NDLI: Image Feature Extraction Using Diameter-Limited Gradient Direction Histograms

Content Provider	IEEE Xplore Digital Library
Author	Birk, J. Kelley, R. Chen, N. Wilson, L.
Copyright Year	1979
Abstract	Features extracted by operators which examine diameter-limited gradient direction histograms are important because they describe images of industrial workpieces efficiently and have the potential for rapid computation via special purpose hardware. When such operators are passed over an image in raster fashion, features such as the following are detected: a strong peak in the direction histogram indicating the presence of a relatively straight edge, a second strong direction indicating a corner, a wide direction group indicating a curved edge, and a uniform distribution of directions over the histogram indicating small holes. Diameter-limited optimization can be used to substantially reduce the number of pixels which have been given feature labels by such operators without losing descriptive power. Feature labels for direction histograms having a second strong direction, a wide direction group, or a uniform distribution might be retained only if all other pixels within a circular aperture have a lower bin value. Pixels with a strong direction label might be retained only if all other pixels within a circular aperture and along the gradient direction have a smaller bin value. Tracking can then be applied to the remaining strong direction pixels in the direction perpendicular to the gradient to achieve representation of edges by endpoints. Experimental results indicate that descriptions which are compatible with human interpretation can be achieved.
Sponsorship	IEEE Computer Society
Starting Page	228
Ending Page	235
Page Count	8
File Size	4855966
File Format	PDF
ISSN	01628828
Volume Number	PAMI-1
Issue Number	2
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1979-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Feature extraction Histograms Computer industry Hardware Image edge detection Apertures Pixel Humans Computer vision Image representation vision for industrial parts diameter limited computations gradient direction histograms image feature extraction image representation
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Artificial Intelligence Computational Theory and Mathematics Computer Vision and Pattern Recognition Software

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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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