Edge Detection
From 2006.igem.org
(→a)The AND approach) |
(→a)The AND approach) |
||
| Line 6: | Line 6: | ||
=The living-organism approach= | =The living-organism approach= | ||
| - | ===a)The AND approach=== | + | ===a)The "AND" approach=== |
One could imagine a population to react to light so that some product A is produced while another possible product B is suppressed, and vice versa in darkness. Also, one could imagine that those products, A or B, do not diffuse very far (or are quickly degenerating). | One could imagine a population to react to light so that some product A is produced while another possible product B is suppressed, and vice versa in darkness. Also, one could imagine that those products, A or B, do not diffuse very far (or are quickly degenerating). | ||
Thus, when a pattern is projected on a population there will be sharp gradients between the lighted area and the one in darkness. | Thus, when a pattern is projected on a population there will be sharp gradients between the lighted area and the one in darkness. | ||
Revision as of 14:45, 29 July 2005
Contents |
Intro
Independently of the group in Texas, we also came to this idea in a discussion during lunch: the classical Edge Detection problem. Rather simple in computer science, but hopefully new to the biology community (well, it seems it isn't).
Principle
In computer vision you typically want to make out the contours of an object or region. At the contour or edge something changes significantly, i.e. there is a strong gradient of color or lighting (e.g. the red car standing in front of the blue garage will have both). Edge Detection algorithms make it possible to find those changes.
The living-organism approach
a)The "AND" approach
One could imagine a population to react to light so that some product A is produced while another possible product B is suppressed, and vice versa in darkness. Also, one could imagine that those products, A or B, do not diffuse very far (or are quickly degenerating). Thus, when a pattern is projected on a population there will be sharp gradients between the lighted area and the one in darkness. Now let's assume that the presence of both products, A and B, are needed, to trigger a third product C, e.g. GFP. Then only the edge will show a change as a fluorescent thin line and biological Edge Detection has been achieved.
