Journal article
LEARNING RELATIONAL STRUCTURES - APPLICATIONS IN COMPUTER VISION
AR PEARCE, T CAELLI, WF BISCHOF
APPLIED INTELLIGENCE | KLUWER ACADEMIC PUBL | Published : 1994
DOI: 10.1007/BF00872092
Abstract
We present and compare two new techniques for learning Relational Structures (RSs) as they occur in 2D pattern and 3D object recognition. These techniques, namely, Evidence-Based Networks (EBS-NNets) and Rulegraphs combine techniques from computer vision with those from machine learning and graph matching. The EBS-NNet has the ability to generalize pattern rules from training instances in terms of bounds on both unary (single part) and binary (part relation) numerical features. It also learns the compatibilities between unary and binary feature states in defining different pattern classes. Rulegraphs check this compatibility between unary and binary rules by combining evidence theory with gr..
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