Supercharge Your Innovation With Domain-Expert AI Agents!

Intelligent image optimization method and system based on crowd image selection

An optimization method and image technology, applied in image enhancement, image analysis, image data processing, etc., can solve the problems of difficulty in highlighting the original portrait features, spending a lot of time and energy, and difficult to manually correct, so as to avoid distortion and avoid image distortion. , the effect of improving the naturalness

Active Publication Date: 2019-10-15
七腾机器人有限公司
View PDF15 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, manual adjustment takes a lot of time and effort, and it is difficult to perform all manual corrections when there are a large number of pictures that need to be corrected
[0003] The automatically corrected image is likely to be too different from the original image, or cause distortion of the portrait, which is not natural enough to highlight the characteristics of the original portrait
Often requires manual correction again

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Intelligent image optimization method and system based on crowd image selection
  • Intelligent image optimization method and system based on crowd image selection
  • Intelligent image optimization method and system based on crowd image selection

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0065] refer to figure 1 .

[0066] Specifically, an intelligent image optimization method based on group portrait selection includes the following steps:

[0067] Obtain crowd images, perform face recognition on crowd images, and crowd images include still images and moving images. Extract independent portraits in crowd images, and perform facial recognition on independent portraits. Facial recognition includes the acquisition of facial features.

[0068]Crowd images include still images and dynamic images, namely pictures and videos. According to the existing face recognition technology, the faces in the crowd images are recognized, and the portraits in the crowd images are locked and captured according to the facial data.

[0069] Extract the facial feature points of the independent portrait, and retrieve the facial feature points of the independent portrait through big data.

[0070] According to the face recognition technology, the face feature points of the independe...

Embodiment 2

[0083] refer to Figure 2-4 .

[0084] This embodiment is basically the same as the first embodiment above, the difference is that, as a preference of this embodiment, the following steps are included:

[0085] Obtain several crowd images at different time nodes, and perform face recognition on the crowd images.

[0086] The crowd images are directly ingested, and multiple crowd images are intercepted at different time nodes after receiving the ingestion instructions. For example, crowd images are intercepted at preset time intervals.

[0087] Extract individual portraits with the same facial features. Extract the independent portraits with the same facial features in the crowd images, that is, extract the independent portraits of the same person in different crowd images.

[0088] Compare independent portraits with the same facial features in crowd images at different time points. Compare independent portraits of the same person in different crowd images.

[0089] If th...

Embodiment 3

[0106] This embodiment is used as the system application of the above embodiments, including:

[0107] The crowd image acquisition module is configured to acquire specified crowd images according to instructions.

[0108] The portrait extraction module is used to extract independent portraits in crowd images according to instructions.

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention relates to an intelligent image optimization method and system based on crowd image selection. The intelligent image optimization method comprises the following steps: obtaining a crowdimage, and carrying out face recognition on the crowd image, wherein the crowd image comprises a static image and a moving image; extracting independent portraits in the crowd image, and carrying outface recognition on the independent portraits, wherein face recognition comprises acquisition of features of five sense organs; extracting face feature points of the independent portraits, and retrieving the face feature points of the independent portraits through big data; acquiring historical images of the independent portraits in a database, and comparing the historical images with the independent portraits; extracting posture features of the historical images and the independent portraits, and obtaining a difference proportion; extracting facial features of the historical images and the independent portraits, comparing the facial features, and obtaining facial feature correction values, wherein the facial feature correction values are facial difference data of the historical images andthe independent portraits; and correcting the independent portrait according to the facial feature correction value and the difference proportion.

Description

technical field [0001] The invention relates to the field of image correction, in particular to an intelligent image optimization method and system based on group image selection. Background technique [0002] Image correction is widely used in daily life photography to actively correct portraits in images. However, the existing ones are divided into automatic correction and manual correction. Manual correction means that the user manually adjusts the image based on the original data of the image, and automatic correction means that the software automatically adjusts the data of the image based on the original data of the image. However, manual adjustment takes a lot of time and effort, and it is difficult to perform all manual corrections when there are a large number of pictures that need to be corrected. [0003] The automatically corrected image is likely to be too different from the original image, or cause distortion of the portrait, which is not natural enough to hi...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
IPC IPC(8): G06T5/00G06K9/00G06F16/583
CPCG06F16/583G06T2207/10004G06T2207/10016G06T2207/30201G06V40/172G06V40/168G06T5/77
Inventor 姚俊浩
Owner 七腾机器人有限公司
Features
  • R&D
  • Intellectual Property
  • Life Sciences
  • Materials
  • Tech Scout
Why Patsnap Eureka
  • Unparalleled Data Quality
  • Higher Quality Content
  • 60% Fewer Hallucinations
Social media
Patsnap Eureka Blog
Learn More

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2025 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com