Neural network approach for object orientation classification
Keith K. Yeung, Pierre Zakarauskas, Allan G. McCray · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1991
A neural network approach to determine the heading orientation of a known object in a noisy image is presented. The gray-scaled image is first preprocessed by the following four procedures: 1) An edge map of the object is extracted using the Sobel edge operator; 2) The discrete 2-D Fourier transform is applied to the edge map to eliminate the translational variance; 3) The Fourier power coefficients are mapped into a polar coordinate system; 4) The amplitudes of the Fourier coefficients in each five-degree angular sector are summed to form a 1-D input vector to the neural network. A backpropagation neural network with one hidden layer was trained with a sequence of seven noise-free object outlines with heading ranging from 0 to 90 deg in 15 deg increments. After the training was complete, the network was tested with three noisy images taken from randomly selected object orientations. The network successfully classified the appropriate headings in each case. These results illustrate the robustness of this neural network design in performing heading classification from noisy images