3 research outputs found
View Selection with Geometric Uncertainty Modeling
Estimating positions of world points from features observed in images is a
key problem in 3D reconstruction, image mosaicking,simultaneous localization
and mapping and structure from motion. We consider a special instance in which
there is a dominant ground plane viewed from a parallel viewing
plane above it. Such instances commonly arise, for example, in
aerial photography. Consider a world point and its worst
case reconstruction uncertainty obtained by
merging \emph{all} possible views of chosen from . We first
show that one can pick two views and such that the uncertainty
obtained using only these two views is almost as
good as (i.e. within a small constant factor of) .
Next, we extend the result to the entire ground plane and show
that one can pick a small subset of (which
grows only linearly with the area of ) and still obtain a constant
factor approximation, for every point , to the minimum worst
case estimate obtained by merging all views in . Finally, we
present a multi-resolution view selection method which extends our techniques
to non-planar scenes. We show that the method can produce rich and accurate
dense reconstructions with a small number of views. Our results provide a view
selection mechanism with provable performance guarantees which can drastically
increase the speed of scene reconstruction algorithms. In addition to
theoretical results, we demonstrate their effectiveness in an application where
aerial imagery is used for monitoring farms and orchards
Signals in the Soil: Subsurface Sensing
In this chapter, novel subsurface soil sensing approaches are presented for monitoring and real-time decision support system applications. The methods, materials, and operational feasibility aspects of soil sensors are explored. The soil sensing techniques covered in this chapter include aerial sensing, in-situ, proximal sensing, and remote sensing. The underlying mechanism used for sensing is also examined as well. The sensor selection and calibration techniques are described in detail. The chapter concludes with discussion of soil sensing challenges
Large Scale Image Mosaic Construction for Agricultural Applications
We present a novel technique for stitching images including those obtained from aerial vehicles flying at low altitudes. Existing image stitching/mosaicking methods rely on inter-image homography computation based on a planar scene assumption. This assumption holds when images are taken from high-altitudes (hence the depth variation is negligible). It is often violated when flying at low altitudes. Further, to avoid scale and resolution changes, existing methods rely on primarily translational motion at fixed altitudes. Our method removes these limitations and performs well even when aerial images are taken from low altitudes by an aerial vehicle performing complex motions. It starts by extracting the ground geometry from a sparse reconstruction of the scene obtained from a small fraction of the input images. Next, it selects the best image (from the entire sequence) for each location on the ground using a novel camera selection criterion. This image is then independently rectified to obtain the corresponding portion of the mosaic. Therefore, the technique avoids performing costly joint-optimization over the entire sequence. It is validated using challenging input sequences motivated by agricultural applications.
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