Terrain reconstruction aims at acquiring height maps by detecting corresponding feature points from two or more
down-looking remote sensing images. This paper proposes a terrain reconstruction algorithm based on epipolar line
rectification and dense matching. At first, it uses fundamental matrix to rectify stereo images to make their epipolar lines
parallel and remove the disparity in vertical direction. Then, dense matching based on grid method is applied, which can
provide sufficient matching points to estimate disparity of the rectified images. Finally, the heights of the matched points
can be calculated according to the obtained disparity and flight parameters. Experiments show that our algorithm can
generate precise and reliable height maps for well depicting terrain features.
KEYWORDS: 3D modeling, Visual process modeling, Motion models, Sensors, Image sensors, 3D image processing, Image segmentation, Optical sensors, 3D image reconstruction, Data modeling
An airborne vehicle must avoid obstacles like towers, fences, tree branches, mountains and building across the flight path.
So the ability to detect and locate obstacles using on-board sensors is an essential step in the autonomous navigation of
aircraft low-altitude flight. In this paper, a novel passive range method using conditional random field (CRF) is presented
to map the 3D scene in front of a moving aircraft with image sequences obtained from a forward-looking imaging sensor.
Finally, An dynamic graph cuts method was presented for the CRF model to recursively update thedepth map.
Experimental data demonstrates the effectiveness of our approach.
We present a novel scheme for discarding wide-baseline mismatches. Based on a general two-frame wide-baseline matching model, the proposed algorithm first generates match clusters that are topologically invariable between frames, and then discards mismatches from clusters. Experimental results demonstrate that our algorithm can effectively extract high-precision scale-invariant feature transform (SIFT) matches from low-precision initial SIFT matches for wide-baseline image pairs. Furthermore, the algorithm always performs best or close to best in the comparison, indicating that it is more robust than other methods for discarding wide-baseline mismatches.
An airborne vehicle such as a tactical missile must avoid obstacles like towers, tree branches, mountains and building
across the flight path. So the ability to detect and locate obstacles using on-board sensors is an essential step in the
autonomous navigation of aircraft low-altitude flight. This paper describes a novel method to detect and locate obstacles
using a sequence of images from a passive sensor (TV, FLIR). We model 3D scenes in the field-of-view (FOV) as a
collection of approximately planar layers that corresponds to the background and obstacles respectively. So each pixel
within a layer can have the same 2D affine motion model which depends on the relative depth of the layer. We formulate
the prior assumptions about the layers and scene within a Bayesian decision making framework which is used to
automatically determine the assignment of individual pixels to layers. Then, a generalized expectation maximization
(EM) method is used to find the MAP solution. Finally, simulation results demonstrate that this method is successful.
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