Fuzzy Classification of Image Patterns Using Invariant Moment Features and Neural Networks

In our experimental implementation based on the research paper "Fuzzy Classification of Image Patterns Using Invariant Moment Features and Neural Networks," we conducted our study using MATLAB. The implementation includes complete code for generating seven invariant moments and performing neural network classification, accompanied by the original research paper. This research focuses on in-depth investigation of fuzzy classification for image patterns by combining invariant moment features with neural networks. We provide detailed exploration of the generation methods for invariant moment features and the implementation process of neural network classification algorithms. The invariant moment generation involves calculating seven Hu moments that remain unchanged under translation, rotation, and scaling transformations, while the neural network implementation includes feature normalization, network architecture design, and training procedures. Through these experiments and research, we have derived significant conclusions and findings that contribute valuable references to this research field. The code structure includes modular functions for image preprocessing, moment calculation, feature extraction, network training, and classification validation.