RGBD image semantic segmentation method

A semantic segmentation and image technology, applied in the field of computer vision and pattern recognition, can solve problems such as not being able to integrate color images and depth images well, and not having global context information for learning images, so as to achieve high accuracy and improve accuracy Effect

Active Publication Date: 2017-11-28
SUN YAT SEN UNIV
View PDF8 Cites 83 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0007] In summary, the existing semantic segmentation methods based on RGBD images are mostly the characteristics of simple stacked convolutional network in the data fusion of color im

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
  • RGBD image semantic segmentation method
  • RGBD image semantic segmentation method
  • RGBD image semantic segmentation method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0057] The technical solutions of the present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0058] Such as figure 1 As shown, a kind of RGBD image semantic segmentation method provided by the present invention comprises the following steps:

[0059] S1. Collect data of training samples;

[0060] S2. Construct a configurable depth model, and input the data of training samples into the depth model to train the depth model;

[0061] S3. Obtain the color image and the corresponding depth image that need to be semantically segmented, analyze the color image and the depth image using the trained depth model, and predict the object category to which each pixel in the RGBD image belongs;

[0062] S4. According to the result of S3, form and output a predicted image semantic segmentation map;

[0063] Specifically, the S1 includes:

[0064] S101. Shoot the scene in the same direction at the same position through...

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 RGBD image semantic segmentation method. The method comprises the following steps of: S1, acquiring data of a training sample; S2, constructing a configurable depth model and inputting data of the training sample into the depth model to train the depth model; S3, obtaining a color image needing semantic segmentation and a corresponding depth image, analyzing the color image and the depth image by utilizing the trained depth model, and predicting an object to which each pixel in an RGBD image belongs; and S4, forming and outputting a predicted image semantic segmentation image according to a result obtained in S3. According to the method, a deep-level convolutional neural network, a long/short-time memory network and big data are utilized, so that features of color images and depth images can be effectively fused, context information in the images can be effectively mined, and high correctness is provided.

Description

technical field [0001] The invention relates to the fields of computer vision and pattern recognition, in particular to a method for semantic segmentation of RGBD images based on a convolutional neural network and a long-short-term memory network. Background technique [0002] Semantic segmentation is an important field in computer vision research. Its main task is to enable computers to know "what" each pixel in an image is. Its applications include robot task planning, pose estimation, and content-based image retrieval. The goal of semantic segmentation is to hope that the computer can automatically predict the object category of each pixel in an unknown image, such as tables, roads, walls, etc. Semantic segmentation can be divided into two directions: semantic segmentation based on outdoor scene images and semantic segmentation based on indoor scene images. In recent years, cheap depth sensors, such as kinect, realsence, xtion, etc., have provided an additional data sour...

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): G06T7/10G06N3/04
CPCG06T7/10G06N3/045
Inventor 林倞甘宇康李冠彬王青
Owner SUN YAT SEN UNIV
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