Corner Detection From Thinned Edge Images Using A Kalman Filter

S. Sengupta; P. M. Lynch; R. Vangal

doi:10.1117/12.969832

1 March 1990 Corner Detection From Thinned Edge Images Using A Kalman Filter

S. Sengupta, P. M. Lynch, R. Vangal

Proceedings Volume 1193, Intelligent Robots and Computer Vision VIII: Systems and Applications; (1990) https://doi.org/10.1117/12.969832
Event: 1989 Symposium on Visual Communications, Image Processing, and Intelligent Robotics Systems, 1989, Philadelphia, PA, United States

Abstract

An algorithm is described to detect corners from thinned edge maps of polygonal or polyhedral objects using a Kalman filter. Straight lines are modelled as constant slope segments. The length of a line is assumed to increase at a constant rate as more points are added to the segment. The state vector consists of the angle of inclination of the straight line, its length and the rate of change of the length with each new pixel. A linear state transition equation is developed based on this model. Plant noise is added to account for the fact that the straight lines are digital straight lines, and it is assumed to be zero mean, Gaussian and uncorrelated. The system measurement consists of the pixel coordinates of the edge. These coordinates can be expressed as a nonlinear function of the state vector. The measurement errors are also assumed to be zero mean, Gaussian and uncorrelated. Using this linear plant model and a nonlinear measurement model, an extended Kalman filter is used to track the line segments. The divergence of the filter from its steady state is detected to locate a corner. Tests done indicate that this technique works well. The results are compared to the results obtained by using a difference of low pass (DOLP) corner detector.

Citation Download Citation

S. Sengupta, P. M. Lynch, and R. Vangal "Corner Detection From Thinned Edge Images Using A Kalman Filter", Proc. SPIE 1193, Intelligent Robots and Computer Vision VIII: Systems and Applications, (1 March 1990); https://doi.org/10.1117/12.969832

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