xu ◽  
Xue Song Chen ◽  
Shao Hua Jiang
Keyword(s):  
Image Registration ◽  
Feature Vector ◽  
High Dimensional ◽  
Feature Points ◽  
Matching Method ◽  
Registration Accuracy ◽  
Matching Algorithm ◽  
Feature Vectors ◽  
Vast Amount

Matching feature points is an important step in image registration. For high- dimensional feature vector, the process of matching is very time-consuming, especially matching the vast amount of points. In the premise of ensuring the registration, filtering the candidate vectors to reduce the number of feature vectors, can effectively reduce the time matching the vectors. This paper presents a matching algorithm based on filtering the feature points on their characteristics of the corner feature. The matching method can effectively improve the matching speed, and can guarantee registration accuracy as well.

A Multi-Gradation Stereo Matching Method Based on Edge Feature Points

2013 ◽  
Vol 748 ◽  
pp. 624-628
Author(s):  
Zhu Lin Li
Keyword(s):  
Object Recognition ◽  
Stereo Matching ◽  
Three Dimensional ◽  
Feature Points ◽  
Image Pair ◽  
Three Dimensional Reconstruction ◽  
Matching Method ◽  
Matching Algorithm ◽  
Dimensional Reconstruction ◽  
Edge Feature

A gradation stereo matching algorithm based on edge feature points was proposed. Its basic idea is: firstly edge feature points of image pair were extracted; then, gradient invariability and singular eigenvalue invariability were analyzed, two-grade stereo matching method was build, foundation matrix was solved further, and three-grade stereo matching algorithm was finished by foundation matrix guidance. The result indicates that the algorithm can improve matching precision, from 58.3% to 73.2%, it is simple and utility, and it is important for object recognition, tracking, and three-dimensional reconstruction.

scholarly journals Feature-Based Retinal Image Registration Using D-Saddle Feature

2017 ◽  
Vol 2017 ◽  
pp. 1-15 ◽  
Author(s):  
Roziana Ramli ◽  
Mohd Yamani Idna Idris ◽  
Khairunnisa Hasikin ◽  
Noor Khairiah A. Karim ◽  
Ainuddin Wahid Abdul Wahab ◽  
...  
Keyword(s):  
Image Registration ◽  
Retinal Image ◽  
Retinal Diseases ◽  
Feature Points ◽  
Registration Accuracy ◽  
Gaussian Pyramid ◽  
Difference Of Gaussian ◽  
Lower Success Rate ◽  
Feature Based ◽  
Image Pairs

Retinal image registration is important to assist diagnosis and monitor retinal diseases, such as diabetic retinopathy and glaucoma. However, registering retinal images for various registration applications requires the detection and distribution of feature points on the low-quality region that consists of vessels of varying contrast and sizes. A recent feature detector known as Saddle detects feature points on vessels that are poorly distributed and densely positioned on strong contrast vessels. Therefore, we propose a multiresolution difference of Gaussian pyramid with Saddle detector (D-Saddle) to detect feature points on the low-quality region that consists of vessels with varying contrast and sizes. D-Saddle is tested on Fundus Image Registration (FIRE) Dataset that consists of 134 retinal image pairs. Experimental results show that D-Saddle successfully registered 43% of retinal image pairs with average registration accuracy of 2.329 pixels while a lower success rate is observed in other four state-of-the-art retinal image registration methods GDB-ICP (28%), Harris-PIIFD (4%), H-M (16%), and Saddle (16%). Furthermore, the registration accuracy of D-Saddle has the weakest correlation (Spearman) with the intensity uniformity metric among all methods. Finally, the paired t-test shows that D-Saddle significantly improved the overall registration accuracy of the original Saddle.

scholarly journals A fast template matching algorithm based on principal orientation difference

2018 ◽  
Vol 15 (3) ◽  
pp. 172988141877822 ◽  
Author(s):  
Jichao Jiao ◽  
Xin Wang ◽  
Zhongliang Deng ◽  
Jichang Cao ◽  
Weihua Tang
Keyword(s):  
Real Time ◽  
Template Matching ◽  
Feature Points ◽  
Matching Method ◽  
Matching Algorithm ◽  
Training Samples ◽  
Illumination Changes ◽  
Orientation Difference ◽  
Direction Information ◽  
State Of Art

In the case that the background scene is dense map regularization complex and the detected objects are low texture, the method of matching according to the feature points is not applicable. Usually, the template matching method is used. When training samples are insufficient, the template matching method gets a worse detection result. In order to resolve the problem stably in real time, we propose a fast template matching algorithm based on the principal orientation difference feature. The algorithm firstly obtains the edge direction information by comparing the images that are binary. Then, the template area is divided where the different features are extracted. Finally, the matching positions are searched around the template. Experiments on the videos whose speed is 30 frames/s show that our algorithm detects the low-texture objects in real time with a matching rate of 95%. Compared with other state-of-art methods, our proposed method reduces the training samples significantly and is more robust to the illumination changes.

