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A fully integrated system for the automatic detection and characterization of cracks in road flexible pavement surfaces, which does not require manually labeled samples, is proposed to minimize the human subjectivity resulting from traditional visual surveys. The first task addressed, i.e., crack detection, is based on a learning from samples paradigm, where a subset of the available image database is automatically selected and used for unsupervised training of the system. The system classifies nonoverlapping image blocks as either containing crack pixels or not. The second task deals with crack type characterization, for which another classification system is constructed, to characterize the detected cracks' connect components. Cracks are labeled according to the types defined in the Portuguese Distress Catalog, with each different crack present in a given image receiving the appropriate label. Moreover, a novel methodology for the assignment of crack severity levels is introduced, computing an estimate for the width of each detected crack. Experimental crack detection and characterization results are presented based on images captured during a visual road pavement surface survey over Portuguese roads, with promising results. This is shown by the quantitative evaluation methodology introduced for the evaluation of this type of system, including a comparison with human experts' manual labeling results.