Computer Vision and Its Relationship to Cognitive Science: A perspective from Bayes Decision Theory

TL;DR

This paper explores the relationship between computer vision and cognitive science through Bayes Decision Theory, comparing Bayesian and deep neural network approaches, and discusses their integration and limitations.

Contribution

It provides a theoretical perspective linking computer vision approaches with cognitive science using Bayes Decision Theory, highlighting their strengths, weaknesses, and potential integration.

Findings

BDT captures key concepts of both Bayesian and neural network approaches

Deep neural networks have driven industry success in computer vision

Discussion of limitations suggests pathways for combining approaches

Abstract

This document presents an introduction to computer vision, and its relationship to Cognitive Science, from the perspective of Bayes Decision Theory (Berger 1985). Computer vision is a vast and complex field, so this overview has a narrow scope and provides a theoretical lens which captures many key concepts. BDT is rich enough to include two different approaches: (i) the Bayesian viewpoint, which gives a conceptually attractive framework for vision with concepts that resonate with Cognitive Science (Griffiths et al., 2024), and (ii) the Deep Neural Network approach whose successes in the real world have made Computer Vision into a trillion-dollar industry and which is motivated by the hierarchical structure of the visual ventral stream. The BDT framework relates and captures the strengths and weakness of these two approaches and, by discussing the limitations of BDT, points the way to how they can be combined in a richer framework.