Sensor array processing techniques for super resolution multi-line-fitting and straight edge detection

A new signal processing method is developed for solving the problem of fitting multiple lines in a two-dimensional image. The proposed technique formulates the multi-line-fitting problem in a special parameter estimation framework such that a signal structure similar to the sensor array processing signal representation is obtained. Then the recently developed algorithms in that formalism (e.g., the ESPRIT technique) can be exploited to produce super resolution estimates for line parameters. The number of lines may also be estimated in this framework. The signal representation employed in this formulation can be generalized 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). Details of the proposed algorithm and several experimental results are presented. The proposed method possesses extensive computational speed superiority over existing single and multiple line fitting algorithms such as the Hough transform method. Potential application areas of the proposed technique include road tracking in robotic vision, mask-wafer alignment in semiconductor manufacturing, aerial image analysis, text alignment in document analysis, particle tracking in bubble chambers, and similar applications.