The Feature Extraction and Matching Algorithm Based on the Fire Video Image Orientation

2013 ◽  
Vol 380-384 ◽  
pp. 3986-3989
Author(s):  
Ying Lu ◽  
Hui Qin Wang ◽  
Fei Xu ◽  
Wei Guang Liu
Keyword(s):  
Scale Space ◽  
Video Image ◽  
Feature Point ◽  
Image Feature ◽  
Feature Points ◽  
Scale Invariant ◽  
Match Rate ◽  
Harris Corner ◽  
Computationally Intensive ◽  
Fire Flame

Because the SIFT (scale invariant feature transform) algorithm can not accurately locate the flame shape features and computationally intensive, this article proposed a stereo video image fire flame matching method which is a combination of Harris corner and SIFT algorithm. Firstly, the algorithm extracts image feature points using Harris operator in Gaussian scale space and defines the main directions for each feature point, and then calculates the 32-dimensional feature vectors of each feature point descriptor and the Euclidean distance to match two images. Experimental results of image matching demonstrate that the new algorithm improves the significance of the shape of the extracted feature points and keep a better match rate of 96%. At the same time the time complexity is reduced by 27.8%. This algorithm has a certain practicality.

Image Feature Matching Based on SIFT Algorithm

2014 ◽  
Vol 644-650 ◽  
pp. 4157-4161
Author(s):  
Xin Zhang ◽  
Ya Sheng Zhang ◽  
Hong Yao
Keyword(s):  
Image Matching ◽  
Feature Matching ◽  
Quadratic Function ◽  
Scale Space ◽  
Feature Point ◽  
Image Feature ◽  
Three Dimension ◽  
Feature Points ◽  
Gradient Distribution ◽  
Sift Feature

In the process of image matching, it is involved such as image rotation, scale zooming, brightness change and other problems. In order to improve the precision of image matching, image matching algorithm based on SIFT feature point is proposed. First, the method of generating scale space is introduced. Then, the scale and position of feature points are determined through three dimension quadratic function and feature vectors are determined through gradient distribution characteristic of neighborhood pixels of feature points. Finally, feature matching is completed based on the Euclidean distance. The experiment result indicates that using SIFT feature point can achieve image matching effectively.

scholarly journals A Registration Scheme for Multispectral Systems Using Phase Correlation and Scale Invariant Feature Matching

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Hanlun Li ◽  
Aiwu Zhang ◽  
Shaoxing Hu
Keyword(s):  
Phase Correlation ◽  
Feature Point ◽  
Reference Image ◽  
Feature Points ◽  
Scale Invariant ◽  
Matching Problem ◽  
Invariant Feature ◽  
Significant Difference ◽  
Scale Invariant Feature ◽  
Band Image

In the past few years, many multispectral systems which consist of several identical monochrome cameras equipped with different bandpass filters have been developed. However, due to the significant difference in the intensity between different band images, image registration becomes very difficult. Considering the common structural characteristic of the multispectral systems, this paper proposes an effective method for registering different band images. First we use the phase correlation method to calculate the parameters of a coarse-offset relationship between different band images. Then we use the scale invariant feature transform (SIFT) to detect the feature points. For every feature point in a reference image, we can use the coarse-offset parameters to predict the location of its matching point. We only need to compare the feature point in the reference image with the several near feature points from the predicted location instead of the feature points all over the input image. Our experiments show that this method does not only avoid false matches and increase correct matches, but also solve the matching problem between an infrared band image and a visible band image in cases lacking man-made objects.

Equi-affine differential invariants for invariant feature point detection

2019 ◽  
Vol 31 (2) ◽  
pp. 277-296
Author(s):  
STANLEY L. TUZNIK ◽  
PETER J. OLVER ◽  
ALLEN TANNENBAUM
Keyword(s):  
Feature Point ◽  
Image Feature ◽  
Differential Invariants ◽  
Feature Points ◽  
Affine Invariant ◽  
Moving Frames ◽  
Feature Detectors ◽  
Point Detection ◽  
Point Detector ◽  
Method Of Moving Frames

Image feature points are detected as pixels which locally maximise a detector function, two commonly used examples of which are the (Euclidean) image gradient and the Harris–Stephens corner detector. A major limitation of these feature detectors is that they are only Euclidean-invariant. In this work, we demonstrate the application of a 2D equi-affine-invariant image feature point detector based on differential invariants as derived through the equivariant method of moving frames. The fundamental equi-affine differential invariants for 3D image volumes are also computed.

