Paper Info
Title
Gradient-Oriented Profiles for Unsupervised Boundary Classification
Abstract
We present a method for unsupervised boundary classification by producing and analyzing intensity profiles.Each profile is created by sampling an ellipsoidal neighborhood of voxels oriented along the image gradient.The profile is analyzed via non-linear optimization to find the best fitting cumulative Gaussian. The parameters of the cumulative Gaussian parameterize the boundary directly yielding (1) extrapolated intensity values for voxels located far inside and outside of the boundary,(2)estimates boundary location and boundary width.For these parameters, intrinsic measures of confidence are established to eliminate low-confidence parameter estimates. Neighborhoods overlap considerably,yielding sufficient high-confidence estimates for a thorough survey of the boundary.Gradient oriented profiles are demonstrated on artificially generated three-dimensional test data and proved to accurately parameterize and classify the boundary.
Year
DOI
Venue
2000
10.1109/AIPRW.2000.953627
AIPR
Keywords
Field
DocType
gaussian processes,nonlinear optimization,sampling methods,cumulant,testing,parameter estimation,three dimensional,detectors,robots,biomedical engineering,image analysis,image classification,edge detection
Voxel,Image gradient,Pattern recognition,Mathematical analysis,Edge detection,Gaussian,Gaussian process,Sampling (statistics),Artificial intelligence,Estimation theory,Contextual image classification,Mathematics
Conference
ISBN
Citations 
PageRank 
0-7695-0978-9
0
0.34
References 
Authors
2
2
Name
Order
Citations
PageRank
Robert J. Tamburo1383.59
George D. Stetten214622.70