Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: 1000 Pupil Segmentations in a Second using Haar Like Features and
  Statistical Learning   In this paper we present a new approach for pupil segmentation. It can be
computed and trained very efficiently, making it ideal for online use for high
speed eye trackers as well as for energy saving pupil detection in mobile eye
tracking. The approach is inspired by the BORE and CBF algorithms and
generalizes the binary comparison by Haar features. Since these features are
intrinsically very susceptible to noise and fluctuating light conditions, we
combine them with conditional pupil shape probabilities. In addition, we also
rank each feature according to its importance in determining the pupil shape.
Another advantage of our method is the use of statistical learning, which is
very efficient and can even be used online.
https://atreus.informatik.uni-tuebingen.de/seafile/d/8e2ab8c3fdd444e1a135/?p=%2FStatsPupil&mode=list

20
The categories of this paper are: acc-phys
2
The categories of this paper are: math
7
The categories of this paper are: hep-th
25
The categories of this paper are: patt-sol
31
The categories of this paper are: ao-sci
34
The categories of this paper are: chem-ph
5
The categories of this paper are: gr-qc
9
The categories of this paper are: nlin
10
The categories of this paper are: q-bio
27
The categories of this paper are: comp-gas
33
The categories of this paper are: plasm-ph
32
The categories of this paper are: atom-ph
16
The categories of this paper are: stat
22
The categories of this paper are: chao-dyn
3
The categories of this paper are: physics
0
The categories of this paper are: hep-ph