Object recognition using multiple view invariance based on complex features

Geometric invariants from multiple views provide useful information for 3D object recognition. However, conventional object recognition methods using invariants based on point features cannot achieve efficient recognition because of large amount of combinations of point features in invariant calculation. To avoid this problem, the authors propose to use more complex features. They adopt arrow junctions and conics as complex features because man-made objects have often trihedral polyhedra (eg. parallelepiped) and circles and they make arrow junctions and conics in images, respectively. The multiple view affine invariance theory can be directly used for arrow junctions. For conics, they propose two types of invariants. They have developed an object recognition method exploiting these invariants. In addition to the recognition method with two input images, they propose a recognition method that needs only a single input image by substituting an image of a target object stored in the model library. Experimental results using 240 pair of images for 24 objects confirm the usefulness of the methods.