Bayesian Inference in Model-Based Machine Vision

TL;DR

This paper presents a preliminary approach to integrating hierarchical Bayesian inference with detailed physical models for multi-sensor machine vision, aiming to improve reasoning about geometry, materials, and sensor data.

Contribution

It introduces a framework combining Bayesian inference with physical object representations for enhanced visual interpretation in machine vision.

Findings

Framework effectively ranks hypotheses based on probabilistic reasoning.

Integration of sensor models improves interpretation accuracy.

Preliminary results demonstrate potential for complex scene understanding.

Abstract

This is a preliminary version of visual interpretation integrating multiple sensors in SUCCESSOR, an intelligent, model-based vision system. We pursue a thorough integration of hierarchical Bayesian inference with comprehensive physical representation of objects and their relations in a system for reasoning with geometry, surface materials and sensor models in machine vision. Bayesian inference provides a framework for accruing_ probabilities to rank order hypotheses.