Vibe-based three-dimensional sonar point cloud image segmentation method

Patent metadata

Field	Value
Publication number	US-2021049769-A1
Application number	US-201816980390-A
Country	US
Kind code	A1
Filing date	Oct 22, 2018
Priority date	Mar 14, 2018
Publication date	Feb 18, 2021
Grant date	—

Abstract

The present invention discloses a ViBe-based three-dimensional sonar point cloud image segmentation method, characterized by including: (1) acquiring sonar data, and converting three-dimensional sonar depth image data corresponding to each frame of the sonar data to point cloud data under an orthogonal coordinate system; (2) sampling the point cloud data, and down-sampling the point cloud data to a plurality of adjacent voxels with side length being R by taking a variable resolution R as a function; (3) carrying out image segmentation on the down-sampled point cloud data by a ViBe algorithm; (4) carrying out accumulative scoring on each voxel according to an image segmentation result, and sorting foreground data and background data according to accumulative score; and (5) clustering the foreground data, and then carrying out expansion operation on the foreground data by taking an original point in the voxel as a center to obtain final foreground point cloud data.

First claim

1 . A Visual Background Subtraction (ViBe) based three-dimensional sonar point cloud image segmentation method, characterized by comprising the following steps of:
(1) acquiring sonar data, and converting three-dimensional sonar depth image data corresponding to each frame of the sonar data to point cloud data under an orthogonal coordinate system; (2) sampling the point cloud data, and down-sampling the point cloud data to a plurality of adjacent voxels with side length being R by taking a variable resolution R as a function; (3) carrying out image segmentation on the down-sampled point cloud data by a ViBe algorithm; (4) carrying out accumulative scoring on each voxel according to an image segmentation result; sorting foreground data and background data according to accumulative score; and (5) clustering the foreground data, and then carrying out expansion operation on the foreground data by taking an original point in the voxel as a center to obtain final foreground point cloud data.

2 . The ViBe-based three-dimensional sonar point cloud image segmentation method according to claim 1 , wherein total point cloud data is directly down-sampled to obtain a plurality of adjacent voxels with side length being R.

3 . The ViBe-based three-dimensional sonar point cloud image segmentation method according to claim 1 , wherein a part of point cloud data is sequentially and segmentally down-sampled to obtain a plurality of adjacent voxels with side length being R.

4 . The ViBe-based three-dimensional sonar point cloud image segmentation method according to claim 1 , wherein the step (3) comprises the following steps of:
(3-1) for each voxel, randomly selecting n points as a sample space of the voxel in N-neighborhood counting on each voxel of the frame and a sample space of the same coordinate corresponding to a point cloud image of the frame, adding 1 to a fitting counter C 1 when the intensity difference between the voxel and a point in the sample space is smaller than a threshold T 1 , considering the voxel as the background data if the whole sample space is compared and the C 1 is larger than or equal to the threshold T 2 , otherwise considering the background data as pre-foreground data; (3-3) during segmenting each frame of point cloud image, changing the voxel to the background data if one foreground point is judged as the pre-foreground data at times exceeding a threshold T 3 ; and (3-4) updating one point of the sample space of the corresponding coordinate if one voxel is the background data and occurs with probability of 1/Alpha, and updating one point of the sample space of one own neighborhood if the probability of 1/Alpha exits.

5 . The ViBe-based three-dimensional sonar point cloud image segmentation method according to claim 1 , wherein in the step (4), if one voxel, in the calculating process, is judged as the pre-foreground data at times not less than Si times when the step (3) is ended, the voxel is judged as the foreground data, otherwise the voxel is judged as the background data.

6 . The ViBe-based three-dimensional sonar point cloud image segmentation method according to claim 1 , wherein in the step (5), the nearest neighbor-clustering method is adopted to cluster the foreground data to obtain one or more voxel sets, and a distance threshold T 4 of clustering is 1.0-20.0.

7 . The ViBe-based three-dimensional sonar point cloud image segmentation method according to claim 1 , wherein in the step (5), an expansion operation with expansion kernels being E 1 , E 2 , and E 3 is carried out on the foreground data after clustered results are reflected onto the original point in the voxel, and values of E 1 , E 2 , and E 3 are all odd numbers from 1 to 21.

8 . The ViBe-based three-dimensional sonar point cloud image segmentation method according to claim 4 , wherein a value of threshold T 1 is 0.01-10,000.00, a value of the threshold T 2 is from 1 to point numbers of the sample space, and a value of the threshold T 3 is 10-100.

9 . The ViBe-based three-dimensional sonar point cloud image segmentation method according to claim 6 , wherein in the step (5), an expansion operation with expansion kernels being E 1 , E 2 , and E 3 is carried out on the foreground data after clustered results are reflected onto the original point in the voxel, and values of E 1 , E 2 , and E 3 are all odd numbers from 1 to 21.

Assignees

• Univ Zhejiang

Inventors

• Chen Yaowu
• Tian Xiang
• Liu Xuesong
• Jiang Rongxin

Classifications

• G06T7/136 Primary

  involving thresholding · CPC title

• G06V10/28

  Quantising the image, e.g. histogram thresholding for discrimination between background and foreground patterns · CPC title

• G06V20/64

  Three-dimensional [3D] objects · CPC title

• G06T7/194 Primary

  involving foreground-background segmentation · CPC title

• G06F18/23

  Clustering techniques · CPC title