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Fast Image Registration via Joint Gradient Maximization : Application to Multi-Modal Data
| Content Provider | Semantic Scholar |
|---|---|
| Author | Porikli, Fatih Murat |
| Copyright Year | 2006 |
| Abstract | We present a computationally inexpensive method for multi-modal image registration. Our approach employs a joint gradient similarity function that is applied only to a set high spatial gradient pixels. We obtain motion parameters by maximizing the similarity function by gradient ascent method, which secures a fast convergence. We apply our technique to the task of affine model based registration of 2D images which undergo large rigit motion, and show promising results. SPIE Conference Electro-Optical and Infrared Systems This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Research Laboratories, Inc.; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Research Laboratories, Inc. All rights reserved. Copyright c © Mitsubishi Electric Research Laboratories, Inc., 2006 201 Broadway, Cambridge, Massachusetts 02139 Fast Image Registration via Joint Gradient Maximization: Application to Multi-Modal Data Xue Mei† Fatih Porikli‡ †University of Maryland, College Park, MD, USA ‡Mitsubishi Electric Research Labs, Cambridge, MA, USA ABSTRACT We present a computationally inexpensive method for multi-modal image registration. Our approach employs a joint gradient similarity function that is applied only to a set high spatial gradient pixels. We obtain motion parameters by maximizing the similarity function by gradient ascent method, which secures a fast convergence. We apply our technique to the task of affine model based registration of 2D images which undergo large rigid motion, and show promising results. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://www.umiacs.umd.edu/~xuemei/files/SPIE2006_XueMei.pdf |
| Alternate Webpage(s) | http://www.porikli.com/pdfs/eurodefense2006-porikli.pdf |
| Alternate Webpage(s) | http://www.merl.com/papers/docs/TR2006-109.pdf |
| Alternate Webpage(s) | http://www.merl.com/publications/docs/TR2006-109.pdf |
| Alternate Webpage(s) | http://vision.poly.edu/~fporikli/pdfs/eurodefense2006-porikli.pdf |
| Language | English |
| Access Restriction | Open |
| Subject Keyword | Acknowledgment index Broadway (microprocessor) Convergence (action) Copy (object) Copyright Electroconvulsive Therapy Expectation–maximization algorithm Experiment Fees Gradient descent Image registration Laboratory Modal logic Muscle Rigidity Numerous Pixel Similarity measure Times Ascent registration - ActClass |
| Content Type | Text |
| Resource Type | Article |
