Last modified: February 14, 2023
Contents
Image [OneBit] medial_axis_transform_hs ()
Operates on: | Image [OneBit] |
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Returns: | Image [OneBit] |
Category: | Thinning |
Defined in: | thinning.py |
Author: | Michael Droettboom and Karl MacMillan (based on code by Øivind Due Trier and Qian Huang) |
"This function is an alias for thin_hs.
Example 1: medial_axis_transform_hs()
Image [OneBit] medial_axis_transform_large_image_hs ()
Operates on: | Image [OneBit] |
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Returns: | Image [OneBit] |
Category: | Thinning |
Defined in: | thinning.py |
Author: | Michael Droettboom and Karl MacMillan (based on code by Øivind Due Trier and Qian Huang) |
This function is an alias for thin_hs_large_image.
Example 1: medial_axis_transform_large_image_hs()
Image [OneBit] thin_hs ()
Operates on: | Image [OneBit] |
---|---|
Returns: | Image [OneBit] |
Category: | Thinning |
Defined in: | thinning.py |
Author: | Michael Droettboom and Karl MacMillan (based on code by Øivind Due Trier and Qian Huang) |
Derives the medial axis transformation from a ONEBIT image using the Haralick and Shapiro algorithm.
Unlike thin_zs and thin_lc, this function performs a medial axis transformation, and the ends of the skeleton extend to the corners of the original image.
Consider using thin_hs_large_image instead, for faster performance on large images with a lot of connected components.
R. M. Haralick and L. G. Shapiro. 1992. Computer and Robot Vision, Vol. 1, Chapter 5 (especially 5.10.1). Reading, MA: Addison-Wesley.
Example 1: thin_hs()
Image [OneBit] thin_hs_large_image ()
Operates on: | Image [OneBit] |
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Returns: | Image [OneBit] |
Category: | Thinning |
Defined in: | thinning.py |
Author: | Michael Droettboom and Karl MacMillan (based on code by Øivind Due Trier and Qian Huang) |
Thins (skeletonizes) a ONEBIT image using the Haralick and Shapiro algorithm.
Unlike thin_hs, this algorithm performs skeletonization on one connected component at a time. On large images with a lot of connected components, this can be significantly faster. However, for small images with a single connected component, this has unnecessary overhead, which is why both versions are included. Please note cc_analysis results in a labelled image, which you can reset afterwards with reset_onebit_image().
Example 1: thin_hs_large_image()
Image [OneBit] thin_lc ()
Operates on: | Image [OneBit] |
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Returns: | Image [OneBit] |
Category: | Thinning |
Defined in: | thinning.py |
Author: | Michael Droettboom and Karl MacMillan (based on code by Øivind Due Trier and Qian Huang) |
Thins (skeletonizes) a ONEBIT image using the Lee and Chen algorithm.
This function is a simple extension to the Zhang and Suen algorithm in thin_zs that ensure that no two pixels are more than 4-connected.
The resulting skeleton is not a medial axis transformation, and the ends of the skeleton will not extend to the edges of the original image.
H.-J. Lee and B. Chen. 1992. Recognition of handwritten Chinese characters via short line segments. Pattern Recognition. 25(5) 543-552.
Example 1: thin_lc()
Image [OneBit] thin_zs ()
Operates on: | Image [OneBit] |
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Returns: | Image [OneBit] |
Category: | Thinning |
Defined in: | thinning.py |
Author: | Michael Droettboom and Karl MacMillan (based on code by Øivind Due Trier and Qian Huang) |
Thins (skeletonizes) a ONEBIT image using the Zhang and Suen algorithm.
The resulting skeleton is not a medial axis transformation, and the ends of the skeleton will not extend to the edges of the original image.
T. Y. Zhang and C. Y. Suen. 1984. A Fast Parallel Algorithm for Thinning Digital Patterns., Communications of ACM, 2(3).
R. C. Gonzalez and P. Wintz. 1987 Digital Image Processing., 2. edition. 398-402.
Example 1: thin_zs()