For segmentation, instead of the neural networks of your mind, we will use computer algorithms that adhere to the minima rule. These images illustrate the proposed segmentation algorithm that is based on the minima rule for boundaries and visual saliency for parts. Note that most observes decompose this teapot into a spout, a body, a handle, and a notch on the top. The minima rule from cognitive psychology research suggests how shape and in particular curvature guides segmentation. Green regions denote flat sections.
Blue regions are areas of deep concavities.
Yellow regions are convex sections. The sporadic appearance of blue triangles in yellow regions and other mixings represent noise either from the data collection process or the differential geometry calculations. Each part is separated from the other parts and painted with a unique color. This segmentation used the curvature estimate in the previous image to identify contours of minima negative curvature, which are contours within the blue regions. These contours form the part boundaries and specify where to segment the teapot.