A subspace fitting approach to super resolution multi-line fitting and straight edge detection
- 1 January 1992
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
Abstract
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.Keywords
This publication has 7 references indexed in Scilit:
- On improving the accuracy of the Hough transform: theory, simulations, and experimentsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2003
- On navigating between friends and foesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1991
- Detection and estimation in sensor arrays using weighted subspace fittingIEEE Transactions on Signal Processing, 1991
- ESPRIT-estimation of signal parameters via rotational invariance techniquesIEEE Transactions on Acoustics, Speech, and Signal Processing, 1989
- Multiple emitter location and signal parameter estimationIEEE Transactions on Antennas and Propagation, 1986
- Detection of signals by information theoretic criteriaIEEE Transactions on Acoustics, Speech, and Signal Processing, 1985
- Use of the Hough transformation to detect lines and curves in picturesCommunications of the ACM, 1972