SIFT-Based Target Recognition in Robot Soccer

2016 ◽  
Vol 693 ◽  
pp. 1419-1427 ◽  
Author(s):  
Yu Hong Du ◽  
Chen Wu ◽  
Di Zhao ◽  
Yun Chang ◽  
Xing Li ◽  
...  
Keyword(s):  
Target Recognition ◽  
Extreme Points ◽  
Scale Space ◽  
Feature Points ◽  
Scale Invariant ◽  
Robot Soccer ◽  
Sift Algorithm ◽  
Sift Feature ◽  
Soccer Game ◽  
Scale Invariant Feature

A novel scale-invariant feature transform (SIFT) algorithm is proposed for soccer target recognition application in a robot soccer game. First, the method of generating scale space is given, extreme points are detected. This gives the precise positioning of the extraction step and the SIFT feature points. Based on the gradient and direction of the feature point neighboring pixels, a description of the key points of the vector is generated. Finally, the matching method based on feature vectors is extracted from SIFT feature points and implemented on the image of the football in a soccer game. By employing the proposed SIFT algorithm for football and stadium key feature points extraction and matching, significant increase can be achieved in the robot soccer ability to identify and locate the football.

scholarly journals Flexible point cloud matching method based on three-dimensional image feature points

2018 ◽  
Vol 10 (9) ◽  
pp. 168781401879503
Author(s):  
Haihua Cui ◽  
Wenhe Liao ◽  
Xiaosheng Cheng ◽  
Ning Dai ◽  
Changye Guo
Keyword(s):  
Point Cloud ◽  
Three Dimensional ◽  
Point Clouds ◽  
Feature Point ◽  
Image Feature ◽  
Surface Measurement ◽  
Surface Model ◽  
Feature Points ◽  
Matching Method ◽  
Dimensional Image

Flexible and robust point cloud matching is important for three-dimensional surface measurement. This article proposes a new matching method based on three-dimensional image feature points. First, an intrinsic shape signature algorithm is used to detect the key shape feature points, using a weighted three-dimensional occupational histogram of the data points within the angular space, which is a view-independent representation of the three-dimensional shape. Then, the point feature histogram is used to represent the underlying surface model properties at a point whose computation is based on the combination of certain geometrical relations between the point’s nearest k-neighbors. The two-view point clouds are robustly matched using the proposed double neighborhood constraint of minimizing the sum of the Euclidean distances between the local neighbors of the point and feature point. The proposed optimization method is immune to noise, reduces the search range for matching points, and improves the correct feature point matching rate for a weak surface texture. The matching accuracy and stability of the proposed method are verified using experiments. This method can be used for a flat surface with weak features and in other applications. The method has a larger application range than the traditional methods.

Research on the SIFT Algorithm in Image Matching

2011 ◽  
Vol 121-126 ◽  
pp. 4656-4660 ◽  
Author(s):  
Yuan Cong ◽  
Xiao Rong Chen ◽  
Yi Ting Li
Keyword(s):  
Image Matching ◽  
Feature Matching ◽  
Scale Space ◽  
Feature Point ◽  
Image Feature ◽  
Sift Algorithm ◽  
Matching Algorithm ◽  
Key Points ◽  
Point Neighborhood ◽  
Accurate Matching

SIFT feature matching algorithm is hot in the field of the currently feature matching research, its matching with the strong ability can deal with the translation, rotation, affine transformation occurring between images , and it also have a stable image feature matching ability to the images filmed at any angle. SIFT algorithm is adopted in this paper, matching feature point through the scale space , calculating the histogram of detecting feature point neighborhood of gradient direction characteristic vector and generation to SIFT eigenvector and the key points similarity measure. From different setting threshold, scale scaling, rotating, noise on the experiment, the experiment result proves this algorithm in the above aspects has good robustness, suitable for mass characteristic database of rapid, accurate matching.

Invariant Features Tracking Based on Transiently Chaotic Neural Network for Markerless Augmented Reality

2011 ◽  
Vol 181-182 ◽  
pp. 37-42
Author(s):  
Xin Yu Li ◽  
Dong Yi Chen
Keyword(s):  
Neural Network ◽  
Augmented Reality ◽  
Image Matching ◽  
Feature Matching ◽  
Feature Point ◽  
Image Feature ◽  
Chaotic Neural Network ◽  
Scale Invariant ◽  
Chaotic Neural Networks ◽  
Invariant Features

Tracking and registration of camera and object is one of the most important issues in Augmented Reality (AR) systems. Markerless visual tracking technologies with image feature are used in many AR applications. Feature point based neural network image matching method has attracted considerable attention in recent years. This paper proposes an approach to feature point correspondence of image sequence based on transient chaotic neural networks. Rotation and scale invariant features are extracted from images firstly, and then transient chaotic neural network is used to perform global feature matching and perform the initialization phase of the tracking. Experimental results demonstrate the efficiency and the effectiveness of the proposed method.

scholarly journals Semantic segmentation guided feature point classification and seam fusion for image stitching

