This final thesis was presented with honors in the academic year of 2005/2006 as a final thesis.
It is based on different theorical publications on texture identification and comparison. The algorithms of these publications were implemented with special emphasis on the teaching and learning side, so that the user could set the parameters of the algorithm and investigate the different results obtained.
These publications are below:
- Clustering of singular value decomposition of image data with
applications to texture classification.
- Texture Classification Using Logical Operators
Note: Although the videos are thoroughly discussed, it requires the reading of the publications and a certain level of mathematics to understand them in depth.
This video shows the results of the image segmentation algorithms. The algorithms of the publications were improved by adding:
- The ability to apply colour inversion and black and white filters to images, as well as being able to rotate them.
- The segmentation of the images has not been generated taking only into account the middle row. This row has just been highlighted to generate the graph.
- Mathematical corrections were applied, resulting from the application of the algorithm at a practical level.
- Important results were obtained regarding the segmentation of images by these methods due to the accuracy of the different parameters, which was absent on the publications
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hi j lucas barcia
ReplyDeletei am newbie to image processing. i m amazed with your videos at youtube on texture segmentation.
i want to learn how to do texture segmentation, could you please give me advice on how to proceed... with links references anything...
thanks in advance
hi maxzoel
ReplyDeletei am glad you like the videos :)
i am not an expert in texture segmentation but i can give you some advices about how to proceed:
the first thing i would do is define the scope. It is not the same segment all images (generic robot vision, ie) than, for example, car's plates taken with a camera.
it is much better to implement different techniques than work fully with an specific one. There are different techniques in different areas: mathematics, logic filtering, artifitial intelligence (neuro fuzzy systems), etc. but none of them are perfect. Most of times it is much better to mix and work with them.
This is one of the techniques:
http://www.emo.org.tr/ekler/ae4f12b897c4bb5_ek.pdf
You can find lots of more techniques like this one searching by medical segmentation, robot vision segmentation, artifitial intelligence image segmentation, etc. :)