Multiresolution Object-of-Interest Detection for Images
with Low Depth of Field
Jia Li, James Ze Wang, Robert M. Gray and Gio Wiederhold
Stanford University, Stanford, CA 94305
Abstract:
This paper describes a novel multiresolution image segmentation
algorithm for separating sharply focused objects-of-interest from
other foreground or background objects in low depth of field (DOF)
images, such as sports, telephoto, macro, and microscopic images. The
algorithm takes a multiscale context-dependent approach to segment
images based on features extracted from wavelet coefficients in high
frequency bands. The algorithm is fully automatic in that all
parameters are image independent. Experiments with the algorithm on
more than 100 low DOF images have shown results close to the human
segmentation of these images. Besides high accuracy, the algorithm also
provides high speed. A 768 x 512 pixel image can be segmented
within two seconds on a Pentium Pro 300MHz PC.
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Last Modified:
Fri Oct 2 00:35:07 PDT 1998
© 1998, James Z. Wang