Multi-mode-based object image augmentation method and device, equipment and storage medium

An object image and multi-modal technology, applied in 3D object recognition, character and pattern recognition, biological neural network model, etc., can solve the problem that attitude data cannot be effectively expanded, and achieve the effect of improving robustness and augmenting images

Active Publication Date: 2022-06-24
CHINA TELECOM CORP LTD
View PDF0 Cites 2 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The present disclosure provides a multimodal object image augmentation method, device, equipment, and storage medium, at least to a certain extent, overcoming the object image data augmentation method in the related art that cannot effectively expand the different attitude data of three-dimensional objects. question

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
  • Multi-mode-based object image augmentation method and device, equipment and storage medium
  • Multi-mode-based object image augmentation method and device, equipment and storage medium
  • Multi-mode-based object image augmentation method and device, equipment and storage medium

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0029] Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments, however, can be embodied in various forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0030] Furthermore, the drawings are merely schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus their repeated descriptions will be omitted. Some of the block diagrams shown in the figures are functional entities, which do not necessarily necessarily correspond to physically or logically separate entities. These func...

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 provides an object image augmentation method and device based on multiple modes, equipment and a storage medium, and relates to the technical field of machine learning image recognition. The method comprises the following steps: acquiring a three-dimensional point cloud model of a to-be-augmented object; segmenting the three-dimensional point cloud model to obtain a plurality of composition blocks of the to-be-augmented object; acquiring actual motion video data of the to-be-augmented object, and determining one or more motion axes of the to-be-augmented object according to the actual motion video data; identifying one or more motion blocks of the to-be-augmented object from the plurality of composition blocks according to one or more motion axes of the to-be-augmented object; and setting different motion states for each motion block of the to-be-augmented object to generate image augmentation data of the to-be-augmented object. According to the invention, the image of the three-dimensional object is widened, the data set for training the image recognition model is expanded, and the robustness of the image recognition model is improved.

Description

technical field [0001] The present disclosure relates to the technical field of machine learning image recognition, and in particular, to a multimodal-based object image augmentation method and device, device, and storage medium. Background technique [0002] With the continuous development of deep learning technology, the performance of image recognition systems based on neural networks is also constantly improving. However, the currently implemented image recognition models cannot reach the level of human cognitive recognition, and their recognition ability depends on the data set used for training. The training data set is collected from 3D objects, and 3D objects will have different light and shadow effects when exposed to different angles and intensities of light. This change cannot be simulated with simple 2D data augmentation. In the prior art, three-dimensional modeling of an object is performed through a two-dimensional image or two-dimensional depth image of multi...

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
Patent Type & Authority Applications(China)
IPC IPC(8): G06V20/40G06V20/64G06V10/26G06V10/774G06V10/82G06K9/62G06N3/04
CPCG06N3/045G06F18/214
Inventor 姚旭杨李伟谷红明
Owner CHINA TELECOM CORP LTD
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

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

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap