Adam, both the anisotorpic and isotorpic photometric normalization techniques in the toolbox are implemented via the inverse of a large sparse matrix, but they could of course also be implemented using multigrid methods. Regarding your results with the retinex techniques, I am not quite sure what you mean poor results. Are the recognition results poor when you use the images for face recognition or do you get visualy poor results. In any case the retinex technique does not depend on face registration at all.
The function contains a few errors. First of all, you do not return the first Walsh function, which is constant and equal to 1. This means that all Walsh functions returned by your function (if it was correctly implemented) would be of one order higher than specified by the parameter n. Next, your Matlab function returns the wrong order of the Walsh functions - have a look at any of the on-line sources. And it seems to me that all returned functions are multiplied by -1.
To anyone using the toolbox: the title of the publication associated with the INFace toolbox has changed; the correct title is: "Photometric normalization techniques for Illumination invariance"
That error jus means what it says. You run out of memory because your input image is too large. If you type help with the two functions, you will see that the two methods are not implemented using multigrid methods, but with a direct inversion of the (sparse) differential operator. Hence, you can only normalize images for which you are able to invert the operator in sparse form. Resize the image to a smaller size and try again.
@Alireza: All function of the toolbox return 8-bit images. This means that the result will always be in the range of [0,255]. If you need the output in [0,1] just divide the result of the normalization with 255. Thanks for the comment.
Adam, both the anisotorpic and isotorpic photometric normalization techniques in the toolbox are implemented via the inverse of a large sparse matrix, but they could of course also be implemented using multigrid methods. Regarding your results with the retinex techniques, I am not quite sure what you mean poor results. Are the recognition results poor when you use the images for face recognition or do you get visualy poor results. In any case the retinex technique does not depend on face registration at all.
It may be easier to iteratively do anisotropic and isotropic filtering for some people. Also, with the retinex imaging I tend to get poor results, with weird shadows in places they shouldnt be. I am not sure if its because you need good face registration or not so Ill have to read the paper.
To anyone using the toolbox: the title of the publication associated with the INFace toolbox has changed; the correct title is: "Photometric normalization techniques for Illumination invariance"
That error jus means what it says. You run out of memory because your input image is too large. If you type help with the two functions, you will see that the two methods are not implemented using multigrid methods, but with a direct inversion of the (sparse) differential operator. Hence, you can only normalize images for which you are able to invert the operator in sparse form. Resize the image to a smaller size and try again.
Adam, both the anisotorpic and isotorpic photometric normalization techniques in the toolbox are implemented via the inverse of a large sparse matrix, but they could of course also be implemented using multigrid methods. Regarding your results with the retinex techniques, I am not quite sure what you mean poor results. Are the recognition results poor when you use the images for face recognition or do you get visualy poor results. In any case the retinex technique does not depend on face registration at all.
It may be easier to iteratively do anisotropic and isotropic filtering for some people. Also, with the retinex imaging I tend to get poor results, with weird shadows in places they shouldnt be. I am not sure if its because you need good face registration or not so Ill have to read the paper.
To anyone using the toolbox: the title of the publication associated with the INFace toolbox has changed; the correct title is: "Photometric normalization techniques for Illumination invariance"
That error jus means what it says. You run out of memory because your input image is too large. If you type help with the two functions, you will see that the two methods are not implemented using multigrid methods, but with a direct inversion of the (sparse) differential operator. Hence, you can only normalize images for which you are able to invert the operator in sparse form. Resize the image to a smaller size and try again.
Comment only