OpenCV
Breaking out Features
Local Binary Patterns (LBP):
OCR Optical Character Recognition
Image correlation: Optical Mice
Browser Based Image Processing:
Subtraction, Area Sum
Methods for detecting Optical Flow
Synthetic Data: To collect large sample sets for character recognition,
you can synthesize "fake" data (Artificial Data) by randomly pasting letters
from a randomly selected font onto a randomly selected background with (perhaps)
random distortions and rotations. The major advantage of doing this is that
you don't have to "label" the data (know what character(s) are represented)
because you made it from the label in the first place. It can also help in
trouble shooting complex systems, by
replacing a stage in a complex system with a "perfect" stage to see how much
that effects the end product. E.g. if you are wondering how much background
removal helps your OCR application, generate input images with no background.
Also:
See also:
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http://hackaday.com/2016/04/02/insanely-quick-3d-tracking-with-1-camera/
Very fast, Single camera, 3D tracking and scanning. Open source code supports
ROS.
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http://www.robots.ox.ac.uk/~misard/condensation.html
The Condensation algorithm (Conditional Density Propagation) algorithm tracks
moving objects through substatial clutter.
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http://neil.fraser.name/software/recog/
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http://www.bitquill.net/trac/wiki/Android/OCR
(see the blog post) Given a greyscale image of dimisions m x n, the lighting
will often change from one part of the image to another. To pick out details
consistantly, you need to set the threshold individually for each pixel (i,j)
as the mean intensity of pixels in a w x w neighborhood around the pixel.
On each row, you might do
for i = 0 to N-1: s = 0; for k = max(i-r,0) to min(i+r,N-1): s +=
a[k];
where w=2r+1. (r is half the width of the window). But it turns out that
this is faster:
Initialize s = sum(a[0]..a[r]); for i = 1 to N-1: if i > r: s -= a[i-r-1];
if i < N-r: s += a[i+r]
at the end of each, s is the threshold for pixel a[i].
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http://www.cs.colostate.edu/~draper/papers/draper_cviu05.pdf
B. Draper and A. Lionelle. “Evaluation of Selective Attention under
Similarity Transformations,” Vision and Image Understanding, 100:152-171,
2005
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http://www.cs.colostate.edu/~draper/research/software.php
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