Seeing Through the Blur

Mobahi, Hossein; Ma, Yi; Zitnick, Larry

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https://hdl.handle.net/2142/74359 Copy

Description

Title

Seeing Through the Blur

Author(s)

• Mobahi, Hossein
• Ma, Yi
• Zitnick, Larry

Issue Date

2011-12

Keyword(s)

• Image alignment
• Homography
• Affine

Date of Ingest

• 2015-04-06T20:54:51Z
• 2017-07-14T23:10:20Z

Abstract

This paper addresses the problem of image alignment using models such as affine and homography and by directly using pixel intensity values. Coarse-to-fine scheme has become a standard for direct intensity-based alignment. It is believed that such coarse-to-fine scale sampling (Gaussian blur) can improve region of convergence of the alignment optimization. Although, it has been proposed that such isotropic blur may not be optimal for some motion models, no rigorous derivation for such kernels has been known to date. In this work, we derive kernels for some of the common motion models such as affine and homography, which are able to smooth the alignment objective function. This is appealing because the smoothing process often removes poor local minima and thus reaches deeper solutions. Our derivation shows that these kernels coincide with Gaussian blur of the image only for displacement motion.

Publisher

Coordinated Science Laboratory, University of Illinois at Urbana-Champaign

Series/Report Name or Number

Coordinated Science Laboratory Report no. UILU-ENG-11-2210, DC-253

Type of Resource

text

Genre of Resource

Report (Grant or Annual)

Language

English

Permalink

http://hdl.handle.net/2142/74359

Sponsor(s)/Grant Number(s)

National Science Foundation / NSF IIS 11-16012

Owning Collections