Unsupervised multiresolution segmentation for images with low depth of field

Unsupervised segmentation of images with law depth of field (DOF) is highly useful in various applications including image enhancement for digital cam eras, 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 b ackground objects. The algorithm is fully automatic in that all parameters are image independent. A multiscale approach based on high frequency wavele t coefficients and their statistics is used to perform context-dependent cl assification of individual blocks of the image. Unlike other edge-based app roaches, our algorithm does not rely on the process of connecting object bo undaries. The algorithm has achieved high accuracy when tested on more than 100 low DOF images. many with inhomogeneous foreground or background distr actions. Compared with the state of the art algorithms, this new algorithm provides better accuracy at higher speed.