Automatic object segmentation in images with low depth of field

Chee Sun Won, Kyungsuk Pyun, Robert M. Gray

Research output: Contribution to conferencePaperpeer-review

45 Scopus citations

Abstract

This 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 the thresholding method to the LVIF for the segmentation often yields blob-like errors in both focused and defocused regions. To eliminate these errors, a block-wise MRF 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.

Original languageEnglish
PagesIII/805-III/808
StatePublished - 2002
EventInternational Conference on Image Processing (ICIP'02) - Rochester, NY, United States
Duration: 22 Sep 200225 Sep 2002

Conference

ConferenceInternational Conference on Image Processing (ICIP'02)
Country/TerritoryUnited States
CityRochester, NY
Period22/09/0225/09/02

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