US 12,223,668 B2
Contour shape recognition method
Jianyu Yang, Jiangsu (CN); Ruipeng Min, Jiangsu (CN); and Yao Huang, Jiangsu (CN)
Assigned to SOOCHOW UNIVERSITY, Suzhou Jiangsu (CN)
Appl. No. 17/790,020
Filed by SOOCHOW UNIVERSITY, Jiangsu (CN)
PCT Filed May 13, 2021, PCT No. PCT/CN2021/093615
§ 371(c)(1), (2) Date Jun. 29, 2022,
PCT Pub. No. WO2022/028031, PCT Pub. Date Feb. 10, 2022.
Claims priority of application No. 202010777341.5 (CN), filed on Aug. 5, 2020.
Prior Publication US 2023/0047131 A1, Feb. 16, 2023
Int. Cl. G06T 7/50 (2017.01); G06T 7/62 (2017.01); G06V 10/46 (2022.01); G06V 10/74 (2022.01); G06V 10/764 (2022.01); G06V 10/82 (2022.01)
CPC G06T 7/50 (2017.01) [G06T 7/62 (2017.01); G06V 10/462 (2022.01); G06V 10/761 (2022.01); G06V 10/764 (2022.01); G06V 10/82 (2022.01); G06T 2207/10024 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20164 (2013.01)] 8 Claims
OG exemplary drawing
 
1. A contour shape recognition method, comprising the following steps:
step 1, sampling and extracting salient feature points of a contour of a shape sample;
step 2, calculating a shape feature function of the shape sample at a semi-global scale by using three types of shape descriptors;
step 3, dividing the scale with a single pixel as a spacing to acquire a shape feature function in a full-scale space;
step 4, storing shape feature function values at various scales into a matrix to acquire three types of shape feature grayscale map representations of the shape sample in the full-scale space;
step 5, synthesizing the three types of shape feature grayscale map representations of the shape sample, as three channels of RGB, into a color feature representation image;
step 6, constructing a two-stream convolutional neural network by taking the shape sample and the color feature representation image as inputs at the same time; and
step 7, training the two-stream convolutional neural network, and inputting a test sample into a trained network model to achieve classified recognition of the contour shape.