Image matching is an important task arising in video compression, optical character recognition, medical imaging, watermarking and in many others fields. Given two digital images A and B, image matching determines a t...
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Image matching is an important task arising in video compression, optical character recognition, medical imaging, watermarking and in many others fields. Given two digital images A and B, image matching determines a transformation f for A such that it most closely resembles B. In this paper, we introduce the first general discretization technique that works for the class of projective transformations as well as plenty of its subclasses such as affine transformations and several combinations of scaling, rotation and translation. Based on this, we provide a fully generic image matching algorithm for all these classes that runs in polynomial time. (C) 2020 Elsevier Inc. All rights reserved.
Projective image matching is an important image processing task. Given digital images A and B, the challenge is to find a projective transformation f for A such that it most closely resembles B. Previous research led ...
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Projective image matching is an important image processing task. Given digital images A and B, the challenge is to find a projective transformation f for A such that it most closely resembles B. Previous research led to a polynomial time algorithm that elaborately searches the so-called dictionary D(A) of all projective transformations of A using a preprocessed data structure. This paper shows that projective image matching is even TC0-complete by applying a much simpler parallel way of browsing D(A). This reveals further insight into the problem's structural properties and relates it to fundamental computer operations like integer multiplication and division. (C) 2016 Elsevier Inc. All rights reserved.
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