Automatic object segmentation in images with low depth of field

The paper describes an automatic object segmentation algorithm for images with low depth of field (DOF). The low DOF images are segmented into two regions, namely, focused objects and defocused background. A local variance image field (LVIF) can represent the pixel-wise spatial distribution of the high-frequency components in the image. However, applying a thresholding method to the LVIF for segmentation often yields blob-like errors in both focused and defocused regions. To eliminate these errors, a block-wise MRF (Markov random field) image model is employed for maximum a posteriori (MAP) segmentation. After the block-wise MAP segmentation, the image blocks in the object boundary are divided into smaller blocks. Then, they are reassigned to one of the neighboring objects through the watershed algorithm, which eventually yields a pixel-level segmentation. Experimental results show that the proposed method yields more accurate segmentation than the multiresolution wavelet-based segmentation method.