Spectral Skyline Separation: Extended Landmark Databases and Panoramic Imaging.

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.

Insect models of illumination-invariant skyline extraction from UV and green channels.

Experiments have shown that the skyline is an important visual cue for navigating insects. However, the comparison between two snapshots collected at different times of day is a complex task due to possible illumination changes. In this study we examine whether the information from two different color channels (UV and green, which are also available for many insects) can be used to obtain an illumination-invariant separation between the sky and ground. We collected UV and green images of seven different scenes over entire days, in which natural and artificial objects are visible in front of the sky. With the collected data we want to find answers to the following two questions: 'Does UV/green contrast vision increase the quality of separation compared to UV-only vision?' and 'What yields a better performance: separation methods based on a fixed threshold (global separation techniques) or separation methods which adapt the threshold dependent on the input image (local separation techniques)?' We implemented several linear separation techniques and found that UV/green contrast only marginally increases the quality of global separation in comparison to UV-only, and that local separation techniques are superior to global separation techniques.

The problem of home choice in skyline-based homing.

Navigation in cluttered environments is an important challenge for animals and robots alike and has been the subject of many studies trying to explain and mimic animal navigational abilities. However, the question of selecting an appropriate home location has, so far, received only little attention. This is surprising, since the choice of a home location might greatly influence an animal's navigation performance. To address the question of home choice in cluttered environments, a systematic analysis of homing trajectories was performed by computer simulations using a skyline-based local homing method. Our analysis reveals that homing performance strongly depends on the location of the home in the environment. Furthermore, it appears that by assessing homing success in the immediate vicinity of the home, an animal might be able to predict its overall success in returning to it from within a much larger area.