Enhancing Sensor Measurements throughWide Baseline Stereo Images

Rimon Elias


In this paper, we suggest an algorithm to enhance the accuracy of sensor measurements representing
camera parameters. The process proposed is based solely on a pair of wide baseline (or sparse view) images.
We use the so-called JUDOCA operator to extract junctions. This operator produces junctions in terms
of locations as well as orientations. Such an information is used to estimate an affine transformation matrix,
which is used to guide a variance normalized correlation process that produces a set of possible matches.
The fundamental matrix can be easily estimated using the so-called RANSAC scheme. Consequently, the
essential matrix can be derived given the available calibration matrix. The essential matrix is then decomposed
using Singular Value Decomposition. In addition to a translation vector, this decomposition results in
a rotation matrix with accurate rotation angles involved. Mathematical derivation is done to extract angles
from the rotation matrix and express them in terms of different rotation systems.


Wide baseline matching; sparse view matching; parameters recovery; rotation systems; JUDOCA; junction detection; feature detection

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