Find ROI on an image from given reference

asked 2017-09-17 10:11:52 -0500

newt gravatar image

updated 2017-09-17 12:21:50 -0500

Hello. I have the following problem to solve. Suppose I have a reference image consisting of some geometric objects and numbers on a homogeneous background. They are sufficiently distinct from the background - all these objects are more close to white gray-color, whereas background is all close to black. I have a reference image and sample images, which can have different scale, some angle of rotation with respect to the reference and also some horizontal or vertical shift. What I need to do is to find the whole ROI, i.e. the region which is clearly distinguished from the background. Moreover, I need to identify regions corresponding to particular geometric objects (e.g. triangles) and regions that contain only numbers.

What method is better to apply here? I guess about SIFT implementation, since it is invariant under affine transformations. But my question is more about technique: how to implement this? I know that SIFT transform in OpenCV gives you coordinates of keypoints. and computes descriptors.

The reference image looks like this:

image description

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Comments

if you provide sample images the question will be more clear

sturkmen gravatar imagesturkmen ( 2017-09-17 11:05:34 -0500 )edit

I added reference image.

newt gravatar imagenewt ( 2017-09-17 12:22:15 -0500 )edit