Journal article
Cross-weighted moments and affine invariants for image registration and matching
IEEE transactions on pattern analysis and machine intelligence, v 21(8), pp 804-814
01 Jan 1999
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
A framework for deriving a class of new global affine invariants for both object matching and positioning based on a novel concept of cross-weighted moments with fractional weights is presented. The fractional weight factor allows for a more flexible range to balance between the capability to discriminate between objects that differ only in small shape details and the sensitivity of small shape details to the presence of the noise. Moreover, it makes it possible to arrive at low order (zero order) affine invariants that are more robust than those derived from higher order regular moments. The affine transformation parameters are recovered from the zero and the first order cross-weighted moments without requiring any feature point correspondence information. The equations used to find the affine transformation parameters are linear algebraic. The sensitivity of the cross-weighted moment invariants to noise, missing data, and perspective effects is shown on real images
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Details
- Title
- Cross-weighted moments and affine invariants for image registration and matching
- Creators
- Zhengwei Yang - KLAF Cohen - Drexel University
- Publication Details
- IEEE transactions on pattern analysis and machine intelligence, v 21(8), pp 804-814
- Publisher
- The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Electrical and Computer Engineering
- Web of Science ID
- WOS:000081993000014
- Scopus ID
- 2-s2.0-0032645095
- Other Identifier
- 991019168256004721
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- Collaboration types
- Industry collaboration
- Domestic collaboration
- Web of Science research areas
- Computer Science, Artificial Intelligence
- Engineering, Electrical & Electronic