Optimization of Line Extraction Based on an Affine Camera Model

Abstract

Line feature constitutes important geometric structure information in image processing and is significant in visual navigation and 3-D structure contour extraction of 3-D objects. The current state-of-the-art line extraction methods exhibit fast extracting speed and good extracting results. However, most line extraction methods face a problem, wherein a long line is easily fragmented. Thus, a long line is broken into several short segments due to local small changes or disturbances. To obtain longer and more useful line segments without the aid of external information, this paper proposes an improved line extraction method called affine-lines, which is based on an affine camera model. First, the affine camera model is used to simulate affine projections from different viewing angles, and a sequence of affine simulated images is obtained via affine transformation matrices. Subsequently, line segments are extracted from the original image and each simulated image. Each set of line segments on the simulated image is back-projected on the original image based on its corresponding inversely affine transformation matrix. Finally, the line segments on the original image are used as references to sequentially purify and optimize subsequent lines. Several defined geometric constraints are used to eliminate pseudo lines and combine short lines. Thus, fragmented lines are connected, and short lines are converted into long lines. Given several sets of close-range and aerial images, experiments are performed and compared via a state-of-the-art line extraction algorithm, called the line segment detector (LSD). The results indicate that the proposed method significantly increases the lengths of the obtained line segments, significantly reduces the fragmentation effect, and obtains more useful line segments. The extracted improved line segments are applied to line matching for building structures, and promising results are obtained.