Image partitioning into convex polygons

Liuyun Duan (1) , Florent Lafarge (1)

1 TITANE - Geometric Modeling of 3D Environments

The over-segmentation of images into atomic regions has become a standard and powerful tool in Vision. Traditional superpixel methods, that operate at the pixel level, cannot directly capture the geometric information disseminated into the images. We propose an alternative to these methods by operating at the level of geometric shapes. Our algorithm partitions images into convex polygons. It presents several interesting properties in terms of geometric guarantees , region compactness and scalability. The overall strategy consists in building a Voronoi diagram that conforms to preliminarily detected line-segments, before homogenizing the partition by spatial point process distributed over the image gradient. Our method is particularly adapted to images with strong geometric signatures, typically man-made objects and environments. We show the potential of our approach with experiments on large-scale images and comparisons with state-of-the-art superpixel methods.

Mots clés: superpixel; computer vision; image segmentation; Voronoi diagram; geometric shape

Domaines: Mathématique discrète [cs.DM]

Liuyun Duan, Florent Lafarge. Image partitioning into convex polygons. IEEE conference on Computer Vision and Pattern Recognition (CVPR), Jun 2015, Boston, United States. ⟨hal-01140320⟩