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We propose a new Statistical Complexity Measure (SCM) to qualify edge mapswithout Ground Truth (GT) knowledge. The measure is the product of two indices,an \emph{Equilibrium} index $\mathcal{E}$ obtained by projecting the edge mapinto a family of edge patterns, and an \emph{Entropy} index $\mathcal{H}$,defined as a function of the Kolmogorov Smirnov (KS) statistic.  This new measure can be used for performance characterization which includes:(i)~the specific evaluation of an algorithm (intra-technique process) in orderto identify its best parameters, and (ii)~the comparison of differentalgorithms (inter-technique process) in order to classify them according totheir quality.  Results made over images of the South Florida and Berkeley databases showthat our approach significantly improves over Pratt's Figure of Merit (PFoM)which is the objective reference-based edge map evaluation standard, as ittakes into account more features in its evaluation.

Unsupervised edge map scoring: a statistical complexity approach