A comparison of two tree representations for data-driven volumetric image filtering

We compare two tree-based, hierarchical representations of volumetric gray-scale images for data-driven image filtering. One representation is the max-tree, in which tree nodes represent connected components of all level sets of a data set. The other representation is the watershed tree, consisting of nodes representing nested, homogeneous image regions. Region attribute-based filtering is achieved by pruning the trees. Visualization is used to compare both the filtered images and trees. In our comparison, we also consider flexibility, intuitiveness, and extendability of both tree representations

Part-based segmentation by skeleton cut space analysis

We present a new method for part-based segmentation of voxel shapes that uses medial surfaces to define a segmenting cut at each medial voxel. The cut has several desirable properties–smoothness, tightness, and orientation with respect to the shape’s local symmetry axis, making it a good segmentation tool. We next analyze the space of all cuts created for a given shape and detect cuts which are good segment borders. Our method is robust to noise, pose invariant, independent on the shape geometry and genus, and is simple to implement. We demonstrate our method on a wide selection of 3D shapes.</p