A subspace fitting approach to super resolution multi-line fitting and straight edge detection

A new fundamental signal processing method is developed for solving the problem of fitting multiple lines in a two-dimensional image. The proposed technique formulates the multiline fitting problem in a special parameter estimation framework such that a signal structure similar to the sensor array processing signal representation is obtained. Then, recently developed algorithms in that formalism (e.g., the ESPRIT technique) are exploited to produce superresolution estimates in this framework. The signal representation used in this formulation can be generalized in a fashion to handle both problems of line fitting (in which a set of binary-valued discrete pixels is given) and of straight edge detection (in which one starts with a gray-scale image). The proposed method possesses extensive computational speed superiority over previous single- and multiple-line fitting algorithms such as the Hough transform method. Details of the new formulation are explained, and several experimental results are presented