Dataset: Multiplexed Illumination for Classifying Visually Similar Objects

Abstract

This dataset accompanies the paper Multiplexed Illumination for Classifying Visually Similar Objects. Project details are here: https://roboticimaging.org/Projects/LSClassifier/ The dataset contains 16000 10-bit images of five types of real and synthetic fruit. It is split across three categories: Relightable models: high-quality single-illuminant images. These drive the pattern selection and classifier training, and can be used to devise and evaluate new multiplexing schemes. SNR-Optimal: Captured with inference-time conditions, with more evident noise, and with illumination patterns selected to be optimal in terms of signal-to-noise (SNR) ratio. Greedy: Also captured with inference-time conditions, these patterns were jointly trained along with the classifier using our proposed greedy pattern selection scheme. Preprint of paper available at: https://arxiv.org/abs/2009.11084