Abstract:
A new part segmentation approach is presented which
works on real 2D images. These images may contain a
complex foreground 3D object with textures and shadows
on a cluttered background. The proposed approach relies on
the outline of the object to guide the grouping of lines using
symmetry and colinearity principles. This grouping of lines
leads to simple shapes which could be modeled by 3D
primitives such as geons or general cylinders. An algorithm
implemented on the basis of this approach appears robust to
noise and generic conditions. Besides, intermediate-level
symmetries employed by the algorithm ensure a good
robustness to internal textures and markings. The results
obtained demonstrate the validity of the approach as a mean
towards 3D generic object recognition from real 2D
images.