GRAPH MATCHING APPLIED FOR TEXTURED PATTERN RECOGNITION

This paper addresses the detection of noisy structures in the context of infrared microscopy using labeled undirected graph matching. The selection of robust features as labels is determinant in this case of study, where hard conditions are dealt with: few relevant topological information, a potentially huge number of outliers and low contrasted images resulting in noisy graphs. Firstly, the image texture is reliably caught through a scale, rotation and intensity invariant histogram of oriented gradients. Secondly, in structures presenting numerous symmetries, the graph shape is locally registered while discriminated using weight from a super-increasing series. Coupled to the flexibility of the graph tensor product-based similarity metric, the matching framework achieves satisfying results.