scholarly journals A novel method for SIFT features matching based on feature dimension matching degree

2019 ◽  
Vol 277 ◽  
pp. 02027
Author(s):  
Yao Yang ◽  
Jinkang Wei ◽  
Ximing Zhan ◽  
Xikui Miao
Keyword(s):  
Feature Vector ◽  
Threshold Value ◽  
Feature Point ◽  
Feature Descriptor ◽  
Feature Points ◽  
Sift Feature ◽  
Novel Method ◽  
Matching Degree ◽  
Feature Dimension ◽  
Vector Element

We proposes a method for fast matching SIFT feature points based on SIFT feature descriptor vector element matching. First, we discretize each dimensional feature element into an array address based on a fixed threshold value and store the corresponding feature point labels in an address. If the same dimensional feature element of the descriptor vector has the same discrete value, their feature point labels may fall into the same address. Secondly, we search the mapping address of the feature descriptor vector element to obtain the matching state of the corresponding dimensions of the feature descriptor vector, thus obtaining the number of dimensions matching between feature points and feature dimension matching degree. Then we use the feature dimension matching degree to obtain the suspect matching feature points. Finally we use the Euclidean distance to eliminate the mismatching feature points to obtain accurate matching feature point pairs. The method is essentially a high-dimensional feature vector matching method based on local feature vector element matching. Experimental results show that the new algorithm can guarantee the number of matching SIFT feature points and their matching accuracy and that its running time is similar to that of HKMT, RKDT and LSH algorithms

A New Method for Feature Point Matching: Inner and Exterior Product

2011 ◽  
Vol 48-49 ◽  
pp. 79-83
Author(s):  
Xu Guang Wang ◽  
Li Jun Lin ◽  
Hai Yan Cheng
Keyword(s):  
Feature Point ◽  
Feature Descriptor ◽  
Feature Points ◽  
Correlation Measure ◽  
Exterior Product ◽  
Illumination Change ◽  
Point Matching ◽  
Feature Point Matching ◽  
Estimate Precision ◽  
Better Than

In this paper, a novel feature descriptor called gradient correlation descriptor (GCD) is proposed. The GCD descriptor uses the gradient correlation measure defined by the inner and exterior product to characterize the gradient distributions in neighborhoods of feature points, and it has the following advantages: Its construction is very simple because of only the inner and exterior product operations are used; Its distinctive performance is better than the region-based SIFT descriptors since the gradient correlation measure can effectively characterize the gradient distributions in neighborhoods of feature points; In the gradient correlation measure the use of gradient mean makes it is not sensitive to the estimate precision of main orientation of feature point, and thus can provide a better stabilization to image rotation; The gradient correlation measure makes it also has very good adaptability to image affine transform, image blur, JPEG compression as well as illumination change.

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.

Non-Uniform SURF Feature Point Detection and Matching

2013 ◽  
Vol 284-287 ◽  
pp. 3184-3188
Author(s):  
Yea Shuan Huang ◽  
Zhi Hong Ou ◽  
Hung Hsiu Yu ◽  
Hsiang Wen Hsieh
Keyword(s):  
Moving Objects ◽  
Salient Feature ◽  
Feature Point ◽  
Feature Descriptor ◽  
Feature Points ◽  
Point Matching ◽  
Point Mapping ◽  
Feature Point Extraction ◽  
Feature Point Matching ◽  
Point Detection

This paper presents a method for detecting feature points from an image and locating their matching correspondence points across images. The proposed method leverages a novel rapid LBP feature point detection to filter out texture-less SURF feature points. The detected feature points, also known as Non-Uniform SURF feature points, are used to match corresponding feature points from other frame images to reliably locate positions of moving objects. The proposed method consists of two processing modules: Feature Point Extraction (FPE) and Feature Point Mapping (FPM). First, FPE extracts salient feature points with Feature Transform and Feature Point Detection. FPM is then applied to generate motion vectors of each feature point with Feature Descriptor and Feature Point Matching. Experiments are conducted on both artificial template patterns and real scenes captured from moving camera at different speed settings. Experimental results show that the proposed method outperforms the commonly-used SURF feature point detection and matching approach.

