This paper presents a new approach to 3D object recognition by using an Octree model library (OML) I, II and fast search algorithm. The fast search algorithm is used for finding the 4 pairs of feature points to estimate the viewing direction uses on effective two level database. The method is based on matching the object contour to the reference occluded shapes of 49, 118 viewing directions. The initially bestmatched viewing direction is calibrated by searching for the 4 pairs of feature points between the input image and the image projected along the estimated viewing direction. At this point, the input shape is recognized by matching it to the projected shape. The computational complexity of the proposed method is shown to be O(n^2) in the worst case, and that of the simple combinatorial method of O(m^4,n^2), where n and m denote the number of feature points of the 3D model object and the 2D object, respectively.
Fuzzy logic, neural network, fuzzy-neural networks play an important role in the linguistic modeling of intelligent control and decision making in complex systems. The Fuzzy-Neural Network (FNN) learning represents one of the most effective algorithms to build such linguistic models. This paper proposes an Artificial Immune Algorithm (AIA) based optimal learning fuzzy-neural network (IM-FNN). The proposed learning scheme includes the discovery of the fuzzy-neural network structure which can handle linguistic knowledge and the tuning of the membership function of the fuzzy inference system is achieved by AIA. The learning algorithm of the IM-FNN is composed of two phases. The first phase is to find the initial membership functions of the fuzzy neural network model. In the second phase, immune algorithm is used for tuning the membership functions of the proposed model. This paper also suggests techniques in determining the values of the steady-state equivalent circuit parameters of a three-phase squirrel-cage induction machine using immune algorithm.