Linear algebra jacobi Singular Value Decomposition (SVD) algorithm
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Hello out there, I have been working on a project to design a new computer vision system and I have succeeded, the only stage remaining is the homography estimation stage.But to estimate the homography matrix the algorithm has to compute the singular value decomposition or svd of the measurement matrix. I can easily use openCV or LAPACK for that but I want to implement my own Jacobi SVD method and the openCV and Lapack are too big, I don't want my computer vision system library to be dependent on them. Does anyone have a copyright free example code of a fast jacobi svd implementation that I can modify without using the big free libraries? I want a simplified implementation, even pseudocode would suffice. I will create a tech demo video for this computer vision system. It out performs state of the art systems in object detection/recognition. I'am surely going to acknowledge any help I can get. Thanks in advance. :-)
“Everything is simple when you take your time to analyze it.”
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Hello out there, I have been working on a project to design a new computer vision system and I have succeeded, the only stage remaining is the homography estimation stage.But to estimate the homography matrix the algorithm has to compute the singular value decomposition or svd of the measurement matrix. I can easily use openCV or LAPACK for that but I want to implement my own Jacobi SVD method and the openCV and Lapack are too big, I don't want my computer vision system library to be dependent on them. Does anyone have a copyright free example code of a fast jacobi svd implementation that I can modify without using the big free libraries? I want a simplified implementation, even pseudocode would suffice. I will create a tech demo video for this computer vision system. It out performs state of the art systems in object detection/recognition. I'am surely going to acknowledge any help I can get. Thanks in advance. :-)
“Everything is simple when you take your time to analyze it.”
Okay I found the solution personally http://www.math.pitt.edu/~sussmanm/2071Spring08/lab09/lab09.pdf[^], but if you have a better solution I would like to have a look at it. :-)
“Everything is simple when you take your time to analyze it.”