Hello list,
It may not be a good place to ask, yet I have a dire need in extracting
information from images.
The problem:
The human mind has the ability to recognize objects and to classify them. An
image with objects having limited contours, let's say the surface of the
moon is visualized by the sun streaking its light at right angles from our
point of view. (A half-full moon). The mountains and valleys will cast their
shadows, and the black-and-white or gray scale image of these casts is
sufficient for us to appreciate a surface with high mountains and deep
valleys.
While this is just an example, another real one is the capturing of living
cells using video microscopy. The goal is to measure volume changes. Images
are in gray scale and enhanced by differential interferential contrast, and
cells are easily recognized by the human eye, while pixel manipulation does
not cut it and leads to garbage. Flood-fill algorithms are difficult to
impose, because the circumference of the cell is not easily defined (pixel
intensities change, etc). Currently we have to go through thousands of
sequential images to measure the diameter using the mouse as a ruler (by
hand).
To address limitations and trying to write a program that can handle quickly
sequential images, human perception based on pattern recognition and
applying some kind of vector graphics seems to be mandatory. This could mean
to draw a shape (shape-vector) and locking it with the contour of the cell
for one image and then apply some magic to let the changing contour of the
cell determine the shape-vector. Use the shape-vector to extract a value.
The problem then reduces to the magic, which could involve hither and yon
(the cell will also grow in the z-direction, giving more blur in time), and
x and y. Because shades of gray also change in time, it might also be
conceivable that other parameters need be determined.
Question(s)
Anybody dealt with this problem, or have some info how to tackle...?
Alfred
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