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

Visual feature degraded image-orientated solid waste object segmentation method

A degraded image and object segmentation technology, applied in the field of image segmentation and robot vision, can solve problems such as difficulties, object adhesion, and visual feature degradation

Active Publication Date: 2017-12-29
杭州视熵科技有限公司
View PDF5 Cites 8 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] In order to solve the problem of segmentation difficulties caused by the degradation of existing visual features, object adhesion and object occlusion

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
  • Visual feature degraded image-orientated solid waste object segmentation method
  • Visual feature degraded image-orientated solid waste object segmentation method
  • Visual feature degraded image-orientated solid waste object segmentation method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0045] The present invention will be further described below in conjunction with the accompanying drawings.

[0046] refer to Figure 1 to Figure 7 , a solid waste object segmentation method for images with degraded visual features, comprising the following steps:

[0047] 1) Use the depth information of a series of background point cloud data to establish a background depth Gaussian mixture model. For each pixel in the image, it is modeled by a Gaussian mixture distribution. The probability distribution of the depth of a pixel equal to d is calculated by formula (1.1) express,

[0048]

[0049] Among them, w j is the weight of the jth Gaussian distribution, K is the total number of Gaussian distribution, gets K=5 among the present invention, and η (d; Θ j ) is the jth Gaussian distribution, expressed by formula (1.2),

[0050]

[0051] Among them, μ k is the mean of the kth Gaussian distribution, ∑ k is the covariance matrix of the kth Gaussian distribution, ∑ ...

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 visual feature degraded image-orientated solid waste object segmentation method and belongs to fields of robot vision, image segmentation and the like. Due to visual feature degrading and the adhesion and occlusion of solid waste objects, a traditional image segmentation algorithm is difficult to obtain high-precision segmentation results. According to the method of the invention, a background model is obtained through deep background modeling; the background model is compared with solid waste point cloud, so that a foreground mask can be extracted; local masks in the foreground mask are extracted, and therefore, an entire image segmentation problem is converted into a plurality of local mask segmentation problems; and as for the local masks, adhered and occluded objects are segmented through fuzzy region extraction, and fuzzy region re-marking is performed to obtain high-precision segmentation results. The method of the invention has high segmentation accuracy and can effectively segment solid waste objects of which the colors are seriously degraded, and enable ideal segmentation effects for the adhered and occluded solid waste objects.

Description

technical field [0001] The invention relates to the technical fields of robot vision and image segmentation, especially the solid waste object segmentation of images with degraded visual features. Background technique [0002] Traditional image segmentation algorithms use color and contour features. However, due to the complex industrial environment: the surface of the conveyor belt is covered by dust, the dust particles on the surface of the solid waste body cause serious visual feature degradation, and the solid waste body has adhesion and occlusion. These will have a great impact on 2D image segmentation, so traditional image segmentation methods are not suitable for industrial scenes. Contents of the invention [0003] In order to solve the problem of segmentation difficulties caused by the degradation of existing visual features, object adhesion and object occlusion. The invention provides a solid waste object segmentation method under severe visual feature degradat...

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): G06T7/11G06T7/12G06T7/187G06T7/194G06T7/00G06T5/30
CPCG06T5/30G06T7/0004G06T7/11G06T7/12G06T7/187G06T7/194G06T2207/10024G06T2207/20116G06T2207/30108
Inventor 刘盛王超冯缘尹科杰陈胜勇
Owner 杭州视熵科技有限公司
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