Linear Feature Extraction using combined approach of Hough transform, Eigen values and Raster scan Algorithms

In this paper we propose a new method for linear geometric primitive identification which uses the generalized standard Hough transform (HT), Eigen value based statistical parameter analysis and Bresenham 's raster scan algorithms. In this method, we use the sparse matrix to find the Hough transform of the given image. Since sparse matrices squeeze zero elements and contain a small number of nonzero elements they provide an advantage in matrix storage space and computational time. Hough peaks are identified based on neighborhood suppression scheme. After finding the meaningful and distinct Hough peaks, coordinates of linear features in Hough space can be obtained using Bresenham's raster scan algorithm. Since quantization in parameter space of the HT gives both the real and false primitives because of quantization in the space of digital image, quantization in parameter space of HT as well as the fact that the edges in typical images are not perfectly constitutes the geometrical features, a statistical analysis is done using the eigen values to characterize and identifying the geometrical primitives. The proposed method has the advantages of small storage, high speed, and accurate digitization of Hough space and less line extraction error ratio over previously presented HT based techniques and its invariants.