Segmenting a Noisy Low-Depth-of-Field Image Using Adaptive Second-Order Statistics
- Title
- Segmenting a Noisy Low-Depth-of-Field Image Using Adaptive Second-Order Statistics
- Author
- 정정화
- Keywords
- Feature transform; image segmentation; low depth-of-field; object detection; region of interest (ROI); QUALITY ASSESSMENT; SEGMENTATION; TRACKING; OBJECTS
- Issue Date
- 2014-09
- Publisher
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
- Citation
- IEEE SIGNAL PROCESSING LETTERS, 2015, 22(3), P.275-278
- Abstract
- We propose a novel algorithm to segment a low depth-of-field (DOF) image into its focused region-of-interest (ROI) and defocused background using adaptive second-order statistics (ASOS). Most previous methods depend on the assumption that the images are in noise-free conditions, which leads to high false positive rates in noisy images. In this letter, we introduce a novel image segmentation algorithm for noisy low-DOF images. Specifically, we propose a novel feature transform method, called ASOS, which indicates the spatial distribution of the high-frequency components in the face of noisy low-DOF images. Experimental results demonstrate that the proposed method is effective for image segmentation in noisy images compared to several state-of-the-art methods proposed in the literature.
- URI
- http://ieeexplore.ieee.org/document/6899656/http://hdl.handle.net/20.500.11754/52658
- ISSN
- 1070-9908; 1558-2361
- DOI
- 10.1109/LSP.2014.2357792
- Appears in Collections:
- COLLEGE OF ENGINEERING[S](공과대학) > ELECTRONIC ENGINEERING(융합전자공학부) > Articles
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