Recently this email, (Below) came to a list I host. I really don't know much
about fourier descriptors, and I thought that possibly someone in Perl-AI
might know more about this gentlemans subject than I. If anyone can help
with the below message, I'm sure it would be greatly appreciated.
~ Josiah Bryan
P.S. If anyone replies, could you CC it to Perl-AI and/or
----- Original Message -----
From: vikram ramaswamy <email@example.com>
Sent: Saturday, January 20, 2001 8:02 AM
Subject: [ai-neuralnet-backprop] respected sir
I am vikram an undergraduate student from india. I am currently working
project on shape classification using a feed forward neural network
standard backprop algorithm.
I initially detect the edges of the shape to be classified and find the
fourier descriptors(fds) of the edge image. I use these fds as the input
the neural network.
This approach has been followed before and a paper in this topic has been
published. We are infact trying to implement what the below listed
have done before (only that the objects to be classified are different):
Hongbong Kim, Kwanhee Nam, 'Object Recognition of one DOF by
neural net', IEEE transactions on Neural Networks Vol.6 No.2, March 1995.
I have a few doubts about the fourier descriptors. Can you please kindly
excuse the trouble and answer my questions?
In my study , I have used only 16 fds as mentioned in the above paper.
1) I take an edge image of an (eg. circular) object and find its fourier
descriptors. I now rotate the original image , detect the edges and get
fourier descriptors(fd) of this image. It is mentioned that fd's are
insensitive to rotation. Does this mean the 2 sets of fd's mentioned
must be identical? If not, what is the way in which the fd's of the
2)Also, when we use an object of a different shape eg. rectangle, and get
fd's , I would expect that fd values be drastically different from the
obtained for the circular object. Is this assumption justified? Moreover,
took a circular object got its fd's; rotated the object obtained another
of fd's. I computed the difference between the two sets of 16 fd . I then
found the difference between the fd of a circular object and one for a
rectangular object. I am troubled by the fact that the 2 differences are
comparable. I thought (fd for circle) - (fd for rotated img. of circle) <
for circle) - (fd for rectangle).
By difference I mean foll.: we calculate 16 fd. take (1st fd value for
- 1st fd value for rotated image of circle). This is done for all the 16
Max difference between circles must (i guess)be < Max difference between
circle and rectangle.
Since this is not so, can you kindly clarify this situation?
IF ANY OTHER APPROACH IS POSSIBLE FOR SHAPE CLASSIFICATION USING FEED -
NEURAL NETS KINDLY INFORM ME OF THE SAME.
Am anxiously awaiting your reply,
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