Performance prediction and validation for object recognition
Boshra, M.
Bhanu, B.
Center for Res. in Intelligent Syst., California Univ., Riverside, CA;
This paper appears in: Computer Vision and Pattern Recognition, 1999. IEEE Computer Society Conference on.
Publication Date: 1999
Volume: 2,
On page(s): -386 Vol. 2
Meeting Date: 06/23/1999 - 06/25/1999
Location: Fort Collins, CO, USA
ISBN: 0-7695-0149-4
References Cited: 5
INSPEC Accession Number: 6338782
Digital Object Identifier: 10.1109/CVPR.1999.784665
Current Version Published: 2002-08-06
Abstract
This paper addresses the problem of predicting fundamental
performance of vote-based object recognition using 2-D point features.
It presents a method for predicting a tight lower bound on performance.
Unlike previous approaches, the proposed method considers
data-distortion factors, namely uncertainty, occlusion, and clutter, in
addition to model similarity, simultaneously. The similarity between
every pair of model objects is captured by comparing their structures as
a function of the relative transformation between them. This 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 synthetic aperture radar (SAR) data obtained under
different depression angles and target configurations
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