3D SIFT aided path independent digital volume correlation and its GPU acceleration

Yang, Junrong; Huang, Jianwen; Jiang, Zhenyu; Dong, Sujun; Tang, Liqun; Liu, Yiping; Liu, Zejia; Zhou, Licheng

doi:10.1016/j.optlaseng.2020.106323

Cited by 21 publications

(6 citation statements)

References 51 publications

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“…Each voxel is compared with those of its neighbors to find the extremum in DOG. The neighborhoods is only 6 neighbors sharing the face with the interrogated cube, and the two cubes located at the same position in the adjacent DoG scales counts 17 . Some extreme points are unstable and will be filtered.…”

Section: Methodsmentioning

confidence: 99%

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Point cloud registration of arrester based on scale-invariant points feature histogram

Wen

Chen

Hou

et al. 2022

Sci Rep

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Arrester is an important lightning protection device in the electrical field. The parameters of arrester such as creepage distance, umbrella distance and diameter are important for product quality, but they are difficult to measure because the shape of arrester is irregular. However, the three-dimensional (3D) reconstruction technique is efficient in measuring arrester parameters. The uniform distributed structure of umbrella skirt on the arrester surface restrict the registration of point cloud. In this paper, a scale-invariant points features histogram (SIPFH) descriptor is proposed; the descriptor combines the characteristics of Scale-invariant Feature Transform (SIFT) and fast point feature histogram (FPFH). Moreover, the improved Levenberg-Marquardt (LM) algorithm is presented, the maximum distance of corresponding points in the iterative process is adjusted to realize the local optimization. The point cloud registration method consists of two parts: primary registration method based on SIPFH, and secondary registration method based on improved LM algorithm. Point clouds of different arresters are collected to establish datasets, some of which have interference. Experimental results indicate that the root mean square error of the method is less than 0.02 m; the average running time is 2.7 s, which is $$41.4\%$$ 41.4 % of the conventional method based on FPFH.

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Section: Methodsmentioning

confidence: 99%

“…These extreme points are approximately equal to the keypoints. Then, the direction of the keypoint is calculated to realize the rotation invariance of 3D objects 17 , the calculation formula is shown in Eq. ( 3 ).…”

Section: Methodsmentioning

confidence: 99%

Point cloud registration of arrester based on scale-invariant points feature histogram

Wen

Chen

Hou

et al. 2022

Sci Rep

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“…is is because dropout reduces the complexity of the model through the inactivation of some neurons and suppresses the degree of overfitting of the neural network to a certain extent. Figure 7 shows that dropout improves the accuracy of behaviour recognition by about 1%, and the effect is 6 Complexity relatively not obvious. is is because the recognition rate has reached more than 90% when dropout is not used, and there is not much room for improvement, so that the effect of dropout is not significant.…”

Section: Analysis Of Dropout's Influence On the Effect Of Behaviourmentioning

confidence: 99%

“…Among them, spatial-temporal interest points have strong robustness to illumination changes, background differences, and environmental noise, feature expression is more adequate, and the recognition rate is the highest. Commonly used time and space points of interest include 3D-SIFT [6], HOG3D [7], and ESURF [8]. However, this type of feature extraction is complicated and time-consuming to match, and it is difficult to meet real-time requirements in practical applications.…”

Section: Introductionmentioning

confidence: 99%

Study of Human Motion Recognition Algorithm Based on Multichannel 3D Convolutional Neural Network

Yang

2021

Complexity

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Aiming at the problem that it is difficult to balance the speed and accuracy of human behaviour recognition, this paper proposes a method of motion recognition based on random projection. Firstly, the optical flow picture and Red, Green, Blue (RGB) picture obtained by the Lucas-Kanade algorithm are used. Secondly, the data of optical flow pictures and RGB pictures are compressed based on a random projection matrix of compressed sensing, which effectively reduces power consumption. At the same time, based on random projection compression data, it can effectively find the optimal linear representation to reconstruct training samples and test samples. Thirdly, a multichannel 3D convolutional neural network is proposed, and the multiple information extracted by the network is fused to form an output recognizer. Experimental results show that the algorithm in this paper significantly improves the recognition rate of multicategory actions and effectively reduces the computational complexity and running time of the recognition algorithm.

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“…Lowe [3][4][5] proposed the Scale Invariant Feature Transform (SIFT), which converts an image into many local feature vectors. Based on this, many pieces of research on the application and improvement of the SIFT method have been carried out [6][7][8][9][10]. In addition to the SIFT method, the Mean Shift, which is a general non-parametric technique, was introduced in 1975 [11].…”

Section: Introductionmentioning

confidence: 99%

Feature Point Matching Method for Aerial Image Based on Recursive Diffusion Algorithm

et al. 2021

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Aerial images are large-scale and susceptible to light. Traditional image feature point matching algorithms cannot achieve satisfactory matching accuracy for aerial images. This paper proposes a recursive diffusion algorithm, which is scale-invariant and can be used to extract symmetrical areas of different images. This narrows the matching range of feature points by extracting high-density areas of the image and improving the matching accuracy through correlation analysis of high-density areas. Through experimental comparison, it can be found that the recursive diffusion algorithm has more advantages compared to the correlation coefficient method and the mean shift algorithm when matching accuracy of aerial images, especially when the light of aerial images changes greatly.

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3D SIFT aided path independent digital volume correlation and its GPU acceleration

Cited by 21 publications

References 51 publications

Point cloud registration of arrester based on scale-invariant points feature histogram

Point cloud registration of arrester based on scale-invariant points feature histogram

Study of Human Motion Recognition Algorithm Based on Multichannel 3D Convolutional Neural Network

Feature Point Matching Method for Aerial Image Based on Recursive Diffusion Algorithm

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