Matching Performance Improvement of Maximally Stable Extremal Region with Local Affine Invariants
Date Issued
2011
Date
2011
Author(s)
Liu, Yu-Ning
Abstract
At first, this paper introduced a powerful region feature – Maximally Stable Extremal Region (MSER), which has a better performance comparing to other region features. Then we use two different descriptors to describe the region, such as ellipse expression and local affine frame construction. Ellipses for each region are computed from covariant matrix and can be normalized to a circle. Local affine frame (LAF) is another description to feature region. In this paper we use several distinguished points, such as geometric center of the region, bi-tangent points of the region, the deepest points of the concavity and the farthest from the bi-tangent line. We also explain algorithms to MSER、LAF, including union-find and bin-sort for MSER detection and contour tracing, tangent from point to any polygon and steps to find bi-tangent points to construct LAF. Finally we compare the MSER to the other region detector and use MSER with LAF to match images from different deformation with image.
Subjects
Maximally Stable Extremal Region
Local Affine Frames (LAF)
Covariance Matrix
Normalization
Bi-tangent line
Type
thesis
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