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A Min-Max Medial Axis Transformation

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2 Author(s)
Shmuel Peleg ; Computer Vision Laboratory, Computer Science Center, University of Maryland, College Park, MD 20742. ; Azriel Rosenfeld

Blum's medial axis transformation (MAT) of the set S of 1's in a binary picture can be defined by an iterative shrinking and reexpanding process which detects ``corners'' on the contours of constant distance from S¿, and thereby yields a ``skeleton'' of S. For unsegmented (gray level) pictures, one can use an analogous definition, in which local MIN and MAX operations play the roles of shrinking and expanding, to compute a ``MMMAT value'' at each point of the picture. The set of points having high values defines a good ``skeleton'' for the set of high-gray level points in the given picture.

Published in:

IEEE Transactions on Pattern Analysis and Machine Intelligence  (Volume:PAMI-3 ,  Issue: 2 )