I was wondering if it's possible to do a subtraction in the decomposition tree visual in PowerBI, and display the difference in the decomposition tree itself. Please use the image provided below for reference. Thank you!
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I have some query to code the algorithm in
Removing shadows from images by finlayson
I have gotten the matlab code to get illumination invariance image by jose alvarez and convert it to c++ coding
I have continued to follow the image reconstruction step outlined by finlayson algorithm but was stumped at the part of the Possion equation which is the part after removing the shadow edge from the log channel image
How should I proceed after that. The discussion of this part is vague to me. SI have read the following presentation slides. It say I must perform a inverse laplace operation on the image.
what should i do ? inverse laplace is not so common to code. Would need any advice I could get
ThANKS
I have to create a fire-detection program using OpenCV and C++ following an alghoritm from a scientific paper (see image below). At a certain point I have to calculate the "L band power" but I don't know what exacly it means. Can you help me?
Image of the Paper
I think that it refers the a wavelength of light/EM spectrum.
https://en.wikipedia.org/wiki/L_band
Edit: Actually I don't think this is correct after reading it again.
I can't find anything similar to Savitzky Golay Polynomial Fit on opencv. This is a standard smoothing operation though, so it seems like something they should have. Does anybody know of anything they have? Using C++ for what its worth.
Thanks!
-Tim
It is not clear what you need to do: fit or smooth, you mentioned both. But if you need to smooth using OpenCV you can try Kalman filter (fit in its way and smooth), smooth-2D (using your 1D-data) or your own convolution smooth kernel 1D+1D using 1D-data for kernelX only convolution (the fastest way to smooth).
OpenCV is near to real time image and video processing library, and it contains most common task solvers for this, where is no polynomial fitting among them yet. But if you really need fitting (not just smoothing) you can use polynomial fitting matrix equation and calculate your answer by yourself in a simple way thanks to Mat objects in OpenCV which has inv()(inverse) and t()(transpose) functions.
I am trying to use two Gaussian mixtures with EM algorithm to estimate color distribution of a video frame. For that, I want to use two separate peaks in the color distribution as the two Gaussian means to facilitate the EM calculation. I have several difficulties with the implementation of these in OpenCV.
My first question is: how can I determine the two peaks? I've searched about peak estimation in OpenCV, but still couldn't find any seperate function. So I am going to determine two regions, then find their maximum values as peaks. Is this way correct?
My second question is: how to perform Gaussian mixture model with EM in OpenCV? As far as I know, the "cv::EM::predict" function could give me the index of the most probable mixture component. But I have difficulties with training EM. I've searched and found some other codes, but finding the correct parameters is too much difficult for. Could someone provide me any example code for this? Thank you in advance.
#ederman, try {OpenCV library location}\opencv\samples\cpp\em.cpp instead of the web link. I think the sample code in the link is out of date now. I have successfully compiled the sample code in OpenCV 2.3.1. It shouldn't be a problem for 2.4.2.
Good luck:)
My first question is: how can I determine the two peaks?
I would iterate through the range of sample values possible, and test when the does EM.predict(sample)[0] peaks.
I am trying to find rank of a matrix. In matlab this is fairly straight-forward but I am using visual studio 2008 (c++). I recently installed openCV and it works for most of my matrix arithmetic so far except I can't figure out how to use openCV to get rank of a matrix. In my research online I found that apparently cvSVD can give me rank
http://www.emgu.com/wiki/files/1.3.0.0/html/55d6f4d2-223d-8c55-2770-2b6a9c6eefa2.htm
But I have no idea how cvSVD will return this particular property. Any ideas on getting matrix rank from openCV???
Thanks.
Read the following
http://en.wikipedia.org/wiki/Singular_value_decomposition#Applications_of_the_SVD
in the section Range, null space and rank it explains how to get the Rank from the singular values. Quoting this page:
As a consequence, the rank of M equals the number of non-zero singular
value
So basically you can count the number of non-zero singular values and that is the rank. According to the link you provide in the question, your SVD calculation function in opencv should return you a matrix or vector of singular values - if it is a matrix, the singular values lie on the main diagonal of this matrix. From here you should be ok. There may be a simpler way, but I am not familiar with opencv.