Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Locally Adapting Technique for Boundary Detection using Image
  Segmentation   Rapid growth in the field of quantitative digital image analysis is paving
the way for researchers to make precise measurements about objects in an image.
To compute quantities from the image such as the density of compressed
materials or the velocity of a shockwave, we must determine object boundaries.
Images containing regions that each have a spatial trend in intensity are of
particular interest. We present a supervised image segmentation method that
incorporates spatial information to locate boundaries between regions with
overlapping intensity histograms. The segmentation of a pixel is determined by
comparing its intensity to distributions from local, nearby pixel intensities.
Because of the statistical nature of the algorithm, we use maximum likelihood
estimation theory to quantify uncertainty about each boundary. We demonstrate
the success of this algorithm on a radiograph of a multicomponent cylinder and
on an optical image of a laser-induced shockwave, and we provide final boundary
locations with associated bands of uncertainty.

20
The categories of this paper are: acc-phys
2
The categories of this paper are: math
19
The categories of this paper are: eess
4
The categories of this paper are: cond-mat
10
The categories of this paper are: q-bio
26
The categories of this paper are: bayes-an
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The categories of this paper are: nucl-ex
6
The categories of this paper are: astro-ph
30
The categories of this paper are: q-alg
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The categories of this paper are: mtrl-th
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The categories of this paper are: hep-th
5
The categories of this paper are: gr-qc
11
The categories of this paper are: hep-lat
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The categories of this paper are: adap-org
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The categories of this paper are: econ
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The categories of this paper are: nucl-th