DocumentCode
1555753
Title
Finding shape axes using magnetic fields
Author
Shroff, Harsh ; Ben-Arie, Jezekiel
Author_Institution
Dept. of Electr. Eng. & Comput. Sci., Illinois Univ., Chicago, IL, USA
Volume
8
Issue
10
fYear
1999
fDate
10/1/1999 12:00:00 AM
Firstpage
1388
Lastpage
1394
Abstract
This paper presents a novel method, based on magnetic field principles, for obtaining the axes of shapes. The method is based on directional information of the shape´s boundary. By simulating a parallel algorithm, we are able to generate the inner as well as the outer axes (axes of concavities) of the shape. The preprocessing phase for this algorithm involves obtaining the shape´s gradient. Each point of the gradient is substituted by a minute magnetic dipole. The cumulative magnetic field due to these dipoles is accumulated at all points in the image in a one-pass algorithm. The magnitude of the final magnetic vector field has valleys that are created from mutual and directionally balanced cancellations of opposing boundary segments. These valleys signify the axes of the shape. The axes are obtained by performing a valley search. The magnetic field modeling (MFM) method has an advantage over previous approaches since it utilizes not only the location information of the boundary, but also its directional information. As demonstrated, experimental results of the MFM method are much improved, compared to other skeletonization algorithms which tend to generate spurious and noisy axes
Keywords
image thinning; magnetic fields; magnetic moments; parallel algorithms; boundary segments; computer vision; concavities axes; directional information; experimental results; gradient; grey scale images; image understanding; inner axes; magnetic dipole; magnetic field modeling; magnetic vector field; noisy axes; one-pass algorithm; outer axes; parallel algorithm simulation; preprocessing phase; shape axes; shape boundary; skeletonization algorithms; spurious axes; Fires; Image segmentation; Magnetic fields; Magnetic force microscopy; Magnetic noise; Noise generators; Noise shaping; Parallel algorithms; Shape; Skeleton;
fLanguage
English
Journal_Title
Image Processing, IEEE Transactions on
Publisher
ieee
ISSN
1057-7149
Type
jour
DOI
10.1109/83.791964
Filename
791964
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