How can I define (statistically) quality of image matching (area-based, feature-based ) between two images.
'If its wrong forum to ask such conceptual questions , please guide me to other forum'.
Thanx and regards,
You should probably look at the cross-correlation between the images.
Related
So this is my idea : I have a photo of a supermarket shelf (Shelf example)
and I would like to have it "categorized" by objects or "same-object-sets", something like this (but on the whole picture)
Do you have any ideas of how this could be done, using a Visual Recognition API or something like OpenCV maybe ?
Thanks ;)
You can give a look to template matching with opencv as shown here or here for a multiscale approach.
For some theory about how it's done you can look here.
I have an image of a layer of carbon nanotubes taken with an electronic microscope and I would like to extract the "shape" of every nanotube.
I think that ImageJ could be very helpful since it has already led me to encouraging results, but I am sure that they could be improved.
Here is the image I start with :
I have seen that there are many tools to detect for example cells, or to divide an image in two areas (with the trainable Weka segmentation tool for instance), but I did not find anything to "follow" a tube from the beginning to the end. Does such a tool exist ?
What would you advise me to do to clean the image ? So far, I have tried 'auto local threshold' (loss of information), 'Remove outliers', 'analyse particles' (to remove the too-little-to-be-tubes-particles), and some skeleton tools + OrientationJ to extract information.
Thank you very much for your help !
EDIT : the "Tubeness" plugin was very helpful Tubeness documentation
I am working on post processing of disparity map.
My disparity image, even though it is WLS filtered, has too many 'holes'.
This is what i get for now. Rectified, but in fish eye way. Anyway rectified for sure, but have many holes. Disparity matching algorithm is SGBM. WLS filter sigma is 2.1, lambda is 30000. Black regions are holes.
I am referring official opencv site which says Disparity map post-filtering and it is using DisparityWLSFilter extensively. But I wonder how it works internally and want to read theoretical paper regarding this implementation. I want to know what Sigma and Lambda does, and how it will filter my image.
And, is there any other good disparity filter that i can use? WLS filter cannot fill the 'holes' effectively. Or, any algorithm that is easy to use or easy to implement, or library that is not GPL?
Self reply.
Got answer from Opencv.
Orig question is HERE.
Reply says
References have been added here, documentation reference
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Check out the comments here, and the code here. That should answer some of your questions. To see how the code author has come up with this method perhaps should contact him directly as there is no reference for that in the code comments.
I am trying to determine when a food packaging have error or not error. Example
the logo " McDonald's " have error misprints or not, as the wrong label, wrong color..( i can not post picture )
What should I do, please help me!!
It's not a trivial task by any stretch of the imagination. Two images of the same identical object will always be different according to lightning conditions, perspective, shooting angle, etc.
Basically you need to:
1. Process the 2 images into "digested" data - dominant color, shapes, etcw
2. Design and run your own similarity algorithm between the 2 objects
You may want to look at Feature detectors in OpenCV: Surf, SIFT, etc.
Along a result I just found your question, so I think I come too late.
If not I think your problem car easily be resolved, it exists since years and is called Sikuli .
While it's for testing purposes, I have been using it in the same way as you need : compare a reference and a production image. Based on OpenCV it does it very well.
I was working with PIL, OpenCV and OCR readers to read texts from Images. The biggest problem I faced is when it comes to Image processing to make texts sharp enough for easier/accurate extraction by the OCR reader.
For that, I thought of increasing the contrast/brightness and do a histogram equlization using PIL but that didn't help the cause either.
So, what would you suggest to do to make the texts appear sharper for better text extraction?
PIL has sharpen and edge enhancing filters. Is this what you want? An example image showing what you are dealing with would be helpful.
Your image has an uneven background color which may be causing problems. Try looking at this solution to create a nice leveled b&w image.
But the black collar is also going to cause problems and you should look at ways of cropping it out.
That said, I get reasonable improvements with a simple PIL SHARPEN filter:
tesseract results after SHARPEN filter:
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