Maximum likely scale estimation
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
A maximum likelihood local scale estimation principle is presented. An actual implementation of the estimation principle uses second order moments of multiple measurements at a fixed location in the image. These measurements consist of Gaussian derivatives possibly taken at several scales and/or having different derivative orders.
Although the principle is applicable to a wide variety of image models, the main focus here is on the Brownian model and its use for scale selection in natural images. Furthermore, in the examples provided, the simplifying assumption is made that the behavior of the measurements is completely characterized by all moments up to second order.
| Original language | English |
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| Title of host publication | Deep Structure, Singularities, and Computer Vision |
| Publisher | Springer |
| Publication date | 2005 |
| Pages | 146-156 |
| ISBN (Print) | 978-3-540-29836-6 |
| DOIs | |
| Publication status | Published - 2005 |
| Event | First International Workshop in Deep Structure, Singularities, and Computer Vision (DSSCV) - Maastricht, Netherlands Duration: 29 Nov 2010 → … Conference number: 1 |
Conference
| Conference | First International Workshop in Deep Structure, Singularities, and Computer Vision (DSSCV) |
|---|---|
| Nummer | 1 |
| Land | Netherlands |
| By | Maastricht |
| Periode | 29/11/2010 → … |
| Series | Lecture notes in computer science |
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| Volume | 3753/2005 |
| ISSN | 0302-9743 |
ID: 4941791