Three-dimensional object recognition using similar triangles and decision trees

摘要

A system, TRIDEC, that is capable of distinguishing between a set of objects, despite changes in the objects' positions in the input field, their size, or their rotational orientation in three-dimensional (3D) space, is described. TRIDEC combines very simple yet effective features with the classification capabilities of inductive decision tree methods. The feature vector is a list of all similar triangles defined by connecting all combinations of three pixels in a coarse coded 127 × 127 pixel input field. The classification is accomplished by building a decision tree using the information provided from a limited number of translated, scaled, and rotated samples. Simulation results are presented which show that TRIDEC achieves 94% recognition accuracy in the two-dimensional (2D) invariant object recognition domain and 98% recognition accuracy in the 3D invariant object recognition domain after training on only a small sample of transformed views of the objects.