Growing process to extract image edges

Jia-Guu Leu; Wengang Lu

doi:10.1117/12.177717

1 June 1994 Growing process to extract image edges

Jia-Guu Leu, Wengang Lu

Author Affiliations +

Proceedings Volume 2238, Hybrid Image and Signal Processing IV; (1994) https://doi.org/10.1117/12.177717
Event: SPIE's International Symposium on Optical Engineering and Photonics in Aerospace Sensing, 1994, Orlando, FL, United States

Abstract

Edges are considered to possess the most valuable information of an image. Extracting image edges has been an essential step in many computer vision processes. Intensity gradient has been widely used as a tool to determine the existence of step or ramp edges. Thresholding can be applied to extract places where intensity gradient is high. One commonly encountered problem with this approach is that sometimes it is difficult to find an effective threshold value. In this paper we describe a growing process to extract image edges. Our goal is to extract fairly complete edges without many spurious segments. We first detect places where there is a clear indication that an edge exists with a higher gradient threshold value. These places are used as seed edges. These initial seed edges might not be complete. We then lower the gradient threshold to expand the initial edge map to include more and more weaker edge parts that are connected to what has already been extracted. In other words, weaker edges grow out from the seed edge map. Random intensity variations over homogeneous areas, because they are not connected to the seed edges, though their gradient values might be higher than the lower threshold, will not be extracted. We have also developed and utilized a pixel gradient ranking method so the resulting edges are single pixel thick and the extracted edges will locate at places where the intensity change is locally the highest. The approach has been tested on a number of real images and is found to be effective.

Citation Download Citation

Jia-Guu Leu and Wengang Lu "Growing process to extract image edges", Proc. SPIE 2238, Hybrid Image and Signal Processing IV, (1 June 1994); https://doi.org/10.1117/12.177717

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