Edinburgh Research Explorer

Probabilistic Inference and Bayesian Priors in Visual Perception

Research output: Chapter in Book/Report/Conference proceedingChapter

Original languageEnglish
Title of host publicationBiologically Inspired Computer Vision
Subtitle of host publicationFundamentals and Applications
PublisherWiley-VCH Verlag GmbH & Co. KGaA
Pages201-220
Number of pages20
ISBN (Electronic)9783527680863
ISBN (Print) 9783527412648
DOIs
Publication statusPublished - 2 Nov 2015

Abstract

This chapter explains what it means to view visual perception as Bayesian inference. It reviews studies using this approach in human psychophysics. Central to Bayesian inference is the notion of priors. The chapter explains which priors are used in human visual perception and how they can be learned. It briefly addresses how Bayesian inference processes could be implemented in the brain, a question still open to debate. Bayesian inference as a model of how the brain works thus rests on critical assumptions that can be tested experimentally. An observer who uses Bayesian inference is called an ideal observer. Bayesian models and probabilistic approaches have been increasingly popular in the machine vision literature. A number of models have been proposed that suggest how Bayesian inference could be implemented in the neural substrate. Similarly, a number of suggestions have been made about how visual priors could be implemented.

    Research areas

  • Bayesian inference, Bayesian priors, probabilistic inference, Visual Perception

ID: 23007224