Interpreting visual signals introduces both challenges and opportunities in the design of control and autonomous systems. This talk will explore two key concepts that address these challenges. In the first part, I will introduceperception contracts—an innovative approach to analyzing visual control systems that rely on Deep Neural Networks for state estimation. A perception contract provides an over-approximation of a state estimator while guaranteeing closed-loop system invariants. These contracts can be automatically synthesized using data and model-based analysis and have been successfully applied to systems such as automated landing controllers and lane-keeping systems. The second part of the talk will focus on algorithms for computing indistinguishable sets—sets of states that cannot be distinguished based on available visual data. These sets help define the theoretical limits of visual control, revealing the boundaries of what can be achieved with coarse measurements in dynamic environments. Throughout the talk, I will mention various examples, highlight the tools available, and discuss open problems that invite further exploration in this area.