2016/09/15 by Soheil Kolouri, Serim Park, Kolouri, Soheil +7 · 5 citations
Chemistry · Computer Science · Environmental Science · #AI in cancer detection #Bayesian Methods and Mixture Models #Computer Vision and Pattern Recognition (cs.CV) #Electrostatics and Colloid Interactions #FOS: Computer and information sciences #Hydrological Forecasting Using AI #Image Retrieval and Classification Techniques #Medical Image Segmentation Techniques #cs.CV
paper · pdf · doi:10.48550/arxiv.1609.04767
arxiv created 2016/09/15 · openalex publication_date 2016/09/15 · arxiv updated 2016/09/23 · openalex created_date 2022/08/30 · openalex updated_date 2026/07/28
Transport-based techniques for signal and data analysis have received increased attention recently. Given their abilities to provide accurate generative models for signal intensities and other data distributions, they have been used in a variety of applications including content-based retrieval, cancer detection, image super-resolution, and statistical machine learning, to name a few, and shown to produce state of the art in several applications. Moreover, the geometric characteristics of transport-related metrics have inspired new kinds of algorithms for interpreting the meaning of data distributions. Here we provide an overview of the mathematical underpinnings of mass transport-related methods, including numerical implementation, as well as a review, with demonstrations, of several applications.