Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Information Theoretic Registration   This paper establishes an information theoretic framework for deep metric
based image registration techniques. We show an exact equivalence between
maximum profile likelihood and minimization of joint entropy, an important
early information theoretic registration method. We further derive deep
classifier-based metrics that can be used with iterated maximum likelihood to
achieve Deep Information Theoretic Registration on patches rather than pixels.
This alleviates a major shortcoming of previous information theoretic
registration approaches, namely the implicit pixel-wise independence
assumptions. Our proposed approach does not require well-registered training
data; this brings previous fully supervised deep metric registration approaches
to the realm of weak supervision. We evaluate our approach on several image
registration tasks and show significantly better performance compared to mutual
information, specifically when images have substantially different contrasts.
This work enables general-purpose registration in applications where current
methods are not successful.

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The categories of this paper are: patt-sol
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The categories of this paper are: hep-lat
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The categories of this paper are: astro-ph
3
The categories of this paper are: physics
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The categories of this paper are: bayes-an
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The categories of this paper are: adap-org
4
The categories of this paper are: cond-mat
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The categories of this paper are: q-fin
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The categories of this paper are: econ
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The categories of this paper are: alg-geom
5
The categories of this paper are: gr-qc
30
The categories of this paper are: q-alg
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The categories of this paper are: nucl-ex
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The categories of this paper are: stat
31
The categories of this paper are: ao-sci
29
The categories of this paper are: funct-an