Extending the Scale Invariant Feature Transform
Descriptor into the Color Domain
Robert H. Luke*, James M. Keller*, Jesus Chamorro-Martinez+
*Electrical and Computer Engineering,
University of Missouri
Columbia, MO, USA, 65202
+Computer Science and Artificial Intelligence,
University of Granada Granada, Spain
Abstract
In recent years, Lowe’s Scale Invariant Feature Transform
(SIFT) algorithm has become a widely used tool for object
recognition. One shortcoming is that it only works on
grayscale image intensities. In this paper, we propose
extending the SIFT descriptor to also incorporate color
information in a novel fashion. The keypoint descriptor
proposed in this paper is a superset of the original feature
vector and works exactly the same on grayscale or single
band images as well as in regions of a color image that are
essentially monochromatic. Examples are presented to
demonstrate the properties and matching capability of the
new descriptor in color image processing.
Keywords: Color
Computer Vision, Scale Invariant Feature Transform, Color
Histogram, Object Recognition.
(
P1150830266,
3.41 MB)
BibTex:
@ARTICLE{P1150830266,
AUTHOR = {Robert H. Luke and James M. Keller and
Jesus Chamorro-Martinez},
TITLE = {Extending
the Scale Invariant Feature Transform
Descriptor into the Color Domain },
JOURNAL ={ICGST
International Journal on Graphics, Vision
and Image Processing, GVIP},
YEAR = {2008},
VOLUME = {08},
ISSUE ={IV},
PAGES={35--43}
}
(P1150830266,
3.41 MB)
