Electronic Letters on Computer Vision and Image Analysis 7(3):36-53, 2008
Enhancing Sensor Measurements through Wide Baseline Stereo
Department of Computer Science and Engineering, German University in Cairo, Cairo, Egypt
Received 26 May 2008; accepted 18 June 2008
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) im-
ages. 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 decom-
posed 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.
Key Words: Wide baseline matching, sparse view matching, parameters recovery, rotation systems, JU-
DOCA, junction detection, feature detection.
Accurate information makes many Computer Vision tasks much easier. For example, point matching and
3D reconstruction of objects would be very much facilitated once the camera parameters for the observed
scene are known accurately. Unfortunately, in many cases, sensors used to capture camera parameters provide
measurements that may be characterized by uncertainty. Different types of errors may be present and vary from
fixed errors to random errors . Fixed errors (also known as bias) are constant deviations from the accurate
value while random errors (