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Scale-adaptive landmark detection, classification and size estimation in 3D object-background images

A method, based on the gradient square tensor (GST), to detect, classify and estimate the size of landmarks based on rods, plates and surfaces in 3D object-background images is described. Scale is automatically adapted to the local situation. Results show that the trace of the GST detects landmarks in composite objects and the determinant detects endpoints of rods. The relation between scale at maximum response and landmark size depends on landmark type. Landmarks can be classified by estimating cylindricality and planarity, derived from the GST-eigenvalues.