The estimation of optical flow has become a vital research field in image sequence
analysis especially in past two decades, which found applications in many fields such as
stereo optics, video compression, robotics and computer vision. In this thesis, the complex
wavelet based algorithm for the estimation of optical flow developed by Magarey and
Kingsbury is implemented and investigated.
The algorithm is based on a complex version of the discrete wavelet transform (CDWT),
which analyzes an image through blocks of filtering with a set of Gabor-like kernels with
different scales and orientations. The output is a hierarchy of scaled and subsampled
orientation-tuned subimages. The motion estimation algorithm is based on the relationship
between translations in image domain and phase shifts in CDWT domain, which is satisfied
by the shiftability and interpolability property of CDWT. Optical flow is estimated by using
this relationship at each scale, in a coarse-to-fine (hierarchical) manner, where information
from finer scales is used to refine the estimates from coarser scales.
The performance of the motion estimation algorithm is investigated with various image
sequences as input and the effects of the options in the algorithm like curvature-correction,
interpolation kernel between levels and some parameter values like confidence threshold
iv
maximum number of CDWT levels and minimum finest level of detail are also experimented
and discussed.
The test results show that the method is superior to other well-known algorithms in
estimation accuracy, especially under high illuminance variations and additive noise.
Identifer | oai:union.ndltd.org:METU/oai:etd.lib.metu.edu.tr:http://etd.lib.metu.edu.tr/upload/1223205/index.pdf |
Date | 01 January 2003 |
Creators | Sari, Huseyin |
Contributors | Severcan, Mete |
Publisher | METU |
Source Sets | Middle East Technical Univ. |
Language | English |
Detected Language | English |
Type | M.S. Thesis |
Format | text/pdf |
Rights | To liberate the content for public access |
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