| Summary: | 碩士 === 靜宜大學 === 管理科學研究所 === 82 === Pattern recognition has been a well-known complicated problem.
Although, numerous efforts have been made based on
traditional computer, they still suffered by the time-
consumed procedure. By the invent of neural networks, which is
an architecture mimicking the spirt of human brain, the
research of pattern recognition is promoted based on the new
technology. In this research, a hybrid neural system is
proposed to attack shape recognition with invariant for
rotation, scaling and distortion. In the system, some efficient
preprocess are proposed to extract shape features. Based up on
those features, the most popular neural networks, back-
propagation (BP), is used to learn and recall. The hybrid
neural system has been implemented on C language. Also, The
benchmark of 2-D plane shape is selected to test the hybrid
neural system. The simulation results show that the proposed
system achieve 97% recognition rate even thought the test
patterns are scaled, rotated and distorted. Since the proposed
system is powerful and efficient for recognition of object
contour, it has very high potential for real-time system. In
other word, it can be applied to objective searching, Chinese
recognition, character recognition, and so on. On the
information management point of view, the proposed system has
achieved a significant contribution on office automation.
|
|---|
