I'm looking for interpolation code for time series in Google Earth Engine, to convert NDVI Sentinel 2 data to daily data. Thank you for any help.
It is probably not as straightforward as a simple interpolation, but you can achieve this with the CCDC temporal segmentation algorithm. It models the seasonal behavior based on harmonic models. You can find the docs here:
https://gee-ccdc-tools.readthedocs.io/en/latest/background.html
If you follow the tutorials you should get a good overview of how it works though.
Good luck!
Related
I'm learning orb-slam and opencv source code, and inside the orb.cpp which lies on modules/features2d/src/ directory I see a bit pattern named as
bit_pattern_31_[256*4]
But I really don't know what's its usage. I search the google and bing long time without any answer given.
So any one know the usage or reference of this majic bit pattern?
Since I came across this on google and eventually found what I think is the answer, I'll give it a shot:
bit_pattern_31_[]
is a pre-computed set of points P1(x,y) and P2(x,y).
I believe it to be the set of points obtained by the greedy search described in section 4.3 Learning Good Binary Features of the original orb paper (ORB: an efficient alternative to SIFT or SURF)
I am a newbie to skilearn.
What I want to do is quite simple - just feed my model with a bunch of similar texts.
Then, I want to be able to give it a new text, and see if it is similar to the existing texts in the dataset.
How should this be done?
Thanks very much in advance.
One good aproach might be using cosine similarity. This is a very good tutorial for starting:
Machine Learning :: Cosine Similarity for Vector Space Models (Part III)
Another good approach would be a Bayesian Classifier, like the ones used for SPAM detection. Take a look at this link to learn more about them.
I am trying to Code a genetic algorithm in Matlab but really dont know how it works in images and how to proceed? Is there any basic tutorial that can help me understand how to apply GA on images (starting from 2d to multidimentional images ).
That will be a great help for me.
Thanking everyone in anticipations.
Kind Regards.
For GA you need two things: a fitness function that can evaluate any solution and tell how good it is, and a representation of your solution so that you can do crossover and mutation. Once you have these, you are good to go. I'm not an expert on image processing so I can't help you with that exactly.
Look at the book Essentials of metaheuristics which is a very good resource for start with evolutionary computation (and not only that) in general. It's free.
There is a paper on this subject which you can find at the IEEE library. I believe it solves the problem you vaguely describe.
The stereo matching problem consists of obtaining a correspondence between right and left images. I want to do an evaluation between a baseline and a Dynamic Programming method. However, I don't have a baseline yet. I would like to know which method should I use. I was thinking to try a brute-force algorithm. Is there something like that in the literature?
What do you suggest as a baseline method? I want a simple solution, something without heuristics and optimizations, such as this brute-force strategy . But, I have no material to research about it, only methods using Graph Cut, Dynamic Programming etc.
Thanks in advance!
The Middlebury Univ. reference datasets and database of algorithms are the standard everyone uses for evaluation these days.
http://vision.middlebury.edu/stereo/
You should have a look at the basics before delving into graph cuts. Consult the relevant chapter here, it might help http://szeliski.org/Book/
I'm trying to cluster a really large dataset - 3030764x162 into 4000 clusters using the cvKmeans2 function in OpenCV 2.1.
I would like to see which iteration the K-means algorithm is currently in (similar to what is displayed in Matlab), but I don't see any documentation that points to how I can do this.
It's kind of frustrating seeing a blank screen and not knowing when the code is going to terminate!
Thank you.
Unfortunate as it seems, the answer is No, you cannot. There are no debugging/informative statements anywhere in the kmeans function as provided by OpenCV. However, you may edit and add statements to the method as you deem appropriate.
#Sau,
May be you need some other way of doing it. Though my answer is not relevant to OpenCV.
I have not tried in OpenCV, I had once done KMeans clustering for a extremely large data set and it was more a option better than OpenCV as it worked in a distributed mode. Though very lengthy, but still you might be interested. Its Kmeans clustering using Mahout
Check it out