Using Local Orientational Information As Image Primitive For Robust Object Recognition

Orientational information can replace the traditional edges as basic image features ("primitives") for object recognition. A comparison of five different orientation operators on 3 x 3 windows is carried out, and it is found that these operators have similar performance. A first attempt at object recognition searches for minimum root mean square deviation of orientation in a picture. This technique shows better object discrimination than the traditional normalized cross-correlation of grey-level images. Additionally the parameters of the Gaussian distribution of orientational correlation can be accurately predicted by a simple theoretical model. The orientational correlation technique shows difficulties in recognizing geometrically distorted and partially occluded objects. For this reason a very robust algorithm for the recognition of simple objects is developed, based only on orientationl information as image feature, and local polar coordinates for the model of the object. Practical examples taken under difficult, natural conditions illustrate the reliable performance of the proposed algorithm, and it becomes apparent that orientational information is indeed a powerful image primitive.