Patent ID: 11880994
Assignee: ZHEJIANG UNIVERSITY
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 0:
1. A method for determining plant leaf surface roughness, comprising:
step S1: acquiring a plurality of continuously captured zoomed-in leaf images by using a zoom microscope image capture system;
step S2: determining a feature match set according to the zoomed-in leaf images via a feature match set determining module of computer-executable instructions stored in a computer device, wherein the determining the feature match set comprises:
step S21: performing Gaussian filter de-noising on the zoomed-in leaf images executed by a Gaussian filter de-noising unit of the computer-executable instructions, to obtain a plurality of de-noised images;
step S22: determining a feature point corresponding to each pixel point in each of the de-noised images by using a Speeded Up Robust Features (SURF) algorithm executed by a feature point determining unit of the computer-executable instructions, the determining the feature point comprising the following steps:
step S221: determining a Hessian matrix corresponding to each pixel point in each of the de-noised images by using the SURF algorithm; and
step S222: determining the feature point corresponding to each pixel point according to the Hessian matrix corresponding to each pixel point; and

step S23: matching the feature points in every two adjacent de-noised images of the de-noised images, by using a violent matching algorithm executed by a feature match set determining unit of the computer-executable instructions, to obtain the feature match set, wherein the matching the feature points in every two adjacent de-noised images of the de-noised images by using a violent matching algorithm, to obtain the feature match set, comprises:
step S231: matching the feature points in every two adjacent de-noised images of the de-noised images by using the violent matching algorithm executed by a matching sub-unit of the feature match set determining unit;
step S232: determining a best match distance according to a principle of closest distance executed by a best match distance determining sub-unit of the feature match set determining unit;
step S233: determining a second best match distance according to the principle of closest distance executed by a second best match distance determining sub-unit of the feature match set determining unit; and
step S234: determining whether a ratio of the best match distance to the second best match distance is greater than or equal to a first set threshold executed by a determining sub-unit of the feature match set determining unit if the ratio of the best match distance to the second best match distance is greater than or equal to the first set threshold, determining the feature match set, wherein the feature match set comprises feature matches corresponding to a plurality of feature points; if the ratio of the best match distance to the second best match distance is less than the first set threshold, deleting a feature match;

step S3: deleting, among the de-noised images, one or more de-noised images of which the number of feature matches in the feature match set is less than a second set threshold executed by a screening module of the computer-executable instructions, to obtain n screened images;
step S4: combining the n screened images via a combined grayscale image determining module of the computer-executable instructions, to obtain a combined grayscale image; and
step S5: determining plant leaf surface roughness according to the combined grayscale image via a plant leaf surface roughness determining module of the computer-executable instructions.