Improving change detection results with knowledge of registration uncertainty
| Autor: | Brian D. Rigling, Andrew J. Lingg |
|---|---|
| Rok vydání: | 2015 |
| Předmět: |
Pixel
Computer science business.industry ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Image registration Pattern recognition Variance (accounting) Covariance Transformation (function) Feature (computer vision) Computer Science::Computer Vision and Pattern Recognition Computer vision Artificial intelligence business Change detection |
| Zdroj: | SPIE Proceedings. |
| ISSN: | 0277-786X |
| DOI: | 10.1117/12.2176438 |
| Popis: | Uncertainty in the registration between two images remains a problematic source of error in performing change detection between them. While a number of methods have been developed for reducing the impact of registration error in change detection, none of these methods are based upon a statistical characterization of the uncertainty in the estimate of the registration transformation. When utilizing a feature-point based registration algorithm, we can compute a Cramer-Rao lower bound (CRLB) on the estimate of the registration transformation based on an assumed covariance in the feature-point locations. This information can be used to predict the variance on the location at which pixels will appear in the registered image, which can be used to estimate the bias and variance introduced into the pixel intensities by registration uncertainty. Here, we use this information to improve change detection performance and verify this improvement with simulated and experimental results. |
| Databáze: | OpenAIRE |
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