Optimal blurred segments decomposition of noisy shapes in linear time
Computers and Graphics
Modeling knot geometry in norway spruce from industrial CT images
SCIA'03 Proceedings of the 13th Scandinavian conference on Image analysis
A discrete geometry approach for dominant point detection
Pattern Recognition
Power Watershed: A Unifying Graph-Based Optimization Framework
IEEE Transactions on Pattern Analysis and Machine Intelligence
Interactive segmentation based on component-trees
Pattern Recognition
Computers and Electronics in Agriculture
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Resolving a 3D segmentation problem is a common challenge in the domain of digital medical imaging. In this work, we focus on another original application domain: the 3D images of wood stem. At first sight, the nature of wood image looks easier to segment than classical medical image. However, the presence in the wood of a wet area called sapwood remains an actual challenge to perform an efficient segmentation. This paper introduces a first general solution to perform knot segmentation on wood with sapwood. The main idea of this work is to exploit the simple geometric properties of wood through an original combination of discrete connected component extractions, 2D contour detection and dominant point detection. The final segmentation algorithm is very fast and allows to extract several geometrical knot features.