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