Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Extremal Region Analysis based Deep Learning Framework for Detecting
  Defects   A maximally stable extreme region (MSER) analysis based convolutional neural
network (CNN) for unified defect detection framework is proposed in this paper.
Our proposed framework utilizes the generality and stability of MSER to
generate the desired defect candidates. Then a specific trained binary CNN
classifier is adopted over the defect candidates to produce the final defect
set. Defect datasets over different categories \blue{are used} in the
experiments. More generally, the parameter settings in MSER can be adjusted to
satisfy different requirements in various industries (high precision, high
recall, etc). Extensive experimental results have shown the efficacy of the
proposed framework.

24
The categories of this paper are: dg-ga
31
The categories of this paper are: ao-sci
12
The categories of this paper are: nucl-th
23
The categories of this paper are: solv-int
26
The categories of this paper are: bayes-an
27
The categories of this paper are: comp-gas
4
The categories of this paper are: cond-mat
18
The categories of this paper are: econ
15
The categories of this paper are: nucl-ex
33
The categories of this paper are: plasm-ph
30
The categories of this paper are: q-alg
5
The categories of this paper are: gr-qc
7
The categories of this paper are: hep-th
17
The categories of this paper are: q-fin
6
The categories of this paper are: astro-ph
21
The categories of this paper are: adap-org