Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Real-time 2D/3D Registration via CNN Regression   In this paper, we present a Convolutional Neural Network (CNN) regression
approach for real-time 2-D/3-D registration. Different from optimization-based
methods, which iteratively optimize the transformation parameters over a
scalar-valued metric function representing the quality of the registration, the
proposed method exploits the information embedded in the appearances of the
Digitally Reconstructed Radiograph and X-ray images, and employs CNN regressors
to directly estimate the transformation parameters. The CNN regressors are
trained for local zones and applied in a hierarchical manner to break down the
complex regression task into simpler sub-tasks that can be learned separately.
Our experiment results demonstrate the advantage of the proposed method in
computational efficiency with negligible degradation of registration accuracy
compared to intensity-based methods.

29
The categories of this paper are: funct-an
16
The categories of this paper are: stat
5
The categories of this paper are: gr-qc
17
The categories of this paper are: q-fin
22
The categories of this paper are: chao-dyn
7
The categories of this paper are: hep-th
27
The categories of this paper are: comp-gas
31
The categories of this paper are: ao-sci
32
The categories of this paper are: atom-ph
34
The categories of this paper are: chem-ph
12
The categories of this paper are: nucl-th
2
The categories of this paper are: math
4
The categories of this paper are: cond-mat
0
The categories of this paper are: hep-ph
37
The categories of this paper are: supr-con
14
The categories of this paper are: math-ph