Predicting performance of object recognition
Boshra, M.
Bhanu, B.
AuthenTec Inc., Melbourne, FL;
This paper appears in: Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publication Date: Sep 2000
Volume: 22,
Issue: 9
On page(s): 956-969
ISSN: 0162-8828
References Cited: 12
CODEN: ITPIDJ
INSPEC Accession Number: 6744985
Digital Object Identifier: 10.1109/34.877519
Current Version Published: 2002-08-06
Abstract
We present a method for predicting fundamental performance of
object recognition. We assume that both scene data and model objects are
represented by 2D point features and a data/model match is evaluated
using a vote-based criterion. The proposed method considers data
distortion factors such as uncertainty, occlusion, and clutter, in
addition to model similarity. This is unlike previous approaches, which
consider only a subset of these factors. Performance is predicted in two
stages. In the first stage, the similarity between every pair of model
objects is captured by comparing their structures as a function of the
relative transformation between them. In the second stage, the
similarity information is used along with statistical models of the
data-distortion factors to determine an upper bound on the probability
of recognition error. This bound is directly used to determine a lower
bound on the probability of correct recognition. The validity of the
method is experimentally demonstrated using real synthetic aperture
radar (SAR) data
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