JalaliLabUCLA/Image-feature-detection-using-Phase-Stretch-Transform
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
- 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).
- 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.
- M. Suthar, H. Asghari, and B. Jalali, "Feature Enhancement in Visually Impaired Images", IEEE Access 6 (2018): 1407-1415.
- 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.
引用格式
Jalali Lab Jalalilab (2024). JalaliLabUCLA/Image-feature-detection-using-Phase-Stretch-Transform (https://www.mathworks.com/matlabcentral/fileexchange/55330-jalalilabucla-image-feature-detection-using-phase-stretch-transform), MATLAB Central File Exchange. 检索来源 .
MATLAB 版本兼容性
平台兼容性
Windows macOS Linux类别
- Image Processing and Computer Vision > Computer Vision Toolbox > Feature Detection and Extraction >
- Sciences > Biological and Health Sciences > Biomedical Imaging >
- Sciences > Physics > Atomic, Molecular & Optical > Optics & Lasers > Optics >
- Image Processing and Computer Vision > Image Processing Toolbox > Image Filtering and Enhancement > Contrast Adjustment >
- MATLAB > Graphics > Images > Read, Write, and Modify Image >
- Sciences > Physics > Atomic, Molecular & Optical > Photonics >
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致谢
参考作品: Image overlay
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Start Hunting!PST code released march 07 2016/
PST code released march 07 2016/PST-feature-detection/
无法下载基于 GitHub 默认分支的版本
版本 | 已发布 | 发行说明 | |
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1.1.0.0 | To make the code easier to understand for users, we put the default feature detection to analog feature detection. |
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1.0.0.0 | Version 1 |
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