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This paper proposes a solution to the problem of mobile robotic localization using visual indoor image sequences with a biologically inspired spatio-temporal neural network approach.
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The robustness of the proposed algorithm is demonstrated through the disparity maps obtained from standard images on the Web, indoor images, and outdoor images of various road traffic scenes.
We have added some databases, composed of several sets of indoor images, captured in real environments under realistic lighting conditions, so students will face the problems that would outcome in a real application.
On Google Maps, however, it was virtually impossible to find these panoramic indoor images.
The KITTI dataset contains outdoor images, whereas Middlebury contains only indoor images.
In this paper, we focus on colour-based features to describe indoor images.
We collected arbitrary outdoor and indoor images from an internet for negative training data.
The FOD verification dataset consisted of 24 indoor images (indoor images were included for performance comparison, although there was no indoor case for FOD detection) and 76 outdoor images, as shown in Fig. 7.
The first row is the indoor images of the metal items, and the second row consists of the outdoor images.
It said the advantages were brighter, clearer indoor images that would be less prone to blur caused by unsteady hands than some SLR (single lens reflex) cameras on the market which feature bigger and more expensive lenses.
The long and short of it is that indoor images should be much more balanced in their color, with more natural skin tones and a balanced foreground and background.
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