Next Article in Journal
Partial Least Square Discriminant Analysis Based on Normalized Two-Stage Vegetation Indices for Mapping Damage from Rice Diseases Using PlanetScope Datasets
Previous Article in Journal
Coastline Detection with Gaofen-3 SAR Images Using an Improved FCM Method
Previous Article in Special Issue
Pulse Based Time-of-Flight Range Sensing
Article Menu
Issue 6 (June) cover image

Export Article

Open AccessArticle
Sensors 2018, 18(6), 1900; https://doi.org/10.3390/s18061900

Human Part Segmentation in Depth Images with Annotated Part Positions

Department of Electrical and Computer Engineering, Memorial University, St. John’s, NL A1B 3X5, Canada
*
Author to whom correspondence should be addressed.
Received: 2 May 2018 / Revised: 31 May 2018 / Accepted: 8 June 2018 / Published: 11 June 2018
(This article belongs to the Special Issue Depth Sensors and 3D Vision)
View Full-Text   |   Download PDF [963 KB, uploaded 11 June 2018]   |  

Abstract

We present a method of segmenting human parts in depth images, when provided the image positions of the body parts. The goal is to facilitate per-pixel labelling of large datasets of human images, which are used for training and testing algorithms for pose estimation and automatic segmentation. A common technique in image segmentation is to represent an image as a two-dimensional grid graph, with one node for each pixel and edges between neighbouring pixels. We introduce a graph with distinct layers of nodes to model occlusion of the body by the arms. Once the graph is constructed, the annotated part positions are used as seeds for a standard interactive segmentation algorithm. Our method is evaluated on two public datasets containing depth images of humans from a frontal view. It produces a mean per-class accuracy of 93.55% on the first dataset, compared to 87.91% (random forest and graph cuts) and 90.31% (random forest and Markov random field). It also achieves a per-class accuracy of 90.60% on the second dataset. Future work can experiment with various methods for creating the graph layers to accurately model occlusion. View Full-Text
Keywords: human parts; interactive image segmentation; occlusion; grid graph human parts; interactive image segmentation; occlusion; grid graph
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Hynes, A.; Czarnuch, S. Human Part Segmentation in Depth Images with Annotated Part Positions. Sensors 2018, 18, 1900.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top