Finding line segments in an intensity image has been one of the most fundamental issues in the area of computer vision. In complex scenes, it is hard to detect the locations of point features. Line features are more robust in providing greater positional accuracy. In this paper we present a robust “line feature extraction” algorithm which extracts line features in a single pass without using any assumptions and constraints. Our algorithm consists of six steps: (1) edge extraction, (2) edge scanning, (3) edge normalization, (4) line-blob extraction, (5) line-feature computation and (6) line linking. By using an edge scanning, the computational complexity due to too many edge pixels is drastically reduced. Edge normalization improves the local quantization error induced from the gradient space partitioning and minimizes perturbations on edge orientation. We also analyze the effects of edge processing, and the least squares-based method and the principal axis-based method on the computation of line orientation. We show its efficiency with some real images.