## Visual Motion Estimation based on Motion Blur Interpretation

**Ioannis Rekleitis**

Master's thesis.
School of Computer Science, McGill University

Nov. 1995
[37 citations]### Abstract

Abstract: When the relative velocity between the different objects in a scene and the camera is relative large -- compared with the camera's exposure time -- in the resulting image we have a distortion called motion blur. In the past, a lot of algorithms have been proposed for estimating the relative velocity from one or, most of the time, more images. The motion blur is generally considered an extra source of noise and is eliminated, or is assumed nonexistent. Unlike most of these approaches, it is feasible to estimate the Optical Flow map using only the information encoded in the motion blur. This thesis presents an algorithm that estimates the velocity vector of an image patch using the motion blur only, in two steps. The information used for the estimation of the velocity vectors is extracted from the frequency domain, and the most computationally expensive operation is the Fast Fourier Transform that transforms the image from the spatial to the frequency domain. Consequently, the complexity of the algorithm is bound by this operation into O(n log(n)). The first step consists of using the response of a family of steerable filters applied on the log of the Power Spectrum in order to calculate the orientation of the velocity vector. The second step uses a technique called Cepstral Analysis. More precisely, the log power spectrum is treated as another signal and we examine the Inverse Fourier Transform of it in order to estimate the magnitude of the velocity vector. Experiments have been conducted on artificially blurred images and with real world data, and an error analysis on these results is also presented.
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### BibTeX

@mastersthesis{Rekleitis95,
author = {Ioannis Rekleitis},
title = {Visual Motion Estimation based on Motion Blur
Interpretation},
school = {School of Computer Science, McGill University},
year = {1995},
month = {Nov.},
address = {Montreal, Quebec, Canada}
}

*Wed Sep 18 06:21:01 EDT 2019
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