Enhancement of Contour Smoothness by Substitution of Interpolated Sub-Pixel Points for Edge Pixels

Abstract

This study designed a sub-pixel precision edge detecting algorithm to enhance contour smoothness. First, the coordinate value of RGB pixel is projected on the space line of R=G=B to obtain gray image. Then, pixel edges are located using a Canny detector. Next, the edge width is thinned to a single pixel using a morphological thinning operation. Finally, sub-pixel-level smooth contours are extracted by interpolation. In this sub-pixel level contour extraction process, a Single-Pixel-Multi-Point interpolation method was developed to enhance edge smoothness and obtain high precision in edge estimation. This method divides edges in a $3\times 3$ pixels block into nine arrangement modes. According to the arrangement of the eight neighborhoods of a centered edge pixel, different locations of interpolated sub-pixel points are calculated by interpolation with Bezier curves. For symmetrically arranged linear edge pixels, this method can be used to determine the exact contour. Experimental results showed that the proposed algorithm can improve the smoothness of image edge contour. As the curvature of the edge increases, the maximum systematic error will increase. For the edge pixel centered in the $3\times 3$ pixels block and two pixels located at the corner of one side, the max systematic error is 0.5 pixel. For two edge pixels aligned in a single row or column with one located at a corner, the max systematic error is 0.25 pixel.

Keywords

• Computer vision
• image segmentation
• sub-pixel contour detection