CN112116016A - Intelligent calibration method for plantar image based on machine learning - Google Patents

Intelligent calibration method for plantar image based on machine learning Download PDF

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CN112116016A
CN112116016A CN202011020788.4A CN202011020788A CN112116016A CN 112116016 A CN112116016 A CN 112116016A CN 202011020788 A CN202011020788 A CN 202011020788A CN 112116016 A CN112116016 A CN 112116016A
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image
plantar
sole
machine learning
calibration
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蒋峥峥
林纯
彭志娟
顾翔
严燕
王丹丹
陈晓红
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Nantong University
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Nantong University
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Priority to CN202011020788.4A priority Critical patent/CN112116016A/en
Publication of CN112116016A publication Critical patent/CN112116016A/en
Priority to LU102782A priority patent/LU102782B1/en
Priority to PCT/CN2020/141906 priority patent/WO2022062262A1/en
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Abstract

The invention is suitable for the technical field of neural network models, and provides a plantar image intelligent calibration method based on machine learning.

Description

Intelligent calibration method for plantar image based on machine learning
Technical Field
The invention belongs to the technical field of neural network models, and particularly relates to a plantar image intelligent calibration method based on machine learning.
Background
The sole reflex region refers to the condition that each part organ of human body has a corresponding part on sole, and the corresponding part can be massaged to adjust the organ function. Under the efforts of many experts and scholars, the theory and method of traditional Chinese medicine are applied to activate the foot reflex region therapy both theoretically and by the application of specific manipulation techniques, and the treatment is again at the highest level in this field in the world. From the perspective of bioholography, the foot region corresponds to a holographic embryo reflecting the whole body information. Because the blood vessels and nerves of the foot are densely distributed, the three yin meridians and the three yang meridians of the foot are communicated with each other at the foot and are communicated with the whole body through the meridian system, so that the foot is a place where the human body information is relatively concentrated. Before the sole of a patient is treated, the reflecting area of the sole of the patient needs to be calibrated.
The prior art does not have a method for intelligently calibrating the reflecting area, so that the operation is inconvenient and the working efficiency is low when the plantar reflecting area is calibrated.
Disclosure of Invention
The invention provides a foot sole image intelligent calibration method based on machine learning, and aims to solve the problems of inconvenient operation and low working efficiency when calibrating a foot sole reflecting area.
The invention is realized in this way, a plantar image intelligent calibration method based on machine learning, comprising the following steps:
s1, acquiring a sole image;
s2, manually calibrating the reflection region of the plantar image, and sending the manually calibrated image data serving as training data to the image calibration model;
s3, training the image calibration model according to the training data to obtain a trained image calibration model;
s4, collecting a sole image to be detected;
and S5, sending the plantar image to be measured to the trained image calibration model for calibration, and outputting the calibration result of the reflecting region.
Preferably, the acquiring of the plantar image specifically includes: and acquiring plantar plane images of both feet through an image acquisition device.
Preferably, step S1 further includes: the collected images of the sole of a foot were enlarged so that the maximum width of the sole of a foot was 20 cm.
Preferably, in step S2, the reflection area includes: heart, liver, lung, stomach and kidney.
Preferably, the image calibration model is trained according to training data to obtain a trained image calibration model, and specifically: and training the image calibration model according to the training data, and then re-training the image data of the low-certainty area to obtain the trained image calibration model.
Preferably, the image acquisition device adopts an infrared camera.
Preferably, the step S1 further includes denoising the obtained sole image, and performing gray-scale processing on the sole image to obtain a sole gray-scale image.
Preferably, the image calibration model is a full convolution neural network.
Compared with the prior art, the invention has the beneficial effects that: according to the intelligent calibration method of the plantar image based on machine learning, the plantar image is collected in sequence, the plantar image is calibrated in the reflecting region manually, the manually calibrated image data is sent to the image calibration model as training data, the image calibration model is trained according to the training data to obtain the trained image calibration model, the plantar image to be measured is collected, the plantar image to be measured is sent to the trained image calibration model for calibration, and the reflecting region calibration result is output, so that the reflecting region calibration of the plantar image to be measured can be carried out quickly, the operation is convenient, the working efficiency is high, and the labor cost is saved.
