Abstract

Corners are special features in images, and are of great use in computing the optical flow and structure from motion. Conventionally, corners have been defined as the junction point of two straight line edges. Most existing edge detectors perform poorly at corners, mainly because they assume an edge to be an entity having infinite extent, which is violated at the corners. Since, most of the corner detectors are based on existing edge detectors, the performance of such corner detectors is not satisfactory. A corner point can be viewed as the intersection of two half-edges, oriented in 2 different directions, which are not 180° apart. This statement defines both a half-edge and a corner in terms of one another. This definition is the essence of the corner detection strategies presented in this paper. The corner detection algorithms rely on detecting half-edges. A half-edge detector uses information from a single orientation rather than opposing directions. We propose two algorithms for edge detection and corner detection, one is based on the First Directional Derivative of Gaussian and the other is based on the Second Directional Derivative of Gaussian. In addition to the location of the corner points, our algorithms also determine the corner angle and the corner orientation. The efficacy of the detectors has been demonstrated by experimental results for laboratory scenes.

Author Keywords: Corner detection; Edge detection; Gaussian filters; Half-edge detection; Image segmentation; Low-level vision

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