Share Email Print
cover

Proceedings Paper

Object recognition by cost minimization
Author(s): Liu Lu; Fang Luo; Nanno J. Mulder
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

Since the analogy between images and statistical mechanics systems was made, numerous research projects have been done to solve problems of image processing by the use of iterative methods. In this paper, we investigate cost functions in image processing and present a general expression and common requirements for cost functions. Meanwhile, we construct cost functions for object recognition. Although the cost functions are defined with geometric errors or radiometric errors, they measure errors in the two domains and their minimum corresponds to the same prediction without any errors in both domains. An optimization procedure is performed to reach the expected result with the minimum cost. The most noticeable characteristic of the method is that it is not sensitive to noise and works remarkably well in the presence of high noise level.

Paper Details

Date Published: 17 August 1994
PDF: 6 pages
Proc. SPIE 2357, ISPRS Commission III Symposium: Spatial Information from Digital Photogrammetry and Computer Vision, (17 August 1994); doi: 10.1117/12.182904
Show Author Affiliations
Liu Lu, ITC (Netherlands)
Fang Luo, ITC (Netherlands)
Nanno J. Mulder, ITC (Netherlands)


Published in SPIE Proceedings Vol. 2357:
ISPRS Commission III Symposium: Spatial Information from Digital Photogrammetry and Computer Vision
Heinrich Ebner; Christian Heipke; Konrad Eder, Editor(s)

© SPIE. Terms of Use
Back to Top