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 Automatic measurement of traditional Chinese costume from its silhouette through Fuzzy c-means clustering method

2020 ◽  
Vol 15 ◽  
pp. 155892502097832
Author(s):  
Jiaqin Zhang ◽  
Jingan Wang ◽  
Le Xing ◽  
Hui’e Liang
Keyword(s):  
Industrial Application ◽  
Clustering Algorithm ◽  
Color Space ◽  
Automatic Measurement ◽  
Feature Point ◽  
Feature Points ◽  
Point Location ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Environmental Robustness

As the precious cultural heritage of the Chinese nation, traditional costumes are in urgent need of scientific research and protection. In particular, there are scanty studies on costume silhouettes, due to the reasons of the need for cultural relic protection, and the strong subjectivity of manual measurement, which limit the accuracy of quantitative research. This paper presents an automatic measurement method for traditional Chinese costume dimensions based on fuzzy C-means clustering and silhouette feature point location. The method is consisted of six steps: (1) costume image acquisition; (2) costume image preprocessing; (3) color space transformation; (4) object clustering segmentation; (5) costume silhouette feature point location; and (6) costume measurement. First, the relative total variation model was used to obtain the environmental robustness and costume color adaptability. Second, the FCM clustering algorithm was used to implement image segmentation to extract the outer silhouette of the costume. Finally, automatic measurement of costume silhouette was achieved by locating its feature points. The experimental results demonstrated that the proposed method could effectively segment the outer silhouette of a costume image and locate the feature points of the silhouette. The measurement accuracy could meet the requirements of industrial application, thus providing the dual value of costume culture research and industrial application.

scholarly journals 3D Mesh Pre-processing Method Based on Feature Point Classification and Anisotropic Vertex Denoising Considering Scene Structure Characteristics

2021 ◽  
Vol 13 (11) ◽  
pp. 2145
Author(s):  
Yawen Liu ◽  
Bingxuan Guo ◽  
Xiongwu Xiao ◽  
Wei Qiu
Keyword(s):  
Real Data ◽  
Geometric Constraints ◽  
Feature Point ◽  
Feature Points ◽  
Denoising Method ◽  
3D Mesh ◽  
Mesh Denoising ◽  
Scene Structure ◽  
Sharp Edges ◽  
Update Strategy

3D mesh denoising plays an important role in 3D model pre-processing and repair. A fundamental challenge in the mesh denoising process is to accurately extract features from the noise and to preserve and restore the scene structure features of the model. In this paper, we propose a novel feature-preserving mesh denoising method, which was based on robust guidance normal estimation, accurate feature point extraction and an anisotropic vertex denoising strategy. The methodology of the proposed approach is as follows: (1) The dual weight function that takes into account the angle characteristics is used to estimate the guidance normals of the surface, which improved the reliability of the joint bilateral filtering algorithm and avoids losing the corner structures; (2) The filtered facet normal is used to classify the feature points based on the normal voting tensor (NVT) method, which raised the accuracy and integrity of feature classification for the noisy model; (3) The anisotropic vertex update strategy is used in triangular mesh denoising: updating the non-feature points with isotropic neighborhood normals, which effectively suppressed the sharp edges from being smoothed; updating the feature points based on local geometric constraints, which preserved and restored the features while avoided sharp pseudo features. The detailed quantitative and qualitative analyses conducted on synthetic and real data show that our method can remove the noise of various mesh models and retain or restore the edge and corner features of the model without generating pseudo features.

scholarly journals Attention-based deep learning networks for identification of human gait using radar micro-Doppler spectrograms

2021 ◽  
pp. 1-6
Author(s):  
Hannah Garcia Doherty ◽  
Roberto Arnaiz Burgueño ◽  
Roeland P. Trommel ◽  
Vasileios Papanastasiou ◽  
Ronny I. A. Harmanny
Keyword(s):  
Neural Networks ◽  
Feature Vector ◽  
Classification Performance ◽  
Input Image ◽  
Human Gait ◽  
Learning Networks ◽  
Class Label ◽  
Deep Convolutional Neural Networks ◽  
Network Layers ◽  
Feature Dimension

Abstract Identification of human individuals within a group of 39 persons using micro-Doppler (μ-D) features has been investigated. Deep convolutional neural networks with two different training procedures have been used to perform classification. Visualization of the inner network layers revealed the sections of the input image most relevant when determining the class label of the target. A convolutional block attention module is added to provide a weighted feature vector in the channel and feature dimension, highlighting the relevant μ-D feature-filled areas in the image and improving classification performance.

