Perceptually Uniform Sampling

Perceptually uniform sampling aims to select data points or image features in a way that reflects human perception, ensuring that the sampled representation is both efficient and preserves important visual information. Current research focuses on developing algorithms that achieve this uniformity, often leveraging diffusion models or biologically-inspired approaches like receptive field simulations to improve robustness to noise and outliers. These advancements are impacting diverse fields, including image morphing, where smoother and more realistic interpolations are achieved, and face recognition, where improved accuracy and speed are obtained through more efficient gallery sampling.