Omid, now it's clear, just a question
about your constant setting inside the code example TRGUESS and so on ..
If i have different type of face images can i use your settings or is possible to improved the recognition during the generation data ?
Thank's in advance
Great job, just one thing : I have try to use the sample code with the photo of they in your picture,but the saliency map is different respect your that i see on the, computer vision, great work, image descriptors, image processing, just one thing ihave try to use it just cat the photo of the lad, object recognition, saliency, why
First, I want to thank you for putting together this set of code, documenting it, and posting it for others to use. I have been using it, and modifying it, for almost a year now, and have found it to be overall a very decent program.
I have made many changes to the set of .m files, most importantly: calculating displacements for EACH image, calculating strains using finite element shape functions, making GUIs to run the code in a more streamlined manner. I plan on documenting the changes and the new code, and hope to post them on Mathworks when I get a final version.
More recently, I have started looking into how to speed up the code. As the number of grid points in each image increases, and the number of images to correlate increases, the automate_image function can take a lot of time to run, nearly all of the time spent running cpcorr. As cpcorr was written by folks at Mathworks, it is pretty well optimized already. However, improvements can be made by parallel computing.
I have started to revise the original code using a parallel for-loop to loop over the images in the automate_image function. There is some improvement, but not as much as I’d like. I searched for ways to parallelize the cpcorr function itself, and I came across a poster on Nvidia’s website (link below) that describes optimizing your original DIC code to run on a GPU. Though the authors reference this File Exchange in the poster, I don’t see that they posted a reply to this thread. I wanted to make all users of this DIC code aware that there is a version that, when run properly on a GPU, should run faster than the original code.
Cuda optimized DIC code: http://dside.dyndns.org/dict