Question

I have an problem as seen below the one on top is the original image the one on the bottom is titlted to the left 90 degrees. Is there a way to calculate the change in angle. I have tried Hough’s Transform, I can detect the location, however I cannot detect the angel that is changed. Is there an alternative to detect the degreeschanged.

Answer 1

I’ve done this before using a Symmetric Phase-Only Matched Filter. It was actually for fingerprint recognition, allowing rotation and scaling. Unfortunately, it’s pretty complicated. You need to be comfortable with how to compute an FFT and with similar-level math. I did not do it in C#, but in MATLAB (and in synthesizable Verilog, but that’s another story altogether). I would recommend using MATLAB or a similar math package first to be sure you have the algorithm right.

Hopefully, though, someone has already implemented this algorithm in a .NET image processing library somewhere.

The paper I used was the following. It is more math-based than algorithmic, so it will take some work to convert it to code:

Qin-Sheng Chen; Defrise, M.; Deconinck, F.
“Symmetric phase-only
matched filtering of Fourier-Mellin
transforms for image registration and
recognition,”
Pattern Analysis and
Machine Intelligence, IEEE
Transactions on , vol.16, no.12,
pp.1156-1168, Dec 1994

Abstract: Presents a new method to
match a 2D image to a translated,
rotated and scaled reference image.
The approach consists of two steps:
the calculation of a Fourier-Mellin
invariant (FMI) descriptor for each
image to be matched, and the matching
of the FMI descriptors. The FMI
descriptor is translation invariant,
and represents rotation and scaling as
translations in parameter space. The
matching of the FMI descriptors is
achieved using symmetric phase-only
matched filtering (SPOMF). The
performance of the FMI-SPOMF algorithm
is the same or similar to that of
phase-only matched filtering when
dealing with image translations. The
significant advantage of the new
technique is its capability to match
rotated and scaled images accurately
and efficiently. The innovation is the
application of SPOMF to the FMI
descriptors, which guarantees high
discriminating power and excellent
robustness in the presence of noise.
This paper describes the principle of
the new method and its discrete
implementation for either image
detection problems or image
registration problems. Practical
results are presented for various
applications in medical imaging,
remote sensing, fingerprint
recognition and multiobject
identification