Learned Correspondence Masks for Cross-Perspective Image Registration

The patent replaces traditional mechanical feature matching algorithms with a machine learning-based deep neural network model. The neural network is trained to predict correspondence masks between images from different perspectives, substituting complex computational geometry operations with learned patterns from training data. This approach achieves high positioning accuracy while reducing computational complexity compared to traditional methods.

The patent changes the approach from explicit geometric parameter estimation to implicit coordinate transforms learned through neural networks. Instead of calculating transformations based on detected features and their geometric relationships, the system uses a neural network to directly map coordinates between different perspectives, achieving more accurate and efficient registration.

The patent changes the registration approach by using a neural network that learns to handle varying accuracy levels implicitly. The model is trained on diverse image pairs and automatically adapts to the quality and characteristics of input images, allowing accurate registration across different data sources without manual intervention or quality filtering.

The neural network-based registration system provides universal functionality to handle multiple image sources with different accuracy levels simultaneously. The single model can process both high-accuracy overhead images and lower-accuracy street-level images, as well as images from various other perspectives, maintaining performance across diverse inputs without requiring separate processing pipelines.