Fluency Function Approximation of Thin Line Images

Abstract

The authors have succeeded in a series of research on function approximation of raster images and their subsequent reconstruction in a scalable manner. However, these earlier research, in which the target of approximation being the image contour, suffered from the quality problem of uneven line width. This problem is particularly apparent when it was applied to images having a large number of thin lines such as maps and circuit diagrams. In this paper, in order to function approximate thin line images with high quality, a novel contour tracking method is proposed that considers both characteristics of connectivity and continuity between contour segments. Furthermore, function approximation of the tracked pixel sequences by the suitable Fluency functions that include straight line, arc and second-degree curve is performed. To verify its effectiveness, the proposed contour tracking method is applied to blank map images for qualitative evaluation.