DocumentCode :
2337580
Title :
Automatic object segmentation in images with low depth of field
Author :
Won, Chee Sun ; Pyun, Kyungsuk ; Gray, Robert M.
Author_Institution :
Dept. of Electron. Eng., Dongguk Univ., Seoul, South Korea
Volume :
3
fYear :
2002
fDate :
24-28 June 2002
Firstpage :
805
Abstract :
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.
Keywords :
Markov processes; edge detection; image segmentation; maximum likelihood estimation; object detection; MAP segmentation; MRF; Markov random field; automatic object segmentation; defocused background; depth of field; edge detection; focused objects; image segmentation; local variance image field; maximum a posteriori segmentation; Energy resolution; Filters; Focusing; Frequency domain analysis; Image edge detection; Image segmentation; Information systems; Object segmentation; Pixel; Sun;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Image Processing. 2002. Proceedings. 2002 International Conference on
ISSN :
1522-4880
Print_ISBN :
0-7803-7622-6
Type :
conf
DOI :
10.1109/ICIP.2002.1039094
Filename :
1039094
Link To Document :
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