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.
