To overcome this drawback, this paper proposes a region-constrained feature matching in which an image is segmented into small regions and feature correspondences are clustered inside each region. The drawback of HAC is its large computational complexity which increases rapidly as the number of feature correspondences increases. Hierarchical agglomerative clustering (HAC) has been effectively used to distinguish inliers from outliers. Local feature matching is one of the most fundamental issues in computer vision. Jung Whan Jang, Mostafiz Mehebuba Hossain and Hyuk-Jae Lee Region-constrained Feature Matching with Hierachical Agglomerative Clustering
We show the usefulness of our approach with several Region-based energies such as the one proposed by Chan and Vese can be easily implemented inīoth two and three dimensional segmentation problems. Tool for the interface, which allows to parametrize any point of the space, inside or outside the closed polygon We use mean value coordinates as the parametrization Moving the control points, the active contour evolves. TheĬontour and the points of the image space are parametrized using a set of reduced control points that have toįorm a closed polygon in two dimensional problems and a closed surface in three dimensional problems. In this paper, we present a new framework for image segmentation based on parametrized active contours.
#J cole neighbors stereo full#
Full Papers Short Papers Posters Area 1 - Image Formation and Preprocessing Full Papers Paper Nr:Īctive Contour Segmentation with Affine Coordinate-based Parametrization
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