| Summary: | 碩士 === 元智大學 === 工業工程研究所 === 83 === A new corner detection method based on artificial neural
network is proposed.In this research,two neural network
models are considered. One neural model participates in
detecting corner points of objects, and the other in the
detecting of tangent points and inflection points of
objects.The input features of the first neural model are the
coordinate vector of the forward segment or the backward
segment with respect to a given boundary point. The output
features of the first neural model is the angle between the X-
axis and the forward segment (or backward segment). We
use the difference of the angle between the forward
segment and the backward segment as the curvature of the
boundary point. If the curvature of a point is smaller than
a given threshold and is a local minimum, this point is
identified as a corner point. Tagent and inflection points
are typical feature points of most man-made industrial
parts. Because the curvatures of the tangent and
inflection point are not explicit, we can not directly use the
magnitude of the curvature to detect the tangent and
inflection points. The chage of the curvature in the
neighborhood of a given boundary point of these two feature
points is different from that of non- tangent and non-
inflection points. Therefore, we use the sign pattern of the
curvatures as the input features of a second neural model.
The second neural model only responds to tangent and
inflection points. The effectiveness of the detectors has
been demonstrated by experimental results of various
laboratory scenes.
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