I would like to know more about Gabor 1D and 2D kernels.
I am a newbie to the field of image processing.
When I search for this question in google, It throws up a lot of results, but all those links including those of wikipedia`s is not suited for beginners like me.
Kindly suggest some links to articles especially for BEGINNERS.
or even better,
It would be great if someone can give a detailed explaination to Gabor Filters for BEGINNERS.
Thanks in advance.
Related
Can anyone suggest me a good source to learn?
I am a newbie in ML
As I am a newbie, I have not done anything in this.
This might be an excellent place to start. You can create a new kernel straight from the dataset page, and the data will be ready for you when you enter the kernel. You can also look at other people's kernels who have used that dataset, and I bet you'll find plenty of helpful examples.
You'll get lots of hate for asking this kind of question, since it doesn't fit in S.O. question parameters, but I prefer to be a useful human.
Can anybody explain me how OPENCV fisherfaceclassifier works?
How it recognize the difference faces ?
It's important to understand that all OpenCV algorithms (usually) are based on a research papers or topics that can be researched and understood.
For FisherFace you can read about the background of it here to understand exactly how it works, this article discussed the background and implementation.
As for implementation of the Fisherface using (as your question is a little unclear) OpenCV provide a tutorial on how to get up and running, they also explain some background and importantly link out too all the underlying papers and research
I didn't really think this question is appropriate for SO as you really could have just googled your question and come to the same answer. I just thought it might be useful to understand how OpenCV is built up.
I am trying to Code a genetic algorithm in Matlab but really dont know how it works in images and how to proceed? Is there any basic tutorial that can help me understand how to apply GA on images (starting from 2d to multidimentional images ).
That will be a great help for me.
Thanking everyone in anticipations.
Kind Regards.
For GA you need two things: a fitness function that can evaluate any solution and tell how good it is, and a representation of your solution so that you can do crossover and mutation. Once you have these, you are good to go. I'm not an expert on image processing so I can't help you with that exactly.
Look at the book Essentials of metaheuristics which is a very good resource for start with evolutionary computation (and not only that) in general. It's free.
There is a paper on this subject which you can find at the IEEE library. I believe it solves the problem you vaguely describe.
I would like to filter an image to make it more clear/sharper. What would be the best filter method for this in OpenCV?
If you want to sharpen the image this question might help you.
And for noise reduction here I have found an interesting article.
Filtering is a very broad topic and "make it more clear" is a rather vague objective. OpenCV has a number of filters implemented for you to try, and they are documented fairly extensively. If you are wanting more detailed advice, perhaps you should show some example images and explain what specifically you want to improve about them.
I am working with OpenCV for a project used for recognition and I had a general question regarding the API and it's terms. I've looked online and couldn't find anything specific to this but I was wondering what the differences were regarding the Discrete Adaboost, Real AdaBoost, LogitBoost, and Gentle AdaBoost. If anyone could direct me to a pros v cons or a general description about these so that I may research which would be useful.
Update
I have added a link to a powerpoint file that goes over the different variations of the Boosting techniques. Hope this hopes someone else out there.
Adaboost powerpoint
Thanks in advance
There isn't really a simple "always use technique X" otherwise there wouldn't be a need for all the others . You really have to understand the details and experiment.
see The opencv discussion and A list of papers and technical summaries