Abstract: Image-based, non-invasive flow measurement techniques such as Particle Image Velocimetry (PIV), Particle Tracking Velocimetry (PTV), and Background-Oriented Schlieren (BOS) represent state-of-the-art methodologies for estimating fluid velocity fields and subsequently deriving pressure and density fields. These techniques are widely applied across critical domains and at varying length scales—ranging from microscale diffusion and rheology measurements to mesoscale cardiovascular flows, multiphase mixing, and even macroscale high Reynolds number flows. For these methods to effectively inform industrial design decisions or validate computational fluid dynamics (CFD) simulations, it is imperative to quantify the uncertainty in the experimentally estimated velocity fields. While significant efforts have been made to minimize measurement errors within the PIV community, the critical issue of accurately estimating local instantaneous uncertainties in PIV velocity vectors remains underexplored. The conventional approach of assigning a uniform uncertainty of 0.1 pixels across the field fails to account for variations in local flow features or measurement noise. This challenge arises from the complexity of modeling uncertainty due to multiple sources of error and their nonlinear interactions within the measurement chain transfer function. This talk will introduce novel uncertainty quantification methods for 2D, stereo, and 3D PIV/PTV measurements. These methods have been experimentally validated, and their relevance is demonstrated across diverse applications, thereby significantly advancing measurement accuracy and reliability.