A robust gross-to-fine pattern recognition system
Al-Mouhamed, M.
Industrial Electronics, IEEE Transactions on
Volume 48, Issue 6, Dec 2001 Page(s):1226 - 1237
Digital Object Identifier 10.1109/41.969403
Summary:This paper presents a model-based vision recognition engine for
planar contours that are scale invariant of known models. Features are
obtained by using a constant-curvature criterion and used to carry out
efficient coarse-to-fine recognition. A robust shape matching is
proposed for comparing contour fragments from scenes with partial
occluding. In order to carry out an early pruning of a large portion of
the models, hypotheses are only generated for a subset of contours with
enough discriminative information. Poor scene contours are used later in
validating or invalidating a relatively small set of hypotheses. Since
hypotheses are selectively verified, blocking is avoided by extending
current matching through pairing of hypotheses, predictive matching, and
retrieving the next weighted hypotheses. This avoids the processing of a
large number of initial hypotheses. The authors' evaluation shows that a
high recognition error results from the use of too small a bucket size
because the indexes may fall at random, producing nonrepeatable results.
They use a multidimensional hashing scheme with space separation between
dense parameter areas to create additional hashing tables. The
robustness of the recognition is based on engineering a coarse bucket
size to the best tolerance with respect to various sources of noise.
Partially occluded scenes having three objects can be recognized with a
success rate of 84%. The results are reproducible against changes in
scale, rotation, and translation. Due to the selection of robust initial
hypotheses and the structure of the selective matching system, the
processing time essentially depends on scene complexity with a marginal
dependence on database size
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