The First Stage in Two-Stage Template Matching

This paper formulates the problem encountered in the first stage of two-stage, binary template matching as a set of hypotheses to be tested, including a hypothesis of ``no object.'' Two new statistics R and G are proposed, based on a likelihood ratio, and are compared to the sum of absolut...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on pattern analysis and machine intelligence 1985-11, Vol.PAMI-7 (6), p.700-707
Main Authors: Li, Xiaobo, Dubes, Richard C.
Format: Article
Language:English
Subjects:
Application software
Applied sciences
Artificial intelligence
Computer errors
Computer science
Computer science
control theory
systems
Distortion measurement
Exact sciences and technology
Image registration
Landsat imagery
Pattern recognition. Digital image processing. Computational geometry
Pixel
Remote sensing
Satellites
similarity measure
Statistical analysis
Statistics
template matching
Testing
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This paper formulates the problem encountered in the first stage of two-stage, binary template matching as a set of hypotheses to be tested, including a hypothesis of ``no object.'' Two new statistics R and G are proposed, based on a likelihood ratio, and are compared to the sum of absolute differences and a correlation measure by analytical approximations and Monte Carlo experiments. Statistical power and a measure of sensitivity to the true location of the object are the criteria. Parameters are the numbers of 1's in object and image, subtemplate size, and parameters reflecting intensity distortion between template and object. One of the proposed statistics R is much more computationally intensive than the other G. Although R is more powerful than G and the other statistics, G is generally more sensitive to the true object location. Statistic G is also more powerful than the sum of absolute differences and correlation. All statistics are robust to incomplete knowledge of distortion parameters. Experiments on Landsat images confirm the sensitivity of G and recommend it for application in the first stage.
ISSN:0162-8828
1939-3539
DOI:10.1109/TPAMI.1985.4767726