Thanks for the feedback. The extraction time of 24s that you are getting is considerably higher than the extraction times that I got in my experiments.
Just for comparison, I have just executed with a 720x576 grayscale image, setting the nt parameter to 8 and the extraction time was 6s:
>> tic; sfta(I, 8); toc;
Elapsed time is 6.039394 seconds.
Which value for the nt parameter that you are using? For the datasets that I have employed to test SFTA, I found that a value of nt higher than 8 did not increase classification accuracy.
Here’s a link to the article that I hope can help you:
Just repeat the same procedure for each set of boundary point returned by bwboundaries.
If you have the original black and white image from which you extracted the boundary points, then you probably can use the bwperim function instead of bwboundaries.
All you have to do is to convert the boundary points to a black and white image, where the non zero pixels are the boundary and the zero pixels are background.
First, get the linear indices of the boundary pixels:
>> B = bwboundaries(I);
>> BP = B{1}
>> L = sub2ind(size(Ib), BP(:,1), BP(:,2)) % Get the linear indices of the boundary pixels.
Then use the linear indices to generate the binary image Ib:
Thanks for the feedback. The extraction time of 24s that you are getting is considerably higher than the extraction times that I got in my experiments.
Just for comparison, I have just executed with a 720x576 grayscale image, setting the nt parameter to 8 and the extraction time was 6s:
>> tic; sfta(I, 8); toc;
Elapsed time is 6.039394 seconds.
Which value for the nt parameter that you are using? For the datasets that I have employed to test SFTA, I found that a value of nt higher than 8 did not increase classification accuracy.
Here’s a link to the article that I hope can help you:
Thank you. This is a nice method to compute the hausdorff's fractal dimension from the border of an object.
I have implemented your method on a data set.
It seems your method is computationally time consuming since for a 720*576 gray scale image, 24.453 second is required for analysis.
Have you any comments about this?
Just repeat the same procedure for each set of boundary point returned by bwboundaries.
If you have the original black and white image from which you extracted the boundary points, then you probably can use the bwperim function instead of bwboundaries.
Hello Kaveh,
Thanks for the feedback. The extraction time of 24s that you are getting is considerably higher than the extraction times that I got in my experiments.
Just for comparison, I have just executed with a 720x576 grayscale image, setting the nt parameter to 8 and the extraction time was 6s:
>> tic; sfta(I, 8); toc;
Elapsed time is 6.039394 seconds.
Which value for the nt parameter that you are using? For the datasets that I have employed to test SFTA, I found that a value of nt higher than 8 did not increase classification accuracy.
Here’s a link to the article that I hope can help you:
http://www2.icmc.usp.br/~alceufc/documents/sibgrapi_afc_2012.pdf
Comment only
04 Nov 2012
SFTA Texture Extractor
Implementation of the SFTA algorithm for texture feature extraction.
Dear Alceu,
Thank you. This is a nice method to compute the hausdorff's fractal dimension from the border of an object.
I have implemented your method on a data set.
It seems your method is computationally time consuming since for a 720*576 gray scale image, 24.453 second is required for analysis.
Have you any comments about this?
Regards,
Kaveh
@Damodara
Just repeat the same procedure for each set of boundary point returned by bwboundaries.
If you have the original black and white image from which you extracted the boundary points, then you probably can use the bwperim function instead of bwboundaries.
Comment only