Unsupervised clustering in Hough space for identification of partially occluded objects Yáñez-Suárez, Oscar ; Azimi-Sadjadi, Mahmood R. An automated approach for template-free identification of partially occluded objects is presented. The contour of each relevant object in the analyzed scene is modeled with an approximating polygon whose edges are then projected into the Hough space. A structurally adaptive self-organizing map neural network generates clusters of collinear and/or parallel edges, which are used as the basis for identifying the partially occluded objects within each polygonal approximation. Results on a number of cases under different conditions are provided. Colorado State University. Libraries 1999 text ; image application/pdf ECEmra00013.pdf FACFECEN100493ARTI eng c1999 IEEE
Unsupervised clustering in Hough space for identification of partially occluded objects
Yáñez-Suárez, Oscar ; Azimi-Sadjadi, Mahmood R.
An automated approach for template-free identification of partially occluded objects is presented. The contour of each relevant object in the analyzed scene is modeled with an approximating polygon whose edges are then projected into the Hough space. A structurally adaptive self-organizing map neural network generates clusters of collinear and/or parallel edges, which are used as the basis for identifying the partially occluded objects within each polygonal approximation. Results on a number of cases under different conditions are provided.
Colorado State University. Libraries
1999
text ; image
application/pdf
ECEmra00013.pdf
FACFECEN100493ARTI
eng
c1999 IEEE