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

Human image segmenting method

A portrait and algorithm technology, applied in the direction of image analysis, image enhancement, image data processing, etc., can solve the problems of complex background and difficult search, and achieve the effect of avoiding uncertainty

Inactive Publication Date: 2015-03-25
ZHEJIANG UNIV
View PDF2 Cites 27 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

But in most pictures, the background is more complicated, and it is very difficult to accurately search for similar clothing, so it is necessary to segment the portraits in the picture

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
  • Human image segmenting method
  • Human image segmenting method
  • Human image segmenting method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0024] The technical solutions of the present invention will be clearly and completely explained below in conjunction with the accompanying drawings in the present invention.

[0025] The invention proposes a portrait segmentation method, which trains a convolutional neural network on a set of marked sample pictures. During the test, first use the convolutional neural network to predict the area that may be a portrait, generate a mask, then use the mask to initialize the Grabcut algorithm, and finally use the Grabcut algorithm to segment the area where the portrait is located in the picture. figure 1 It is a flow chart of the portrait segmentation method of the present invention. Such as figure 1 Shown, portrait segmentation method of the present invention comprises the following steps:

[0026] Step 1, collect image sample sets, and preprocess the image data. A sample set of pictures containing standing portraits and not containing portraits is collected from e-commerce w...

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 discloses a human image segmenting method. The method includes the steps that firstly, multiple pictures containing standing human images are collected from an electric commerce website, regions where the human images are located are marked, and a training data set is generated; then, a convolutional neural network is initialized and trained by the training data set; in the picture test process, firstly, the regions where the human images are possibly located are marked in the test pictures through the convolutional neural network and serve as possible foreground regions, a Grabcut algorithm is initialized, and the regions with the human images are segmented through the Grabcut algorithm finally. According to the method, the regions where targets possibly exist are predicted through the convolutional neural network, the possible target regions serve as masks which are used for initializing the Grabcut algorithm, the problem that interaction is needed for the Grabcut algorithm is solved, and segmenting accuracy is improved.

Description

technical field [0001] The invention belongs to the field of target detection and recognition, and relates to a method for segmenting a specific target, especially a portrait, from an image. Background technique [0002] With the development of e-commerce, more and more people choose to buy clothing on the Internet, so the function of searching for goods by pictures of e-commerce came into being. But in most pictures, the background is more complicated, and it is very difficult to accurately search for similar clothing, so it is necessary to segment the portraits in the picture. [0003] The Grabcut segmentation algorithm can get more accurate object segmentation results, but its initialization process requires manual intervention, and it needs to manually specify the possible foreground and background areas, otherwise it will regard the image boundary area as a possible background and the middle of the image as a possible Prospects. [0004] Convolutional neural network i...

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): G06T7/00
CPCG06T7/11G06T2207/10004G06T2207/30196
Inventor 宋明黎周星辰冯尊磊陈纯卜佳俊
Owner ZHEJIANG 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