5.3.3 Four Basic Features of ga 5.4 Improvement of the Network Performance Using Regularization 5.5 Formulation of the problem 5.6 Numerical Examples 5.6.1 Classification of Synthetic Data 5.6.2 Classification of Vowels 28887%9 5.6.3 Classification with Regularization 5.7 Conclusion References 6 Knowledge-Based Occluded Object Recognition Based on New Interpretation of MFI and Floating Point Genetic 6.1 Introduction 6.2 Statement of the problem 6.3 Design of Knowledge-Based Occluded Object Recognizer 6.3.1 Local Feature Extraction 3.2 Training phase 108 6.3.3 Testing Phase l11 63 4 Condition of recognition l11 6.4 Control scheme of the vision process 6.5 Effectiveness of the Proposed Classifier 116 6.5.1 Recognition of the scene Consist of model Objects Mi and M2 116 6.5.2 Recognition of the Scene Consist of Model Objects M, M. and m 118 ro-Fuzzy Approach to Occluded Object Recognition Based on New Interpretation of MFI .2 Implementation of the New Interpretation of MFI on Back Propagation-Type Neural Network 122 7.3 Formulation of the problem 7.3.1 Local Feature Extraction 7.3.2 Process of fuzzification 7.3.3 Assignment of the Membership Function to the Consequent Part of the If-Then Rules 7. 3. 4 Process of Defuzzification 7.3.5 Generation of the Model-Based Object Recognition5.3.3 Four Basic Features of GA. . . . . . . . . . . . . . . . . . . . . . 80 5.3.4 Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82 5.4 Improvement of the Network Performance Using Regularization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83 5.5 Formulation of the Problem . . . . . . . . . . . . . . . . . . . . . . . . . . 83 5.6 Numerical Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87 5.6.1 Classification of Synthetic Data . . . . . . . . . . . . . . . . . . 87 5.6.2 Classification of Vowels . . . . . . . . . . . . . . . . . . . . . . . 98 5.6.3 Classification with Regularization . . . . . . . . . . . . . . . . . 99 5.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100 6 Knowledge-Based Occluded Object Recognition Based on New Interpretation of MFI and Floating Point Genetic Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101 6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102 6.2 Statement of the Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105 6.3 Design of Knowledge-Based Occluded Object Recognizer . . . . . 105 6.3.1 Local Feature Extraction . . . . . . . . . . . . . . . . . . . . . . . 106 6.3.2 Training Phase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108 6.3.3 Testing Phase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111 6.3.4 Condition of Recognition . . . . . . . . . . . . . . . . . . . . . . . 111 6.4 Control Scheme of the Vision Process . . . . . . . . . . . . . . . . . . . 113 6.5 Effectiveness of the Proposed Classifier . . . . . . . . . . . . . . . . . . 116 6.5.1 Recognition of the Scene Consist of Model Objects M1 and M2 . . . . . . . . . . . . . . . . . . . . . . . . . . . 116 6.5.2 Recognition of the Scene Consist of Model Objects M1, M2, and M3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118 6.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119 7 Neuro-Fuzzy Approach to Occluded Object Recognition Based on New Interpretation of MFI . . . . . . . . . . . . . . . . . . . . . . . . . . . 121 7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121 7.2 Implementation of the New Interpretation of MFI on Back Propagation-Type Neural Network. . . . . . . . . . . . . . . . . . . . . . 122 7.3 Formulation of the Problem . . . . . . . . . . . . . . . . . . . . . . . . . . 123 7.3.1 Local Feature Extraction . . . . . . . . . . . . . . . . . . . . . . . 123 7.3.2 Process of Fuzzification. . . . . . . . . . . . . . . . . . . . . . . . 125 7.3.3 Assignment of the Membership Function to the Consequent Part of the If–Then Rules . . . . . . . . . . . . . . 125 7.3.4 Process of Defuzzification . . . . . . . . . . . . . . . . . . . . . . 126 7.3.5 Generation of the Model-Based Object Recognition Scheme . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127 Contents xi