Sequential Decision Model for Inference and Prediction on Non-Uniform Hypergraphs with Application to Knot Matching from Computational Forestry

TL;DR

This paper introduces a sequential decision model for non-uniform hypergraph inference, specifically applied to knot matching in computational forestry, improving automatic lumber strength prediction.

Contribution

It formulates knot matching as a quadripartite matching problem and develops an efficient sequential decision model with Monte Carlo sampling for graph matching.

Findings

Effective on 30 annotated boards

Supports rapid sampling of graph matchings

Demonstrates strong performance in simulation studies

Abstract

In this paper, we consider the knot matching problem arising in computational forestry. The knot matching problem is an important problem that needs to be solved to advance the state of the art in automatic strength prediction of lumber. We show that this problem can be formulated as a quadripartite matching problem and develop a sequential decision model that admits efficient parameter estimation along with a sequential Monte Carlo sampler on graph matching that can be utilized for rapid sampling of graph matching. We demonstrate the effectiveness of our methods on 30 manually annotated boards and present findings from various simulation studies to provide further evidence supporting the efficacy of our methods.