2021 ◽  
Vol 15 ◽  
pp. 174830262110653
Author(s):  
Huafeng Huang ◽  
Fei Chen ◽  
Hang Cheng ◽  
Liyao Li ◽  
Meiqing Wang
Keyword(s):  
Semantic Segmentation ◽  
Classification Problem ◽  
Feature Point ◽  
Image Feature ◽  
Image Stitching ◽  
Feature Points ◽  
Target Image ◽  
Distance Transformation ◽  
Cutting Algorithm

Image stitching can be employed to stitch images taken from different times, perspectives, or devices into a panorama with a wider view. However, the imaging specification of images to be stitched is strict. If the imaging specification is not satisfied, artefacts caused by inaccurate alignment and unnatural distortion will occur. Semantic segmentation can solve the classification problem at the pixel level; however, image stitching significantly depends on the accuracy of feature points. Therefore, this paper proposes an image stitching algorithm based on semantic segmentation to guide feature point classification and seam fusion. First, the images are recognized by a cascade semantic segmentation network, and the image feature points are classified. Thereafter, the corresponding homography transformations are calculated using different class feature points, and the best homography mapping for the entire target image is selected. Finally, a seam-cutting algorithm based on semantic segmentation is used to compute the seam, and a feathering Poisson fusion with distance transformation is used to eliminate artefacts and light differences. Experiments show that the algorithm can generate transitional natural and perceptual stitching results even under the influence of perspective and light differences.

scholarly journals Panoramic image mosaic method based on image segmentation and Improved SIFT algorithm

2021 ◽  
Vol 2113 (1) ◽  
pp. 012066
Author(s):  
Lei Zhuang ◽  
Jiyan Yu ◽  
Yang Song
Keyword(s):  
Image Segmentation ◽  
Feature Point ◽  
Image Feature ◽  
Feature Descriptor ◽  
Image Mosaic ◽  
Panoramic Image ◽  
Feature Points ◽  
Sift Algorithm ◽  
Original Algorithm ◽  
Image Pairs

Abstract Aiming at the problem of large amount of calculation in extracting image feature points in panoramic image mosaic by SIFT algorithm, a panoramic image mosaic algorithm based on image segmentation and Improved SIFT is proposed in this paper. The algorithm fully considers the characteristics of panoramic image stitching. Firstly, the stitched image is divided into blocks, and the maximum overlapping block of image pairs is extracted by using mutual information. The SIFT key points are extracted by SIFT algorithm, and the dog is filtered before the spatial extreme value detection of SIFT algorithm to eliminate the feature points with small intensity value; When establishing the feature descriptor, the 128 dimension of the original algorithm is reduced to 64 dimensions to reduce the amount of calculation. In the feature point registration process, the feature descriptor is reduced to 32 dimensions, the feature point pairs are roughly extracted by the optimal node first BBF algorithm, and the feature point pairs are registered and screened by RANSAC; Finally, the image transformation matrix is obtained to realize panoramic image mosaic. The experimental results show that the proposed algorithm not only ensures the panoramic mosaic effect, but also extracts the feature points in 11% of the time of the traditional SIFT algorithm, and the feature point registration speed is 27.17% of the traditional SIFT algorithm.

scholarly journals Feature Point Extraction from the Local Frequency Map of an Image

2012 ◽  
Vol 2012 ◽  
pp. 1-15 ◽  
Author(s):  
Jesmin Khan ◽  
Sharif Bhuiyan ◽  
Reza Adhami
Keyword(s):  
Frequency Estimation ◽  
Detection Algorithm ◽  
Feature Point ◽  
Feature Points ◽  
Local Phase ◽  
Interest Points ◽  
Scale Invariant ◽  
Suggested Technique ◽  
Frequency Map ◽  
Interest Point Detector

We propose a novel technique for detecting rotation- and scale-invariant interest points from the local frequency representation of an image. Local or instantaneous frequency is the spatial derivative of the local phase, where the local phase of any signal can be found from its Hilbert transform. Local frequency estimation can detect edge, ridge, corner, and texture information at the same time, and it shows high values at those dominant features of an image. For each pixel, we select an appropriate width of the window for computing the derivative of the phase. In order to select the width of the window for any given pixel, we make use of the measure of the extent to which the phases, in the neighborhood of that pixel, are in the same direction. The local frequency map, thus obtained, is then thresholded by employing a global thresholding approach to detect the interest or feature points. Repeatability rate, a performance evaluation criterion for an interest point detector, is used to check the geometric stability of the proposed method under different transformations. We present simulation results of the detection of feature points from image utilizing the suggested technique and compare the proposed method with five existing approaches that yield good results. The results prove the efficacy of the proposed feature point detection algorithm. Moreover, in terms of repeatability rate; the results show that the performance of the proposed method with respect to different aspect is compatible with the existing methods.

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