Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
527380 | Image and Vision Computing | 2010 | 10 Pages |
Abstract
In this paper, we present a new geodesic distance transform that uses a non-Euclidean metric suitable for non-convex discrete 2D domains. The geodesic metric used is defined as the shortest path length through a set of pixels called Locally Nearest Hidden Pixels, and manages visibility zones using bounding angles. The algorithm is designed using ordered propagation, which makes it extremely efficient and linear in the number of pixels in the domain. We have compared our algorithm with the four most similar geodesic distance transform techniques, and we show that our approach has higher accuracy and lower computational complexity.
Keywords
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Physical Sciences and Engineering
Computer Science
Computer Vision and Pattern Recognition
Authors
Rubén Cárdenes, Carlos Alberola-López, Juan Ruiz-Alzola,