Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Comparison of Stereo-Matching Cost between Convolutional Neural
  Network and Census for Satellite Images   Stereo dense image matching can be categorized to low-level feature based
matching and deep feature based matching according to their matching cost
metrics. Census has been proofed to be one of the most efficient low-level
feature based matching methods, while fast Convolutional Neural Network
(fst-CNN), as a deep feature based method, has small computing time and is
robust for satellite images. Thus, a comparison between fst-CNN and census is
critical for further studies in stereo dense image matching. This paper used
cost function of fst-CNN and census to do stereo matching, then utilized
semi-global matching method to obtain optimized disparity images. Those images
are used to produce digital surface model to compare with ground truth points.
It addresses that fstCNN performs better than census in the aspect of absolute
matching accuracy, histogram of error distribution and matching completeness,
but these two algorithms still performs in the same order of magnitude.

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The categories of this paper are: alg-geom
5
The categories of this paper are: gr-qc
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The categories of this paper are: q-fin
12
The categories of this paper are: nucl-th
0
The categories of this paper are: hep-ph
14
The categories of this paper are: math-ph
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The categories of this paper are: eess
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The categories of this paper are: plasm-ph
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The categories of this paper are: adap-org
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The categories of this paper are: comp-gas
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The categories of this paper are: mtrl-th
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The categories of this paper are: chao-dyn
8
The categories of this paper are: hep-ex
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The categories of this paper are: dg-ga
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The categories of this paper are: patt-sol
2
The categories of this paper are: math