Title
Effects of hyperellipsoidal decision surfaces on image segmentation in artificial color
Abstract
Artificial color uses the projection of the spectrum into two or more broad, overlapping spectral bands to discriminate, pixel by pixel, among user-defined classes of objects. As initially practiced, it used a sequence of hyperspherical regions of the decision space to define class membership. Of course, a hypersphere is just a degenerate hyperellipsoid; thus, exploring the effect of loosening that degeneracy seemed appropriate. Initially, we use two-foci hyperellipsoids with a hyperellipsoidal distance metric to classify pixels with dramatic improvement in performance. We explore the work even further by allowing many foci and noting the effects of increased complexity of the decision surfaces. In the example case, three foci gave superior performance to one or two foci, but four added little improvement. (C) 2010 SPIE and IS&T. [DOI: 10.1117/1.3377146]
Year
DOI
Venue
2010
10.1117/1.3377146
JOURNAL OF ELECTRONIC IMAGING
Keywords
Field
DocType
pattern recognition,distance metric,spectrum,image segmentation
Computer vision,Degenerate energy levels,Pattern recognition,Focus (geometry),Computer science,Metric (mathematics),Hypersphere,Image segmentation,Degeneracy (mathematics),Artificial intelligence,Pixel,Spectral bands
Journal
Volume
Issue
ISSN
19
2
1017-9909
Citations 
PageRank 
References 
2
0.41
22
Authors
4
Name
Order
Citations
PageRank
Jian Fu1545.62
H. John Caulfield2443164.79
Dongsheng Wu361.55
Trent Montgomery420.41