Last modified: November 20, 2009
Contents
Image [OneBit] medial_axis_transform_hs ()
Operates on: | Image [OneBit] |
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Returns: | Image [OneBit] |
Category: | Filter/Thinning |
Defined in: | thinning.py |
Author: | Michael Droettboom and Karl MacMillan (based on code by Øivind Due Trier and Qian Huang) |
Warning
No documentation written.
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: | Filter/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] |
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Returns: | Image [OneBit] |
Category: | Filter/Thinning |
Defined in: | thinning.py |
Author: | Michael Droettboom and Karl MacMillan (based on code by Øivind Due Trier and Qian Huang) |
Warning
No documentation written.
Example 1: thin_hs()
Image [OneBit] thin_hs_large_image ()
Operates on: | Image [OneBit] |
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Returns: | Image [OneBit] |
Category: | Filter/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: | Filter/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: | Filter/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()
Image [Float] create_gabor_filter (float orientation = 45.00, float frequency = 0.38, int direction = 5)
Operates on: | Image [GreyScale] |
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Returns: | Image [Float] |
Category: | Filter |
Defined in: | misc_filters.py |
Author: | Ullrich Köthe (wrapped from VIGRA by Uma Kompella) |
Computes the convolution of an image with a two dimensional Gabor function. The result is returned as a float image.
The orientation is given in radians, the other parameters are the center frequency (for example 0.375 or smaller) and the two angular and radial sigmas of the gabor filter.
The energy of the filter is explicitly normalized to 1.0.
Example 1: create_gabor_filter()
Image [OneBit] outline ()
Operates on: | Image [OneBit] |
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Returns: | Image [OneBit] |
Category: | Filter |
Defined in: | misc_filters.py |
Author: | Michael Droettboom and Karl MacMillan |
Traces the outline of the image. This result is obtained by dilating the image and then XOR'ing the result with the original.
Example 1: outline()