Invariant Object Recognition by Artificial Neural Network Using Fahlman and Lebiere's Learning Algorithm

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概要

• 論文の詳細を見る

• A new neural network system for object recognition is proposed which is invariant to translation, scaling and rotation. The system consists of two parts. The first is a preprocessor which obtains projection from the input image plane such that the projection features are translation and scale invariant, and then adopts the Rapid Transform which makes the transformed outputs rotation invariant. The second part is a neural net classifier which receives the outputs of preprocessing part as the input signals. The most attractive feature of this system is that, by using only a simple shift invariant transformation (Rapid transformation) in conjunction with the projection of the input image plane, invariancy is achieved and the system is of reasonably small size. Experiments with six geometrical objects with different degrees of scaling and rotation shows that the proposed system performs excellent when the neural net classifier is trained by the Cascade-correlation learning algorithm proposed by Fahlman and Lebiere.

• 社団法人電子情報通信学会の論文
• 1993-07-25

著者

• Ito Kazuki

  The Department Of Computer Science And Systems Engineering Muroran Institute Of Technology

• Hamamoto Masanori

  the Department of Computer Science and Systems Engineering, Muroran Institute of Technology

• Kamruzzaman Joarder

  the Department of Electrical & Electronic Engineering, Bangladesh University of Engineering and Tech

• Kumagai Yukio

  the Department of Computer Science and Systems Engineering, Muroran Institute of Technology

• Kumagai Yukio

  Department Of Computer Science And Systems Engineering Muroran Institute Of Technology

• Hamamoto Masanori

  The Department Of Computer Science & Systems Engineering Muroran Institute Of Technology

• Kamruzzaman J

  Monash Univ. Aus

関連論文

• Enlargement of Multiple Cavernous Hemangioma of the Liver in Association with Pregnancy
• Invariant Object Recognition by Artificial Neural Network Using Fahlman and Lebiere's Learning Algorithm
• Performance Formulation and Evaluation of Associative Memory Extended to Higher Order
• Generalization Ability of Extended Cascaded Artificial Neural Network Architecture
• Comparison of Convergence Behavior and Generalization Ability in Backpropagation Learning with Linear and Sigmoid Output Units
• Robust Performance Using Cascaded Artificial Neural Network Architecture
• Incremental Learning and Generalization Ability of Artificial Neural Network Trained by Fahlman and Lebiere's Learning Algorithm

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