Detection of Long Edges on a Computational Budget
SIAM Journal on Imaging Sciences(2015)
摘要
Edge detection is a challenging, important task in image analysis.
Various applications require real-time detection of long edges in large and noisy images, possibly under limited
computational resources.
While standard edge detection methods are computationally fast, they perform well only at low levels of noise.
Modern sophisticated methods, in contrast, are robust to noise, but may be too slow for real-time processing of
large images.
This raises the following question, which is the focus of our paper:
How well can one detect long edges in noisy images under severe computational constraints that allow only a fraction
of all image pixels to be processed?
We make several theoretical and practical contributions regarding this problem.
We develop possibly the first sublinear algorithm to detect long straight edges in noisy images.
In addition, we theoretically analyze the inevitable tradeoff between its detection performance and the allowed
computational budget.
Finally, we demonstrate its competitive performance on both simulated and real images.
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关键词
edge detection,sublinear algorithms,group testing,design of experiments,68U10,68W40,62K99,62F03,62F30
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