Image Invariant Moments and Pattern Recognition for Aircraft Type Matching

Using image invariant moments and pattern recognition techniques enables the construction of moment features for 3D aircraft images. The implementation typically involves calculating Hu's seven invariant moments through image preprocessing and contour extraction, which remain unchanged under translation, rotation, and scaling transformations. By applying the improved D-S evidence theory absorption method, image fusion effectiveness can be further enhanced through evidence combination rules that handle uncertainty in feature matching. This methodology can be applied to aircraft model matching, achieving more accurate aircraft identification and classification. The algorithm workflow generally includes feature vector normalization, basic probability assignment calculation, and evidence combination using Dempster's rule. Furthermore, additional image processing and feature extraction techniques can be incorporated to improve aircraft image analysis, such as Convolutional Neural Networks (CNN) for automated feature learning through convolutional and pooling layers, and Local Binary Patterns (LBP) for texture feature extraction using neighborhood pixel comparison. Through continuous exploration and refinement of these methods, the application domain of aircraft image processing can be expanded, providing more possibilities for research and development in aircraft-related fields. Code implementation would typically involve OpenCV for moment calculation, TensorFlow/PyTorch for CNN implementation, and custom evidence combination functions for D-S theory.