Linear-time algorithms for region growing with applications to image and curve segmentation

Peter Veelaert

doi:10.1117/12.292788

20 October 1997 Linear-time algorithms for region growing with applications to image and curve segmentation

Peter Veelaert

Proceedings Volume 3168, Vision Geometry VI; (1997) https://doi.org/10.1117/12.292788
Event: Optical Science, Engineering and Instrumentation '97, 1997, San Diego, CA, United States

Abstract

The goal of segmentation is to partition a digital image or curve into segments such that the points in each segment share a common property. For example, we can partition a curve into connected subsets such that the points of each subset lie on a common straight line, or we can partition an image such that intensity function is linearly varying when restricted to ne part. A region growing algorithm starts from a small seed segment, and then repeatedly tries to add new points to this segment. Each time a point is added seed segment, whether the segmentation criterion is still satisfied for the enlarge segment, otherwise a new segment is started. In general, the verification of the segmentation criterion becomes increasingly more difficult when the segment gets larger. We propose new linear-time algorithms for region growing. These algorithms are related to the economical design of mechanical frameworks, where the goal is to make a rigid construction with as few bars as possible. According to this analogy, the region growing algorithm tries to attach each new point as firmly as possible to the existing region with a minimal amount of computation. We illustrate this technique for the segmentation of digital curves into straight or parabolic line segments, and for image segmentation with segments of linearly varying intensity.

Citation Download Citation

Peter Veelaert "Linear-time algorithms for region growing with applications to image and curve segmentation", Proc. SPIE 3168, Vision Geometry VI, (20 October 1997); https://doi.org/10.1117/12.292788

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