Event-based vision is a research field and methodological approach in computer vision that utilizes data from neuromorphic event cameras. These sensors asynchronously report pixel-level brightness changes as discrete events, offering advantages over traditional frame-based systems. This field investigates algorithms and systems that leverage the unique characteristics of event streams, such as high temporal resolution, low latency, and sparse data representation, for tasks including motion estimation, object tracking, and scene reconstruction. Its significance lies in its potential to overcome limitations of frame-based vision in challenging scenarios involving high speed, high dynamic range, or low power requirements, offering novel capabilities for robotics, autonomous systems, and high-performance monitoring.