Making algorithms at least 3D, preferably nD

[Juan Nunez-Iglesias]

On Mon, Apr 29, 2013 at 11:06 AM, Ankit Agrawal <aaaagrawal at gmail.com>wrote:

> @Josh and Juan,
> Thanks for your explanation.
>
> I may be wrong but I feel that there would be a limited number of
> algorithms that are nD aware but will scale down nicely if provided with a
> 2D image. For instance, if we have 3D data of the type (m x n x p), many
> functions and algorithms involving spatial components for eg: gradient
> based edge detectors won't be applicable since our 3rd dimension represents
> a series of images, we can't have something like a gradient in that
> dimension.
>

@Ankit, actually, edge detectors generalise quite nicely to nD, e.g.:
http://docs.scipy.org/doc/scipy/reference/generated/scipy.ndimage.filters.gaussian_gradient_magnitude.html

Many other examples of nD algorithms:
http://docs.scipy.org/doc/scipy/reference/ndimage.html

Instead, if our data is 3D volumetric image,  a great percentage of
> Computer Vision algorithms won't be of any use since they rely on making
> sense 3D world from 2D data. I would love to hear any comments on this
> point. Thanks.
>

Photographs are rarely 3D, but various kinds of microscopy produce truly 3D
images, not a sequence of unrelated images. If you give specific
algorithms, we might be better able to point out how to generalise to 3D,
but the gist is that most algorithms *do* generalise. It is the
implementations that are 2D, not the algorithms.
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