Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Geometric primitive feature extraction - concepts, algorithms, and
  applications   This thesis presents important insights and concepts related to the topic of
the extraction of geometric primitives from the edge contours of digital
images. Three specific problems related to this topic have been studied, viz.,
polygonal approximation of digital curves, tangent estimation of digital
curves, and ellipse fitting anddetection from digital curves. For the problem
of polygonal approximation, two fundamental problems have been addressed.
First, the nature of the performance evaluation metrics in relation to the
local and global fitting characteristics has been studied. Second, an explicit
error bound of the error introduced by digitizing a continuous line segment has
been derived and used to propose a generic non-heuristic parameter independent
framework which can be used in several dominant point detection methods. For
the problem of tangent estimation for digital curves, a simple method of
tangent estimation has been proposed. It is shown that the method has a
definite upper bound of the error for conic digital curves. It has been shown
that the method performs better than almost all (seventy two) existing tangent
estimation methods for conic as well as several non-conic digital curves. For
the problem of fitting ellipses on digital curves, a geometric distance
minimization model has been considered. An unconstrained, linear,
non-iterative, and numerically stable ellipse fitting method has been proposed
and it has been shown that the proposed method has better selectivity for
elliptic digital curves (high true positive and low false positive) as compared
to several other ellipse fitting methods. For the problem of detecting ellipses
in a set of digital curves, several innovative and fast pre-processing,
grouping, and hypotheses evaluation concepts applicable for digital curves have
been proposed and combined to form an ellipse detection method.

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The categories of this paper are: hep-ph
20
The categories of this paper are: acc-phys
36
The categories of this paper are: cmp-lg
34
The categories of this paper are: chem-ph
24
The categories of this paper are: dg-ga
19
The categories of this paper are: eess
17
The categories of this paper are: q-fin
15
The categories of this paper are: nucl-ex
25
The categories of this paper are: patt-sol
4
The categories of this paper are: cond-mat
23
The categories of this paper are: solv-int
26
The categories of this paper are: bayes-an
10
The categories of this paper are: q-bio
27
The categories of this paper are: comp-gas
31
The categories of this paper are: ao-sci
7
The categories of this paper are: hep-th