point matching
Recently Published Documents


TOTAL DOCUMENTS

646
(FIVE YEARS 96)

H-INDEX

34
(FIVE YEARS 3)

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 417
Author(s):  
Jinlong Li ◽  
Bingren Chen ◽  
Meng Yuan ◽  
Qian Zhao ◽  
Lin Luo ◽  
...  

Establishing an effective local feature descriptor and using an accurate key point matching algorithm are two crucial tasks in recognizing and registering on the 3D point cloud. Because the descriptors need to keep enough descriptive ability against the effect of noise, occlusion, and incomplete regions in the point cloud, a suitable key point matching algorithm can get more precise matched pairs. To obtain an effective descriptor, this paper proposes a Multi-Statistics Histogram Descriptor (MSHD) that combines spatial distribution and geometric attributes features. Furthermore, based on deep learning, we developed a new key point matching algorithm that could identify more corresponding point pairs than the existing methods. Our method is evaluated based on Stanford 3D dataset and four real component point cloud dataset from the train bottom. The experimental results demonstrate the superiority of MSHD because its descriptive ability and robustness to noise and mesh resolution are greater than those of carefully selected baselines (e.g., FPFH, SHOT, RoPS, and SpinImage descriptors). Importantly, it has been confirmed that the error of rotation and translation matrix is much smaller based on our key point matching algorithm, and the precise corresponding point pairs can be captured, resulting in enhanced recognition and registration for three-dimensional surface matching.


2021 ◽  
pp. 1-7
Author(s):  
Minhui Yu ◽  
Mei Sang ◽  
Cheng Guo ◽  
Ruifeng Zhang ◽  
Fan Zhao ◽  
...  

Abstract A high-frequency short-pulsed stroboscopic micro-visual system was employed to capture the transient image sequences of a periodically in-plane working micro-electro-mechanical system (MEMS) devices. To demodulate the motion parameters of the devices from the images, we developed the feature point matching (FPM) algorithm based on Speeded-Up Robust Features (SURF). A MEMS gyroscope, vibrating at a frequency of 8.189 kHz, was used as a testing sample to evaluate the performance of the proposed algorithm. Within the same processing time, the SURF-based FPM method demodulated the velocity of the in-plane motion with a precision of 10−5 pixels of the image, which was two orders of magnitude higher than the template-matching and frame-difference algorithms.


2021 ◽  
Vol 9 (12) ◽  
pp. 1357
Author(s):  
Qinglian Hou ◽  
Cheng Zhou ◽  
Rong Wan ◽  
Junbo Zhang ◽  
Feng Xue

Tuna fish school detection provides information on the fishing decisions of purse seine fleets. Here, we present a recognition system that included fish shoal image acquisition, point extraction, point matching, and data storage. Points are a crucial characteristic for images of free-swimming tuna schools, and point algorithm analysis and point matching were studied for their applications in fish shoal recognition. The feature points were obtained by using one of the best point algorithms (scale invariant feature transform, speeded up robust features, oriented fast and rotated brief). The k-nearest neighbors (KNN) algorithm uses ‘feature similarity’ to predict the values of new points, which means that new data points will be assigned a value based on how closely they match the points that exist in the database. Finally, we tested the model, and the experimental results show that the proposed method can accurately and effectively recognize tuna free-swimming schools.


Geomatics ◽  
2021 ◽  
Vol 1 (4) ◽  
pp. 464-495
Author(s):  
Desi Suyamto ◽  
Lilik Prasetyo ◽  
Yudi Setiawan ◽  
Arief Wijaya ◽  
Kustiyo Kustiyo ◽  
...  

This article demonstrated an easily applicable method for measuring the similarity between a pair of point patterns, which applies to spatial or temporal data sets. Such a measurement was performed using similarity-based pattern analysis as an alternative to conventional approaches, which typically utilize straightforward point-to-point matching. Using our approach, in each point data set, two geometric features (i.e., the distance and angle from the centroid) were calculated and represented as probability density functions (PDFs). The PDF similarity of each geometric feature was measured using nine metrics, with values ranging from zero (very contrasting) to one (exactly the same). The overall similarity was defined as the average of the distance and angle similarities. In terms of sensibility, the method was shown to be capable of measuring, at a human visual sensing level, two pairs of hypothetical patterns, presenting reasonable results. Meanwhile, in terms of the method′s sensitivity to both spatial and temporal displacements from the hypothetical origin, the method is also capable of consistently measuring the similarity of spatial and temporal patterns. The application of the method to assess both spatial and temporal pattern similarities between two deforestation data sets with different resolutions was also discussed.


Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7610
Author(s):  
Yongji Li ◽  
Rui Wu ◽  
Zhenhong Jia ◽  
Jie Yang ◽  
Nikola Kasabov

Outdoor vision sensing systems often struggle with poor weather conditions, such as snow and rain, which poses a great challenge to existing video desnowing and deraining methods. In this paper, we propose a novel video desnowing and deraining model that utilizes the salience information of moving objects to address this problem. First, we remove the snow and rain from the video by low-rank tensor decomposition, which makes full use of the spatial location information and the correlation between the three channels of the color video. Second, because existing algorithms often regard sparse snowflakes and rain streaks as moving objects, this paper injects salience information into moving object detection, which reduces the false alarms and missed alarms of moving objects. At the same time, feature point matching is used to mine the redundant information of moving objects in continuous frames, and a dual adaptive minimum filtering algorithm in the spatiotemporal domain is proposed by us to remove snow and rain in front of moving objects. Both qualitative and quantitative experimental results show that the proposed algorithm is more competitive than other state-of-the-art snow and rain removal methods.


2021 ◽  
Author(s):  
Junchong Huang ◽  
Wei Tian ◽  
Yongkun Wen ◽  
Zhan Chen ◽  
Yuyao Huang

2021 ◽  
Author(s):  
Jian Liang ◽  
Hui Xu ◽  
QiYuan Liu ◽  
WuBin Luo ◽  
GuoYing Fen

2021 ◽  
Vol 87 (10) ◽  
pp. 767-780
Author(s):  
Min Chen ◽  
Tong Fang ◽  
Qing Zhu ◽  
Xuming Ge ◽  
Zhanhao Zhang ◽  
...  

In this study, we propose a feature-point matching method that is robust to viewpoint, scale, and illumination changes between aerial and ground images, to improve matching performance. First, a 3D rendering strategy is adopted to synthesize ground-view images from the 3D mesh model reconstructed from aerial images and overcome the global geometric distortion between aerial and ground images. We do not directly match feature points between the synthesized and ground images, but extract line-segment correspondences by designing a line-segment matching method that can adapt to the local geometric deformation, holes, and blurred textures on the synthesized image. Then, on the basis of the line-segment matches, local-region correspondences are constructed, and local regions on the synthesized image are propagated back to the original aerial images. Lastly, feature-point matching is performed between the aerial and ground images with the constraints of the local-region correspondences. Experimental results demonstrate that the proposed method can obtain more correct matches and higher matching precision than state-of-the-art methods. Specifically, the proposed method increases the average number of correct matches and average matching precision of the second-best method by more than five times and 40%, respectively.


Sign in / Sign up

Export Citation Format

Share Document