Noise Reduction using Mean Shift Algorithm for Estimating 3D Shape
The technique to estimate the three-dimensional (3D) geometry of an object from a sequence of images obtained at different focus settings is called shape from focus (SFF). In SFF, the measure of focus – sharpness – is the crucial part for final 3D shape estimation. However, it is difficult to comput...
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| Main Authors: | , , |
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| Format: | Citation Index Journal |
| Published: |
Maney Publishing
2011
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| Subjects: | |
| Online Access: | http://eprints.utp.edu.my/6613/1/ims053.pdf http://www.ingentaconnect.com/content/maney/isj/pre-prints/136821910X12867873897553 http://eprints.utp.edu.my/6613/ |
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| Institution: | Universiti Teknologi Petronas |
| Summary: | The technique to estimate the three-dimensional (3D) geometry of an object from a sequence of images obtained at different focus settings is called shape from focus (SFF). In SFF, the measure of focus – sharpness – is the crucial part for final 3D shape estimation. However, it is difficult to compute accurate and precise focus value because of the noise presence during the image acquisition by imaging system. Various noise filters can be employed to tackle this problem but they also remove the sharpness information in addition to the noise. In this paper, we propose a method based on mean shift algorithm to remove noise introduced by the imaging process while minimizing loss of edges. We test the algorithm in the presence of Gaussian noise and Impulse noise. Experimental results show that the proposed algorithm based on the mean shift algorithm provides better results than the traditional focus measures in the presence of the above mentioned two types of noise. |
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