Weighted mean of a pair of graphs
Computing
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This paper studies the structural sensitivity of line-pattern recognition using shape-graphs. We compare the recognition performance for four different algorithms. Each algorithm uses a set of pair wise geometric attributes and a neighborhood graph to represent the structure of the line patterns. The first algorithm uses a pair-wise geometric histogram, the second uses a relational histogram on the edges of the shape graph, the third compares the set of attributes on the edges of the shape graph and the final algorithm compares the arrangement of line correspondences using graph-matching. The different algorithms are compared under line deletion, line addition, line fragmentation and line end-point measurement errors. It is the graph-matching algorithm which proves to be the most effective.