Rulegraphs for Pattern Recognition

Rulegraphs for graph matching in pattern recognition

In Pattern Recognition, the Graph Matching problem involves the matching of a sample graph with the subgraph of a larger model graph where vertices and edges correspond to pattern parts and their relations. In this paper, we present Rulegraphs, a new method that combines the Graph Matching approach with Rule-Based approaches from Machine Learning. They reduce the cardinality of the (NP-Complete...

Learning Relational Structures for 2D Pattern and 3D Object Recognition

We present and compare three new techniques for Learning Relational Structures (RS) as they occur in 2D pattern and 3D object recognition. The three techniques, Evidence-Based Systems (EBS), Conditional Rule Generation (CRG) and Rulegraphs (RG) combine techniques from Computer Vision with those from Machine Learning, Uncertainty Theory, and Graph Matching. EBS have the ability to generalize pat...

School of Computing Relational Evidence Theory and Spatial Interpretation Procedures

i Synopsis Spatial interpretation involves the intelligent processing of images for learning, planning and visualisation. This involves building systems which learn to recognise patterns from the content of unconstrained data such as handwritten schematic symbols, photographic , and video images. The eeciency of spatial interpretation systems is measured not only in terms of their ability to le...

Local gradient pattern - A novel feature representation for facial expression recognition

Many researchers adopt Local Binary Pattern for pattern analysis. However, the long histogram created by Local Binary Pattern is not suitable for large-scale facial database. This paper presents a simple facial pattern descriptor for facial expression recognition. Local pattern is computed based on local gradient flow from one side to another side through the center pixel in a 3x3 pixels region...

Recent Advances in EMG Pattern Recognition for Prosthetic Control