Unsupervised Multiresolution Segmentation
for Images with Low Depth of Field
James Z. Wang, Jia Li
Pennsylvania State University
Robert M. Gray and Gio Wiederhold
Stanford University, Stanford, CA 94305
Abstract:
Unsupervised segmentation of images with low depth of field (DOF) is
highly useful in various applications including image enhancement for
digital cameras, target recognition, image indexing for content-based
retrieval, and 3-D microscopic image analysis. This paper describes a
novel multiresolution image segmentation algorithm for low DOF images.
The algorithm is designed to separate a sharply focused
object-of-interest from other foreground or background objects. The
algorithm is fully automatic in that all parameters are image
independent. A multiscale approach based on high frequency wavelet
coefficients and their statistics is used to perform context-dependent
classification of individual blocks of the image. Unlike other
edge-based approaches, our algorithm does not rely on the process of
connecting object boundaries. The algorithm has achieved high
accuracy when tested on more than 100 low DOF images, many with
inhomogeneous foreground or background distractions. Compared with
the state of the art algorithms, this new algorithm provides better
accuracy at higher speed.
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Citation:
James Z. Wang, Jia Li, Robert M. Gray and Gio Wiederhold,
``Unsupervised Multiresolution Segmentation for Images with Low Depth
of Field,'' IEEE Transactions on Pattern Analysis and Machine
Intelligence, vol. 23, no. 1, pp. 85-90, 2001.
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Last Modified:
Mon Dec 4 16:34:52 EST 2000
© 2000, James Z. Wang