Thesis: Requirement-Driven Neurosymbolic Visual Recognition

This thesis explores combining symbolic requirements with neural approaches for industrial visual recognition applications. The work involves mapping object knowledge into logical, relational, and geometric constraints and evaluating two integration methods: post-hoc verification and requirement-aligned learning. The goal is to develop a methodology and prototype for requirement-driven visual reasoning to improve prediction consistency, robustness, interpretability, or data efficiency.