  This paper presents a model-driven approach to detect image line segments.
The approach incrementally detects segments on the gradient image using a
linear Kalman filter that estimates the supporting line parameters and their
associated variances. The algorithm is fast and robust with respect to image
noise and illumination variations, it allows the detection of longer line
segments than data-driven approaches, and does not require any tedious
parameters tuning. An extension of the algorithm that exploits a pyramidal
approach to enhance the quality of results is proposed. Results with varying
scene illumination and comparisons to classic existing approaches are
presented.
