To transcribe lines in an image, there are two possible schemes. In one scheme, we label on a physical basis. That is, we only transcribe lines which are physically in existence in the image. Whereas in the other scheme, we label on a perceptual basis - we identify outlines of trunks. Even if some part of a trunk is blocked by something else, we still mark it out because we know by perception that there should be a line hiding behind the blocking stuff. The first scheme fits common line detection algorithms better, which can only detect actually existing lines. The latter is more coherent to human perception, but it requires an intelligent line detection algorithm, which is really a challenge. We did evaluation experiments with both schemes on a small data set consisting of 10 images. The evaluation results below show that the performance of the edge and line detectors drops significantly with the second scheme. This is reasonable because there are much more long reference lines in this case while the line detector tends to output short lines, hence the rate of mismatch in length increases drastically.

  • Evaluation Results

    Scheme Conditions Reference Detection Error(%) Insertion
    Gaussian Variance High Threshold Low Threshold Line Threshold No Match Distance Mismatch Length Mismatch Slope Mismatch Total
    1 4 80 40 40 194 8514 3.1 11.9 58.8 0 73.7 8326
    2 4 80 40 40 254 8514 0.8 19.3 76.4 0 96.5 8262

  • Example Transcriptions (click on the images for magnification)

    Scheme 1 Scheme 2
    Example 1 for scheme 1 Example 1 for scheme 2
    Example 2 for scheme 1 Example 2 for scheme 2
    Example 3 for scheme 1 Example 3 for scheme 2
    Example 4 for scheme 1 Example 4 for scheme 2