Next Article in Journal
Ensemble of One-Class Classifiers for Personal Risk Detection Based on Wearable Sensor Data
Previous Article in Journal
Integration of Error Compensation of Coordinate Measuring Machines into Feature Measurement: Part I—Model Development
Article Menu

Export Article

Open AccessArticle
Sensors 2016, 16(10), 1614; doi:10.3390/s16101614

Spectral Skyline Separation: Extended Landmark Databases and Panoramic Imaging

Computer Engineering Group, Faculty of Technology, Bielefeld University, D-33594 Bielefeld, Germany
*
Author to whom correspondence should be addressed.
Academic Editor: Vittorio M. N. Passar
Received: 9 August 2016 / Revised: 20 September 2016 / Accepted: 26 September 2016 / Published: 29 September 2016
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [5132 KB, uploaded 30 September 2016]   |  

Abstract

Evidence from behavioral experiments suggests that insects use the skyline as a cue for visual navigation. However, changes of lighting conditions, over hours, days or possibly seasons, significantly affect the appearance of the sky and ground objects. One possible solution to this problem is to extract the “skyline” by an illumination-invariant classification of the environment into two classes, ground objects and sky. In a previous study (Insect models of illumination-invariant skyline extraction from UV (ultraviolet) and green channels), we examined the idea of using two different color channels available for many insects (UV and green) to perform this segmentation. We found out that for suburban scenes in temperate zones, where the skyline is dominated by trees and artificial objects like houses, a “local” UV segmentation with adaptive thresholds applied to individual images leads to the most reliable classification. Furthermore, a “global” segmentation with fixed thresholds (trained on an image dataset recorded over several days) using UV-only information is only slightly worse compared to using both the UV and green channel. In this study, we address three issues: First, to enhance the limited range of environments covered by the dataset collected in the previous study, we gathered additional data samples of skylines consisting of minerals (stones, sand, earth) as ground objects. We could show that also for mineral-rich environments, UV-only segmentation achieves a quality comparable to multi-spectral (UV and green) segmentation. Second, we collected a wide variety of ground objects to examine their spectral characteristics under different lighting conditions. On the one hand, we found that the special case of diffusely-illuminated minerals increases the difficulty to reliably separate ground objects from the sky. On the other hand, the spectral characteristics of this collection of ground objects covers well with the data collected in the skyline databases, increasing, due to the increased variety of ground objects, the validity of our findings for novel environments. Third, we collected omnidirectional images, as often used for visual navigation tasks, of skylines using an UV-reflective hyperbolic mirror. We could show that “local” separation techniques can be adapted to the use of panoramic images by splitting the image into segments and finding individual thresholds for each segment. Contrarily, this is not possible for ‘global’ separation techniques. View Full-Text
Keywords: UV; color vision; insect vision; linear separation UV; color vision; insect vision; linear separation
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

Differt, D.; Möller, R. Spectral Skyline Separation: Extended Landmark Databases and Panoramic Imaging. Sensors 2016, 16, 1614.

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