https://github.com/jalalilabucla/image-feature-detection-using-phase-stretch-transform
PST or Phase Stretch Transform is an operator that finds features in an image. PST implemented using MATLAB here, takes an intensity image I as its input, and returns a binary image out of the same size as I, with 1's where the function finds sharp transitions in I and 0's elsewhere.
https://github.com/jalalilabucla/image-feature-detection-using-phase-stretch-transform
edge-detection jalali matlab phase-stretch-transform pst python texture-analysis ucla
Last synced: 6 months ago
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PST or Phase Stretch Transform is an operator that finds features in an image. PST implemented using MATLAB here, takes an intensity image I as its input, and returns a binary image out of the same size as I, with 1's where the function finds sharp transitions in I and 0's elsewhere.
- Host: GitHub
- URL: https://github.com/jalalilabucla/image-feature-detection-using-phase-stretch-transform
- Owner: JalaliLabUCLA
- License: other
- Created: 2016-02-10T04:36:24.000Z (over 9 years ago)
- Default Branch: master
- Last Pushed: 2021-12-16T23:16:12.000Z (almost 4 years ago)
- Last Synced: 2025-04-04T01:37:17.508Z (6 months ago)
- Topics: edge-detection, jalali, matlab, phase-stretch-transform, pst, python, texture-analysis, ucla
- Language: Python
- Homepage: https://en.wikipedia.org/wiki/Phase_stretch_transform
- Size: 506 KB
- Stars: 838
- Watchers: 96
- Forks: 222
- Open Issues: 7
-
Metadata Files:
- Readme: README.md
- License: LICENSE.md
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README
# Image-feature-detection-using-Phase-Stretch-Transform
Phase Stretch Transform (PST) is an operator that finds features
in an image. PST takes an intensity image I as its input, and returns
a binary image out of the same size as I, with 1's where the function
finds sharp transitions in I and 0's elsewhere. PST function is also
able to return the detected features in gray scale level (i.e.
without thresholding).
In PST, the image is first filtered by passing through a
smoothing filter followed by application of a nonlinear
frequency-dependent phase described by the PST phase kernel. The
output of the transform is the phase in the spatial domain. The main
step is the 2-D phase function (PST phase kernel) which is typically
applied in the frequency domain. The amount of phase applied to the
image is frequency dependent with higher amount of phase applied to
higher frequency features of the image. Since sharp transitions,
such as edges and corners, contain higher frequencies, PST
emphasizes the edge information. Features can be further enhanced by
applying thresholding and morphological operations. For more
information please visit:
https://en.wikipedia.org/wiki/Phase_stretch_transform
## Copyright
PST function is developed in Jalali Lab at University of
California, Los Angeles (UCLA). PST is a spin-off from research on
the photonic time stretch technique in Jalali lab at UCLA. More
information about the technique can be found on our group website:
http://www.photonics.ucla.edu
This function is provided for research purposes only. A license
must be obtained from the University of California, Los Angeles for
any commercial applications. The software is protected under a US
patent.
## Citations
1. M. H. Asghari, and B. Jalali, "Edge detection in digital
images using dispersive phase stretch," International Journal of
Biomedical Imaging, Vol. 2015, Article ID 687819, pp. 1-6 (2015).
2. M. H. Asghari, and B. Jalali, "Physics-inspired image edge
detection," IEEE Global Signal and Information Processing Symposium
(GlobalSIP 2014), paper: WdBD-L.1, Atlanta, December 2014.
3. M. Suthar, H. Asghari, and B. Jalali, "Feature Enhancement in Visually
Impaired Images", IEEE Access 6 (2018): 1407-1415.
4. Y. Han, and B. Jalali, "Photonic time-stretched analog-to-digital converter:
Fundamental concepts and practical considerations", Journal of
Lightwave Technology 21, no. 12 (2003): 3085.
#
Copyright (c) 2016, Jalali Lab All rights reserved.