Home

About

Advanced Search

Browse by Discipline

Scientific Societies

E-print Alerts

Add E-prints

E-print Network
FAQHELPSITE MAPCONTACT US


  Advanced Search  

 
Bayesian recognition of targets by parts in second generation forward looking infrared images
 

Summary: Bayesian recognition of targets by parts in second generation forward
looking infrared images
D. Naira
, J.K. Aggarwalb,*
a
National Instruments, 11500 N. Mopac Expwy, Austin, TX 78759, USA
b
Computer and Vision Research Center, Department of Electrical and Computer Engineering, ENS 522, The University of Texas at Austin, Austin,
TX 78712-1084, USA
Received 20 January 1998; received in revised form 25 November 1999; accepted 25 November 1999
Abstract
This paper presents a system for the recognition of targets in second generation forward looking infrared images (FLIR). The recognition
of targets is based on a methodology for recognition of two-dimensional objects using object parts. The methodology is based on a
hierarchical, modular structure for object recognition. In the most general form, the lowest level consists of classifiers that are trained to
recognize the class of the input object, while at the next level, classifiers are trained to recognize specific objects. At each level, the objects are
recognized by their parts, and thus each classifier is made up of modules, each of which is an expert on a specific part of the object. Each
modular expert is trained to recognize one part under different viewing angles and transformations. A Bayesian realization of the proposed
methodology is presented in this paper, in which the expert modules represent the probability density functions of each part, modeled as a
mixture of densities to incorporate different views (aspects) of each part. Recognition relies on the sequential presentation of the parts to the
system, without using any relational information between the parts. A new method to decompose a target into its parts and results obtained for

  

Source: Aggarwal, J. K. - Department of Electrical and Computer Engineering, University of Texas at Austin

 

Collections: Computer Technologies and Information Sciences; Engineering