| Summary: | 碩士 === 國立交通大學 === 資訊工程研究所 === 83 === Computer vision systems often use shape information to
construct a geometric structure of an object and recognize this
object by using the domain knowledge. However, the domain
knowledge is hard to obtain. We usually acquire the domain
knowledge through interaction with experts. To solve the
problem to ask for experts, machine learning is introduced.
Many applications perform well to obtain the domain knowledge
by using machine learning. However, one deficiency is that the
knowledge acquired by machine learning is not easily
understandable to human. We tried to solve the deficiency by
using the new technique in machine learning, i.e., inductive
logic programming (ILP). To achieve our attempt, we use a
smoothing method to smooth a contour, determine the principal
axes of each contour by using Hotelling transform, utilize the
modified k-curvature algorithm to segment the processed
contours. After obtaining the segemnts, we propose the vectors
to describe the properties of each segment and the
interrelations between segments. We transform these vectors
into symbolic representations. Finally, we use FOIL which is an
ILP system to read these tuples and produce rules. These rules
reflect the characteristics of shape contours and the meanings
of these rules are intuitive to us. Our method can be an aided
tool to discover knowledge.
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