Image Registration Based on Feature Points Krawtchouk Moments

2010 ◽  
Vol 40-41 ◽  
pp. 584-589
Author(s):  
Fan Hui ◽  
Hai Feng Wang ◽  
Jin Jiang Li
Keyword(s):  
Image Registration ◽  
Euclidean Distance ◽  
Moment Invariants ◽  
Shape Descriptors ◽  
Feature Points ◽  
Orthogonal Moments ◽  
Feature Vectors ◽  
Krawtchouk Moments ◽  
Discrete Orthogonal ◽  
Scaling And Translation

An image registration based on feature points Krawtchouk moments is proposed. Moments are the shape descriptors based on region. Krawtchouk moments are a set of discrete orthogonal moments and are more suitable for describing two-dimensional images compared to Zemike, Legendre moments. In the image registration based on feature points Krawtchouk moments, Krawtchouk moment invariants of the feature points neighborhood that have been extracted are solved, and then these Krawtchouk moment invariants constitute feature vectors used to describe the feature points, finally feature points are matched by calculating the Euclidean distance of feature vectors. The results of experiments show that Krawtchouk moment is simple and effective to describe image and is independent of rotation, scaling, and translation of the image.

Registration and Fusion of UAV LiDAR System Sequence Images and Laser Point Clouds

2020 ◽  
Author(s):  
Jiayong Yu ◽  
Longchen Ma ◽  
Maoyi Tian, ◽  
Xiushan Lu
Keyword(s):  
Point Cloud ◽  
Image Data ◽  
Point Clouds ◽  
Optical Image ◽  
Point Cloud Data ◽  
Feature Points ◽  
Lidar System ◽  
Registration Accuracy ◽  
Cloud Data ◽  
Intensity Image

The unmanned aerial vehicle (UAV)-mounted mobile LiDAR system (ULS) is widely used for geomatics owing to its efficient data acquisition and convenient operation. However, due to limited carrying capacity of a UAV, sensors integrated in the ULS should be small and lightweight, which results in decrease in the density of the collected scanning points. This affects registration between image data and point cloud data. To address this issue, the authors propose a method for registering and fusing ULS sequence images and laser point clouds, wherein they convert the problem of registering point cloud data and image data into a problem of matching feature points between the two images. First, a point cloud is selected to produce an intensity image. Subsequently, the corresponding feature points of the intensity image and the optical image are matched, and exterior orientation parameters are solved using a collinear equation based on image position and orientation. Finally, the sequence images are fused with the laser point cloud, based on the Global Navigation Satellite System (GNSS) time index of the optical image, to generate a true color point cloud. The experimental results show the higher registration accuracy and fusion speed of the proposed method, thereby demonstrating its accuracy and effectiveness.

scholarly journals Detection of vessel bifurcations in CT scans for automatic objective assessment of deformable image registration accuracy

2021 ◽  
Author(s):  
Guillaume Cazoulat ◽  
Brian M Anderson ◽  
Molly M McCulloch ◽  
Bastien Rigaud ◽  
Eugene J Koay ◽  
...  
Keyword(s):  
Image Registration ◽  
Objective Assessment ◽  
Deformable Image Registration ◽  
Ct Scans ◽  
Registration Accuracy ◽  
Vessel Bifurcations

An Image Matching Algorithm Based on SIFT and Invariability of Feature Points Set

2011 ◽  
Vol 121-126 ◽  
pp. 701-704
Author(s):  
Xue Tong Wang ◽  
Yao Xu ◽  
Feng Gao ◽  
Jing Yi Bai
Keyword(s):  
Image Matching ◽  
Singular Value ◽  
Feature Points ◽  
Matching Algorithm ◽  
Candidate Feature

Feature points can be used to match images. Candidate feature points are extracted through SIFT firstly. Then feature points are selected from candidate points through singular value decomposing. Distance between feature points sets is computed According to theory of invariability of feature points set, images are matched if the distance is less than a threshold. Experiment showed that this algorithm is available.

scholarly journals Handwritten tifinagh character recognition using simple geometric shapes and graphs

2019 ◽  
Vol 13 (2) ◽  
pp. 598
Author(s):  
Youssef Ouadid ◽  
Abderrahmane Elbalaoui ◽  
Mehdi Boutaounte ◽  
Mohamed Fakir ◽  
Brahim Minaoui
Keyword(s):  
Character Recognition ◽  
Graph Matching ◽  
Recognition Rate ◽  
Recognition System ◽  
Features Extraction ◽  
Experimental Results ◽  
Feature Points ◽  
Matching Method ◽  
Extraction Algorithm ◽  
Novel Algorithm

<p>In this paper, a graph based handwritten Tifinagh character recognition system is presented. In preprocessing Zhang Suen algorithm is enhanced. In features extraction, a novel key point extraction algorithm is presented. Images are then represented by adjacency matrices defining graphs where nodes represent feature points extracted by a novel algorithm. These graphs are classified using a graph matching method. Experimental results are obtained using two databases to test the effectiveness. The system shows good results in terms of recognition rate.</p>

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