Greetings All, 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 [EMAIL PROTECTED]? Thankyou! ----- Original Message ----- From: vikram ramaswamy <[EMAIL PROTECTED]> To: <[EMAIL PROTECTED]> Sent: Saturday, January 20, 2001 8:02 AM Subject: [ai-neuralnet-backprop] respected sir Respected Sir, I am vikram an undergraduate student from india. I am currently working on a project on shape classification using a feed forward neural network trained by 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 to 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 authors have done before (only that the objects to be classified are different): Hongbong Kim, Kwanhee Nam, 'Object Recognition of one DOF by back-propagation 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 the 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 above must be identical? If not, what is the way in which the fd's of the rotated images related? 2)Also, when we use an object of a different shape eg. rectangle, and get the fd's , I would expect that fd values be drastically different from the values obtained for the circular object. Is this assumption justified? Moreover, I took a circular object got its fd's; rotated the object obtained another set 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) < (fd for circle) - (fd for rectangle). By difference I mean foll.: we calculate 16 fd. take (1st fd value for circle - 1st fd value for rotated image of circle). This is done for all the 16 values. 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 - FWD NEURAL NETS KINDLY INFORM ME OF THE SAME. Am anxiously awaiting your reply, Yours respectfully, vikram To unsubscribe from this group, send an email to: [EMAIL PROTECTED]