Drawings
Fig. 1 is a schematic flow chart of a plantar image intelligent calibration method based on machine learning according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Example one
Referring to fig. 1, the present invention provides a technical solution: a plantar image intelligent calibration method based on machine learning comprises the following steps:
and S1, acquiring a sole image. And acquiring plantar plane images of both feet through an image acquisition device. The collected images of the sole of a foot were enlarged so that the maximum width of the sole of a foot was 20 cm. And denoising the obtained sole image, and carrying out gray level processing on the sole image to obtain a sole gray level image.
S2, calibrating a reflecting area of the sole image manually, wherein the reflecting area comprises: the heart, the liver, the lung, the stomach and the kidney, and the image data calibrated manually are sent to the image calibration model as training data.
In this embodiment, the image capturing device employs an infrared camera.
And S3, training the image calibration model according to the training data, and then training the image data of the low-certainty region again to obtain the trained image calibration model. The image calibration model is a full convolution neural network.
And S4, acquiring the sole image to be detected. The collected images of the sole of a foot were enlarged so that the maximum width of the sole of a foot was 20 cm. And denoising the obtained sole image, and carrying out gray level processing on the sole image to obtain a sole gray level image.
And S5, sending the plantar image to be measured to the trained image calibration model for calibration, and outputting the calibration result of the reflecting region.
Example two
Compared with the first embodiment, the method for intelligently calibrating the plantar image based on machine learning increases the reflecting area to be calibrated, so that the marking information of the plantar reflecting area is more sufficient.
And S1, acquiring a sole image. And acquiring plantar plane images of both feet through an image acquisition device. The collected images of the sole of a foot were enlarged so that the maximum width of the sole of a foot was 20 cm. And denoising the obtained sole image, and carrying out gray level processing on the sole image to obtain a sole gray level image.
S2, calibrating a reflecting area of the sole image manually, wherein the reflecting area comprises: the heart, the liver, the lung, the stomach, the kidney, the spleen, the small intestine, the thyroid gland and the pancreas are used for sending the artificially calibrated image data to the image calibration model as training data.
In this embodiment, the image capturing device employs an infrared camera.
And S3, training the image calibration model according to the training data, and then training the image data of the low-certainty region again to obtain the trained image calibration model. The image calibration model is a full convolution neural network.
And S4, acquiring the sole image to be detected. The collected images of the sole of a foot were enlarged so that the maximum width of the sole of a foot was 20 cm. And denoising the obtained sole image, and carrying out gray level processing on the sole image to obtain a sole gray level image.
And S5, sending the plantar image to be measured to the trained image calibration model for calibration, and outputting the calibration result of the reflecting region.
EXAMPLE III
The embodiment provides a plantar image intelligent calibration system based on machine learning, which comprises an image acquisition module, an image processing module and an intelligent calibration module.
The image acquisition module is used for acquiring plantar images. The image processing module is used for amplifying the collected sole image so that the maximum width of the sole is 20 cm. Denoising the obtained sole image, carrying out gray level processing on the sole image to obtain a sole gray level image, and finally outputting the sole gray level image as training data. The intelligent calibration module is used for training according to training data, then retraining the image data of the low-certainty region again to obtain a trained image calibration model, and the image calibration model is used for intelligently calibrating the plantar image to be tested and outputting a reflecting region calibration result.
According to the intelligent calibration method of the plantar image based on machine learning, the plantar image is collected in sequence, the plantar image is calibrated in the reflecting region manually, the manually calibrated image data is sent to the image calibration model as training data, the image calibration model is trained according to the training data to obtain the trained image calibration model, the plantar image to be measured is collected, the plantar image to be measured is sent to the trained image calibration model for calibration, and the reflecting region calibration result is output, so that the reflecting region calibration of the plantar image to be measured can be carried out quickly, the operation is convenient, the working efficiency is high, and the labor cost is saved.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.

