Neural Network Localization Using Synthetic Landmark Images

The patent creates synthetic copies of landmark images through a generator network that learns from real landmark images. These synthetic images serve as training data for the localization network, replacing the need for extensive manually annotated real images. The generator produces realistic landmark images with corresponding position annotations, enabling training with substantially reduced data expenditure while maintaining position determination accuracy.

The generator network is trained beforehand to learn the distribution of real landmark images and their corresponding position annotations. Once trained, the generator can rapidly produce unlimited synthetic training data without requiring additional manual annotation or extensive training time. This preliminary training of the generator enables fast generation of diverse training samples, reducing overall training time while maintaining localization accuracy.

The generator network automatically generates synthetic landmark images with embedded position annotations without requiring manual annotation. The system serves itself by learning the annotation process during generator training and then autonomously producing annotated training data. This eliminates the need for manual annotation efforts while maintaining high data quality, significantly improving ease of data production and processing.

The generator network incorporates feedback mechanisms where the discriminator evaluates the realism of generated images and provides gradient feedback to improve generation quality. Additionally, the generator is trained to reproduce the statistical distribution of real landmark images, ensuring that synthetic images maintain the same positional relationships and visual characteristics as real data. This feedback-driven approach ensures that synthetic images preserve measurement precision while enabling efficient training operations.