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

Differt, Dario; Möller, Ralf

doi:10.1016/j.jtbi.2015.06.020

Cited by 20 publications

(30 citation statements)

References 48 publications

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“…For example, UV light itself or in combination with green can be used to reliably separate sky from ground in images [42][43][44][45]. Infrared light also allows the separation of sky and ground, as [46] show by solving an energy maximization problem.…”

Section: Related Work: Edge Suppressionmentioning

confidence: 99%

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Cloud-Edge Suppression for Visual Outdoor Navigation

Hoffmann

Möller

2017

Robotics

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Outdoor environments pose multiple challenges for the visual navigation of robots, like changing illumination conditions, seasonal changes, dynamic environments and non-planar terrain. Illumination changes are mostly caused by the movement of the Sun and by changing cloud cover. Moving clouds themselves also are a dynamic aspect of a visual scene. For visual homing algorithms, which compute the direction to a previously visited place by comparing the current view with a snapshot taken at that place, in particular, the changing cloud cover poses a problem, since cloud movements do not correspond to movements of the camera and thus constitute misleading information. We propose an edge-filtering method operating on linearly-transformed RGB channels, which reliably detects edges in the ground region of the image while suppressing edges in the sky region. To fulfill this criterion, the factors for the linear transformation of the RGB channels are optimized systematically concerning this special requirement. Furthermore, we test the proposed linear transformation on an existing visual homing algorithm (MinWarping) and show that the performance of the visual homing method is significantly improved compared to the use of edge-filtering methods on alternative color information.

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Section: Related Work: Edge Suppressionmentioning

confidence: 99%

“…First, the low dynamic range images are combined into a high dynamic range image using the HDR procedure described in [57]. We use the modified version from [43] and compute the HDR images for each RGB channel separately. This step is included to avoid problems with over-and under-exposure of the images.…”

Section: Image Preprocessingmentioning

confidence: 99%

Cloud-Edge Suppression for Visual Outdoor Navigation

Hoffmann

Möller

2017

Robotics

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“…In comparison to the former studies using sensors with a single photodiode per channel, in this study (and our previous study [19]), we use a camera setup that allows the acquisition of complete log UV/G images instead of single log UV/G data points. The photodiode sensors had an opening angle of around 10 degrees, such that precise measurements of single objects were not possible, contrary to the camera setup that allows one to collect precise measurements of objects and is only limited by the resolution of the cameras used.…”

Section: Introductionmentioning

confidence: 99%

“…For all tested methods in this study, we assume that the projection plane is fixed and needs to be learned. Note that in our previous study [19] we also examined local separation techniques (unsupervised) that learned both the projection angle and the threshold value. Due to their comparable weak performance (also on the newly-collected databases), they are not considered in this study (Section 2.3).…”

Section: Introductionmentioning

confidence: 99%

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Spectral Skyline Separation: Extended Landmark Databases and Panoramic Imaging

Differt

Möller

2016

Sensors

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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.

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Snapshot Navigation in the Wavelet Domain

Meyer

Nowotny

Graham

et al. 2020

Biomimetic and Biohybrid Systems

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Many animals rely on robust visual navigation which can be explained by snapshot models, where an agent is assumed to store egocentric panoramic images and subsequently use them to recover a heading by comparing current views to the stored snapshots. Long-range route navigation can also be explained by such models, by storing multiple snapshots along a training route and comparing the current image to these. For such models, memory capacity and comparison time increase dramatically with route length, rendering them unfeasible for smallbrained insects and low-power robots where computation and storage are limited. One way to reduce the requirements is to use a compressed image representation. Inspired by the filter bank-like arrangement of the visual system, we here investigate how a frequency-based image representation influences the performance of a typical snapshot model. By decomposing views into wavelet coefficients at different levels and orientations, we achieve a compressed visual representation that remains robust when used for navigation. Our results indicate that route following based on wavelet coefficients is not only possible but gives increased performance over a range of other models.

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

Cited by 20 publications

References 48 publications

Cloud-Edge Suppression for Visual Outdoor Navigation

Cloud-Edge Suppression for Visual Outdoor Navigation

Spectral Skyline Separation: Extended Landmark Databases and Panoramic Imaging

Snapshot Navigation in the Wavelet Domain

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