BMVC 2004, Kingston, 7th-9th Sept, 2004
Principal Components Analysis of Optical Snow
V. Chapdelaine-.Couture, S. Roy (University of Montreal, Canada), M.S.
Langer (McGill University, Canada) and R. Mann (University of Waterloo,
Many applications in computer vision use Principal Components Analysis
(PCA), for example, in camera calibration, stereo, localization and motion
estimation. We present a new and fast PCA-based method to analyze optical
snow. Optical snow is a complex form of visual motion that occurs when
an observer moves through a highly cluttered 3D scene. For this category of
motion field, no spatial or depth coherence can be assumed. Previous methods
for measuring optical snow have used a wedge filter in a spatiotemporal
frequency domain. The PCA method is also based on the spatiotemporal
frequency domain analysis, but examines a different geometry property of
the spectrum. We compare the results of the PCA method to the previous
methods using both real and synthetic sequences.