Claims (8)

1. A plantar image intelligent calibration method based on machine learning is characterized by comprising the following steps: the method comprises the following steps:
s1, acquiring a sole image;
s2, manually calibrating the reflection region of the plantar image, and sending the manually calibrated image data serving as training data to the image calibration model;
s3, training the image calibration model according to the training data to obtain a trained image calibration model;
s4, collecting a sole image to be detected;
and S5, sending the plantar image to be measured to the trained image calibration model for calibration, and outputting the calibration result of the reflecting region.
2. The intelligent calibration method for the plantar image based on the machine learning of claim 1, which is characterized by comprising the following steps: the acquisition of the plantar image specifically comprises the following steps: and acquiring plantar plane images of both feet through an image acquisition device.
3. The intelligent calibration method for the plantar image based on the machine learning of claim 1, which is characterized by comprising the following steps: step S1 further includes: the collected images of the sole of a foot were enlarged so that the maximum width of the sole of a foot was 20 cm.
4. The intelligent calibration method for the plantar image based on the machine learning of claim 1, which is characterized by comprising the following steps: in step S2, the reflection region includes: heart, liver, lung, stomach and kidney.
5. The intelligent calibration method for the plantar image based on the machine learning of claim 1, which is characterized by comprising the following steps: the image calibration model is trained according to training data to obtain a trained image calibration model, and the method specifically comprises the following steps: and training the image calibration model according to the training data, and then re-training the image data of the low-certainty area to obtain the trained image calibration model.
6. The intelligent calibration method for the plantar image based on the machine learning as claimed in claim 2, characterized in that: the image acquisition equipment adopts an infrared camera.
7. The intelligent calibration method for the plantar image based on the machine learning of claim 1, which is characterized by comprising the following steps: the step S1 further includes denoising the obtained sole image, and performing gray processing on the sole image to obtain a sole gray image.
8. The intelligent calibration method for the plantar image based on the machine learning of claim 1, which is characterized by comprising the following steps: the image calibration model is a full convolution neural network.
CN202011020788.4A 2020-09-25 2020-09-25 Intelligent calibration method for plantar image based on machine learning Pending CN112116016A (en)

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CN202011020788.4A CN112116016A (en) 2020-09-25 2020-09-25 Intelligent calibration method for plantar image based on machine learning
LU102782A LU102782B1 (en) 2020-09-25 2020-12-31 Intelligent calibration method of plantar images based on machine learning
PCT/CN2020/141906 WO2022062262A1 (en) 2020-09-25 2020-12-31 Machine learning-based method for intelligent sole image calibration

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WO2022062262A1 (en) * 2020-09-25 2022-03-31 南通大学 Machine learning-based method for intelligent sole image calibration

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TW201609077A (en) * 2014-09-03 2016-03-16 Univ Chienkuo Technology Enclosed intelligent image recognition foot sole massager
CN106389107A (en) * 2016-09-21 2017-02-15 云南中医学院 A foot bottom reflection zone self-adaptive identification intelligent foot therapy apparatus and method
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CN109815830A (en) * 2018-12-28 2019-05-28 梦多科技有限公司 A method of obtaining foot information in the slave photo based on machine learning
CN110051530A (en) * 2019-04-30 2019-07-26 西华大学 A kind of sole echo area localization method based on piecewise linear function

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Publication number Priority date Publication date Assignee Title
TW201609077A (en) * 2014-09-03 2016-03-16 Univ Chienkuo Technology Enclosed intelligent image recognition foot sole massager
CN106389107A (en) * 2016-09-21 2017-02-15 云南中医学院 A foot bottom reflection zone self-adaptive identification intelligent foot therapy apparatus and method
CN109086785A (en) * 2017-06-14 2018-12-25 北京图森未来科技有限公司 A kind of training method and device of image calibration model
CN109815830A (en) * 2018-12-28 2019-05-28 梦多科技有限公司 A method of obtaining foot information in the slave photo based on machine learning
CN110051530A (en) * 2019-04-30 2019-07-26 西华大学 A kind of sole echo area localization method based on piecewise linear function

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2022062262A1 (en) * 2020-09-25 2022-03-31 南通大学 Machine learning-based method for intelligent sole image calibration

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