Making algorithms at least 3D, preferably nD

[Ankit Agrawal]

@Josh and Juan,
Thanks for your explanation.

On Sat, Apr 27, 2013 at 11:58 AM, Ankit Agrawal <aaaagrawal at gmail.com>wrote:
>
>> Hi all,
>>
>>      I guess I need some clarification what nD images exactly mean. For
>> 3D, these are the following ways I can think of it --
>> 1. RGB image (n x m x 3) : Total m*n pixels
>> 2. Series of grey-scale images (m x n x p): Total p images each with m*n
>> pixels
>> 3. A grey-scale(m x n x 2) or an RGB image(m x n x 4) with depth value at
>> each pixel like a Pointcloud(http://pointclouds.org/)?
>>
>
> As others have pointed out, I think (1) and (3) are 2D+c, and (2) is 3D.
> You can also have (m x n x p x 3), or even (t x m x n x p x c) for
> arbitrary t and c.
>
> In general, when I said 3D I meant (m x n x p) and (m x n x p x 3), and
> when I said nD I meant (t x m x n x p x c). But, importantly, what I'm
> looking for is functions that are nD aware but will degrade nicely if
> provided with e.g. a 2D image.
>

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. 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.

Regards,
Ankit  Agrawal,
Communication and Signal Processing,
IIT Bombay.
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mail.python.org/pipermail/scikit-image/attachments/20130429/3ee83ad0/attachment.html>