Franc Solina and Aleš Leonardis (1998) Proper scale for modeling visual data. Image and Vision Computing, 16 (2). pp. 89-98.
Abstract
We propose a method for determining the proper scale for modeling visual data. An efficient architecture for selective image modeling is discussed which selects models according to the task, the nature of the scene and the computational constraints. We give an example in which models of different scales are recovered in parallel and show that this redundant representation can effectively be pruned using the criterion of Minimal Description Length. Models that are selected in the final description indicate the appropriate scale of observation.
| Item Type: | Article |
|---|
| Keywords: | scale, image modeling, vision architecture |
|---|
| Language of Content: | English |
|---|
| Related URLs: | |
|---|
| Institution: | University of Ljubljana |
|---|
| Department: | Faculty of Computer and Information Science |
|---|
| Divisions: | Faculty of Computer and Information Science > Computer Vision Laboratory |
|---|
| Item ID: | 33 |
|---|
| Date Deposited: | 22 Jan 2003 |
|---|
| Last Modified: | 12 Dec 2013 15:42 |
|---|
| URI: | http://eprints.fri.uni-lj.si/id/eprint/33 |
|---|
Actions (login required)
![[img]](http://eprints.fri.uni-lj.si/style/images/fileicons/application_pdf.png)
