Contour polygonal approximation using shortest path in networks
This work addresses the problem of efficiently simplifying contours for applications in computer vision and image processing, though it appears incremental as it builds on existing network-based approaches.
The paper tackles contour polygonal approximation by proposing a novel method based on Complex Networks theory, transforming contour points into a Small-World network and using geodesic paths to compute approximations, with evaluation on benchmark contours showing competitive results compared to other methods.
Contour polygonal approximation is a simplified representation of a contour by line segments, so that the main characteristics of the contour remain in a small number of line segments. This paper presents a novel method for polygonal approximation based on the Complex Networks theory. We convert each point of the contour into a vertex, so that we model a regular network. Then we transform this network into a Small-World Complex Network by applying some transformations over its edges. By analyzing of network properties, especially the geodesic path, we compute the polygonal approximation. The paper presents the main characteristics of the method, as well as its functionality. We evaluate the proposed method using benchmark contours, and compare its results with other polygonal approximation methods.