Abstract A new signal processing method solves the problem of fitting multiple lines in a two-dimensional image. The Subspace-Based Line Detection (SLIDE) algorithm 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. Any spectral estimation method can then be exploited to obtain estimates of the line parameters. In particular, subspace-based algorithms of sensor array processing (e.g., the ESPRIT technique) can be used to produce closed-form and high resolution estimates for line parameters. 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 grey-scale image).
A method of estimating line parameters including angles and offsets of straight lines in a two-dimensional image comprising the steps of a) producing an edge-enhanced image from a grey-scale image, b) performing a constant-μ propagation on the image to obtain a measurement vector z, c) estimating the cisoidal components of said measurement vector z including the number of angles present, and d) estimating the offsets of lines for each angle.
Claim 2
Independent Claim
70 words
A method of estimating line parameters including angles and offsets of straight lines in a two-dimensional image comprising the steps of a) producing an edge-enhanced image from a grey-scale image, b) performing a constant-μ propagation on the image to obtain a measurement vector z, c) estimating the cisoidal components of said measurement vector z including the number of angles present, and d) estimating the offsets of lines for each angle.
Claim 3
Dependent Claim
48 words
The method as defined by claim 1 wherein step c) includes rearranging the measurement vector such that P snapshot vectors of size M are produced, computing the same covariance matrix of said snapshots, and performing eigendecomposition of said sample covariance matrix to estimate the number of angles present.
Claim 4
Dependent Claim
15 words
The method as defined by claim 1 wherein step d) includes using a least-squares minimization.
Claim 5
Dependent Claim
21 words
The method as defined by claim 1 wherein step d) includes projecting images along each detected angle and searching for peaks.
Claim 6
Dependent Claim
23 words
The method as defined by claim 1 wherein step d) includes applying a variable τ propagation stage of a subspace-based line detection algorithm.