Linear feature extraction based on an AR model edge detector

Extraction of linear features is an important task in many image understanding and computer vision systems. In this paper, we describe a linear feature extracor which operates on the edge pixels detected by a space varying 2 - D autoregressive (AR) model. The sequence of steps for extraction of linear segments consists of tracing the edge points, linking and fitting with lines. The heuristics used in tracing and linking steps are specific to the characteristics of the AR model based edge detector. The performance of the linear feature extractor is illustrated using real images.

[1]  Rama Chellappa,et al.  Edge detection using zero crossings of directional derivatives of a random field model , 1986, ICASSP '86. IEEE International Conference on Acoustics, Speech, and Signal Processing.

[2]  Ramakant Nevatia,et al.  Segment-based stereo matching , 1985, Comput. Vis. Graph. Image Process..

[3]  B. Anderson,et al.  Optimal Filtering , 1979, IEEE Transactions on Systems, Man, and Cybernetics.

[4]  Thomas S. Huang,et al.  Determining 3-D motion and structure of a rigid body using straight line correspondences , 1983, ICASSP.

[5]  Robert M. Haralick,et al.  Digital Step Edges from Zero Crossing of Second Directional Derivatives , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Allen R. Hanson,et al.  Extracting Straight Lines , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  K. Ramesh Babu,et al.  Linear Feature Extraction and Description , 1979, IJCAI.

[8]  Ramakant Nevatia,et al.  Locating Structures in Aerial Images , 1982, IEEE Transactions on Pattern Analysis and Machine Intelligence.