Python Implementation of Color texture image segmentation based on neutrosophic set and wavelet transformation paper
- Python 3.8.3
- opencv-python 4.3.0.36, numba 0.50, numpy 1.19, scipy 1.4.1, scikit-learn 0.23.1, PyWavelets 1.1.1
Color texture image segmentation based on neutrosophic set and wavelet transformation paper python implementation
License: MIT License
Python Implementation of Color texture image segmentation based on neutrosophic set and wavelet transformation paper
- Python 3.8.3
- opencv-python 4.3.0.36, numba 0.50, numpy 1.19, scipy 1.4.1, scikit-learn 0.23.1, PyWavelets 1.1.1
Hi! Thanks for your implementation of this paper it has been very helpful and I've learnt some cool efficiency tricks from it.
I have a doubt about the vectorized function Alpha_I_Pixel:
@jit(nopython=True)
def Alpha_I_Pixel(item,minVal, maxVal):
return (item * minVal) / (maxVal - minVal)
I don't see in the paper where this operation is applied, in equation 15 it applies an operation equal to T_or_I_of_Neutrsophic function not Alpha_I_Pixel. Am I missing something?
Also (this is not an issue but a curious doubt), I've notice you do the mean filtering in a separate fashion. I can see that the mean filter is a separable one, so the two step convolution should be the same as doing it in only one step with the square filter. I was intrigued and checked the computation time, in deed doing it separately takes almost half the time, but I checked the results and they are not equal. I simply did the difference between the results from doing it in two steps or in one with the square filter and surprisingly I found that not all the results were the same, yours seems to return higher values. Maybe you've checked this and have some thoughts to share!
Thank you in advance!
Joaquín
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.