Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

A Fabric Defect Detection Method Based on Light Field Camera Depth Information Extraction

A light field camera, depth information technology, applied in image analysis, image enhancement, instrumentation, etc., can solve problems such as not well solved, vignetting effect, complex target effect, etc.

Active Publication Date: 2021-06-08
WUHAN TEXTILE UNIV
View PDF10 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Most of the existing light field camera depth processing technologies deal with some relatively large objects, or the shooting target is far away from the imaging surface, such as the depth processing of light field cameras used for statues and Lego cars, because the light field camera microlens array Because of the vignetting effect, it will have a great impact on close-range high-precision and high-texture complexity targets, such as fabric textures, etc.
The current light field camera depth map construction technology has been very developed, and the light field camera can be used to realize 3D reconstruction very clearly, but the vignetting effect of the light field camera has not been well solved yet.
Existing solutions are all estimated based on the particularity of the object, and the estimated results cannot be used in the detection field

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
  • A Fabric Defect Detection Method Based on Light Field Camera Depth Information Extraction
  • A Fabric Defect Detection Method Based on Light Field Camera Depth Information Extraction
  • A Fabric Defect Detection Method Based on Light Field Camera Depth Information Extraction

Examples

Experimental program
Comparison scheme
Effect test

Embodiment

[0042] The present invention comprises the following steps:

[0043] Step 1: Collect a set of multi-view images with a light field camera.

[0044]Step 1.1: Shoot the target fabric with a light field camera in a uniform light field, extract the RAW file and white image file in the light field camera, decode the extracted RAW file first, and then color correct it, which needs to use Matlab light field toolkit, this toolkit is developed by D.G.Dansereau et al. Currently, there are two versions of toolbox0.3 and toolbox0.4. What is used in this embodiment is toolbox0.4, and the white image file will be used when decoding the image , each light field camera will have its own white image file, the toolkit reads the WhiteImagesDataBase mapping table, and the toolkit selects the most suitable white image and microlens grid model to obtain the light field image in Bayer format. Then perform frequency domain filtering on the image to perform demosaic operation on the image to obtain a...

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 a fabric defect detection method based on light field camera depth information extraction, the method can be used in the field of fabric detection, and is a method for detecting fabric defects from a 3-dimensional space. This method utilizes a light field camera to generate a multi-view image sequence of the target fabric. By substituting the slope for the estimated depth value of the disparity, the depth map is obtained by using the method of sub-pixel offset. In order to avoid the interference of the vignetting effect of the microlens of the light field camera, the weakly affected area is extracted, and the noise reduction process is performed on this part. The noise reduction of the present invention uses an image self-adaptive window filter noise reduction method. Effectively avoid the error caused by too large and too small median filter window. Finally, binarization is performed to obtain a segmentation map. By using the method of the invention to process the fabric, the defective part of the fabric can be effectively detected.

Description

technical field [0001] The invention relates to a method for detecting fabric defects, especially the processing, extraction and detection of three-dimensional depth information of fabrics Background technique [0002] The earliest depth processing is to use the camera array to obtain the parallax. The light field camera replaces the complex camera array with the main lens and the microlens array. One camera can realize the function of a camera array to obtain the depth. Most of the existing light field camera depth processing technologies deal with some relatively large objects, or the shooting target is far away from the imaging surface, such as the depth processing of light field cameras used for statues and Lego cars, because the light field camera microlens array Because of the vignetting effect, there will be a vignetting effect, which will have a great impact on close-range high-precision and high-texture complexity targets, such as fabric textures. The current light...

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 Patents(China)
IPC IPC(8): G06T7/00G06T5/00G06T7/557G06T7/11G06T7/136
CPCG06T5/002G06T5/007G06T7/0004G06T2207/10052G06T2207/20132G06T2207/30124G06T7/11G06T7/136G06T7/50
Inventor 袁理程哲
Owner WUHAN TEXTILE UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

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

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com