Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: AutoCount: Unsupervised Segmentation and Counting of Organs in Field
  Images   Counting plant organs such as heads or tassels from outdoor imagery is a
popular benchmark computer vision task in plant phenotyping, which has been
previously investigated in the literature using state-of-the-art supervised
deep learning techniques. However, the annotation of organs in field images is
time-consuming and prone to errors. In this paper, we propose a fully
unsupervised technique for counting dense objects such as plant organs. We use
a convolutional network-based unsupervised segmentation method followed by two
post-hoc optimization steps. The proposed technique is shown to provide
competitive counting performance on a range of organ counting tasks in sorghum
(S. bicolor) and wheat (T. aestivum) with no dataset-dependent tuning or
modifications.

31
The categories of this paper are: ao-sci
36
The categories of this paper are: cmp-lg
7
The categories of this paper are: hep-th
27
The categories of this paper are: comp-gas
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The categories of this paper are: acc-phys
16
The categories of this paper are: stat
12
The categories of this paper are: nucl-th
0
The categories of this paper are: hep-ph
35
The categories of this paper are: mtrl-th
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The categories of this paper are: dg-ga
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The categories of this paper are: adap-org
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The categories of this paper are: math-ph
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The categories of this paper are: quant-ph
23
The categories of this paper are: solv-int
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The categories of this paper are: chao-dyn
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The categories of this paper are: nucl-ex