scholarly journals Identification of the Disrupted Pathways Associated with Periodontitis Based on Human Pathway Network

2016 ◽  
Vol 5 (4) ◽  
pp. 93-98
Author(s):  
Wen Sun ◽  
Lin Han ◽  
Wenmao Xu ◽  
Yazhen Sun
Keyword(s):  
Protein Interactions ◽  
Threshold Value ◽  
Normal Subjects ◽  
Protein Protein Interactions ◽  
Pathway Network ◽  
Differential Expression Genes ◽  
Weight Value ◽  
Limma Package ◽  
Novel Method ◽  
Cognitively Normal Subjects

AbstractObjective: The objective of this work is to search for a novel method to explore the disrupted pathways associated with periodontitis (PD) based on the network level.Methods: Firstly, the differential expression genes (DEGs) between PD patients and cognitively normal subjects were inferred based on LIMMA package. Then, the protein-protein interactions (PPI) in each pathway were explored by Empirical Bayesian (EB) co-expression program. Specifically, we determined the 100th weight value as the threshold value of the disrupted pathways of PPI by constructing the randomly model and confirmed the weight value of each pathway. Meanwhile, we dissected the disrupted pathways under the weight value > the threshold value. Pathways enrichment analyses of DEGs were carried out based on Expression Analysis Systematic Explored (EASE) test. Finally, the better method was selected based on the more rich and significant obtained pathways by comparing the two methods.Results: After the calculation of LIMMA package, we estimated 524 DEGs in all. Then we determined 0.115222 as the threshold value of the disrupted pathways of PPI. When the weight value>0.115222, there were 258 disrupted pathways of PPI enriched in. Additionally, we observed those 524 DEGs that were enriched in 4 pathways under EASE=0.1.Conclusion: We proposed a novel network method inferring the disrupted pathway for PD. The disrupted pathways might be underlying biomarkers for treatment associated with PD.

scholarly journals AN INTEGRATED RANSAC AND GRAPH BASED MISMATCH ELIMINATION APPROACH FOR WIDE-BASELINE IMAGE MATCHING

2015 ◽  
Vol XL-1-W5 ◽  
pp. 297-300 ◽  
Author(s):  
M. Hasheminasab ◽  
H. Ebadi ◽  
A. Sedaghat
Keyword(s):  
Image Matching ◽  
Graph Transformation ◽  
Feature Point ◽  
Consensus Method ◽  
Feature Descriptor ◽  
Range Image ◽  
Point Sets ◽  
Short Baseline ◽  
Epipolar Constraint ◽  
Baseline Image

In this paper we propose an integrated approach in order to increase the precision of feature point matching. Many different algorithms have been developed as to optimizing the short-baseline image matching while because of illumination differences and viewpoints changes, wide-baseline image matching is so difficult to handle. Fortunately, the recent developments in the automatic extraction of local invariant features make wide-baseline image matching possible. The matching algorithms which are based on local feature similarity principle, using feature descriptor as to establish correspondence between feature point sets. To date, the most remarkable descriptor is the scale-invariant feature transform (SIFT) descriptor , which is invariant to image rotation and scale, and it remains robust across a substantial range of affine distortion, presence of noise, and changes in illumination. The epipolar constraint based on RANSAC (random sample consensus) method is a conventional model for mismatch elimination, particularly in computer vision. Because only the distance from the epipolar line is considered, there are a few false matches in the selected matching results based on epipolar geometry and RANSAC. Aguilariu et al. proposed Graph Transformation Matching (GTM) algorithm to remove outliers which has some difficulties when the mismatched points surrounded by the same local neighbor structure. In this study to overcome these limitations, which mentioned above, a new three step matching scheme is presented where the SIFT algorithm is used to obtain initial corresponding point sets. In the second step, in order to reduce the outliers, RANSAC algorithm is applied. Finally, to remove the remained mismatches, based on the adjacent K-NN graph, the GTM is implemented. Four different close range image datasets with changes in viewpoint are utilized to evaluate the performance of the proposed method and the experimental results indicate its robustness and capability.

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