Next Article in Journal
Online Doppler Effect Elimination Based on Unequal Time Interval Sampling for Wayside Acoustic Bearing Fault Detecting System
Next Article in Special Issue
Received Signal Strength Database Interpolation by Kriging for a Wi-Fi Indoor Positioning System
Previous Article in Journal
A Combination of Genetic Algorithm and Particle Swarm Optimization for Vehicle Routing Problem with Time Windows
Previous Article in Special Issue
Infrastructure-Less Indoor Localization Using the Microphone, Magnetometer and Light Sensor of a Smartphone
Article Menu

Export Article

Open AccessArticle
Sensors 2015, 15(9), 21054-21074; doi:10.3390/s150921054

Saliency-Guided Detection of Unknown Objects in RGB-D Indoor Scenes

1
Department of Hydraulic, Energy and Power Engineering, Yangzhou University, Yangzhou 225000, China
2
Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI 48824, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Kourosh Khoshelham and Sisi Zlatanova
Received: 27 June 2015 / Revised: 19 August 2015 / Accepted: 21 August 2015 / Published: 27 August 2015
(This article belongs to the Special Issue Sensors for Indoor Mapping and Navigation)
View Full-Text   |   Download PDF [7193 KB, uploaded 27 August 2015]   |  

Abstract

This paper studies the problem of detecting unknown objects within indoor environments in an active and natural manner. The visual saliency scheme utilizing both color and depth cues is proposed to arouse the interests of the machine system for detecting unknown objects at salient positions in a 3D scene. The 3D points at the salient positions are selected as seed points for generating object hypotheses using the 3D shape. We perform multi-class labeling on a Markov random field (MRF) over the voxels of the 3D scene, combining cues from object hypotheses and 3D shape. The results from MRF are further refined by merging the labeled objects, which are spatially connected and have high correlation between color histograms. Quantitative and qualitative evaluations on two benchmark RGB-D datasets illustrate the advantages of the proposed method. The experiments of object detection and manipulation performed on a mobile manipulator validate its effectiveness and practicability in robotic applications. View Full-Text
Keywords: unknown object detection; saliency detection; RGB-D object segmentation unknown object detection; saliency detection; RGB-D object segmentation
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 alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Bao, J.; Jia, Y.; Cheng, Y.; Xi, N. Saliency-Guided Detection of Unknown Objects in RGB-D Indoor Scenes. Sensors 2015, 15, 21054-21074.

Show more citation formats Show less citations formats

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