146 research outputs found
An Epipolar Line from a Single Pixel
Computing the epipolar geometry from feature points between cameras with very
different viewpoints is often error prone, as an object's appearance can vary
greatly between images. For such cases, it has been shown that using motion
extracted from video can achieve much better results than using a static image.
This paper extends these earlier works based on the scene dynamics. In this
paper we propose a new method to compute the epipolar geometry from a video
stream, by exploiting the following observation: For a pixel p in Image A, all
pixels corresponding to p in Image B are on the same epipolar line.
Equivalently, the image of the line going through camera A's center and p is an
epipolar line in B. Therefore, when cameras A and B are synchronized, the
momentary images of two objects projecting to the same pixel, p, in camera A at
times t1 and t2, lie on an epipolar line in camera B. Based on this observation
we achieve fast and precise computation of epipolar lines. Calibrating cameras
based on our method of finding epipolar lines is much faster and more robust
than previous methods.Comment: WACV 201
Intrinsic Volumes of Random Cubical Complexes
Intrinsic volumes, which generalize both Euler characteristic and Lebesgue
volume, are important properties of -dimensional sets. A random cubical
complex is a union of unit cubes, each with vertices on a regular cubic
lattice, constructed according to some probability model. We analyze and give
exact polynomial formulae, dependent on a probability, for the expected value
and variance of the intrinsic volumes of several models of random cubical
complexes. We then prove a central limit theorem for these intrinsic volumes.
For our primary model, we also prove an interleaving theorem for the zeros of
the expected-value polynomials. The intrinsic volumes of cubical complexes are
useful for understanding the shape of random -dimensional sets and for
characterizing noise in applications.Comment: 17 pages with 7 figures; this version includes a central limit
theore
Camera Calibration from Dynamic Silhouettes Using Motion Barcodes
Computing the epipolar geometry between cameras with very different
viewpoints is often problematic as matching points are hard to find. In these
cases, it has been proposed to use information from dynamic objects in the
scene for suggesting point and line correspondences.
We propose a speed up of about two orders of magnitude, as well as an
increase in robustness and accuracy, to methods computing epipolar geometry
from dynamic silhouettes. This improvement is based on a new temporal
signature: motion barcode for lines. Motion barcode is a binary temporal
sequence for lines, indicating for each frame the existence of at least one
foreground pixel on that line. The motion barcodes of two corresponding
epipolar lines are very similar, so the search for corresponding epipolar lines
can be limited only to lines having similar barcodes. The use of motion
barcodes leads to increased speed, accuracy, and robustness in computing the
epipolar geometry.Comment: Update metadat
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