One of the most important processes in the human visual system involves detecting and understanding edges. Edges allow humans to break a visual scene up into meaningful chunks of information. Without edges, a visual scene is meaningless. As important as edges are to human visual perception, how they are detected and classified is not well understood. This study provides evidence that humans are able to classify edges into appropriate categories when enough visual information is presented but objects in the scene are not detectable. In addition, this study shows that regions of interest (RO Is) of a particular edge type can be clustered according to similarities in structure using a simple algorithm. This study examines the relationship between image features (i.e. closure, texture & repetition) and the type or cause of an edge (i.e. albedo, depth, shadow & specular) in natural visual scenes. Two groups of human subjects were used to carry out the current study; the cause estimators (CEs) and the feature experts (FEs). The CEs were asked to state the cause of an edge presented in a ROI. The FEs were asked to label specific features for the same set of RO Is as the CEs. The first analysis describes the relationship between image features and the actual cause of the edge in the ROis presented. The second analysis describes the relationship between image features and the cause estimation provided by the CEs. This study provides evidence that closure, texture and repetition may help to inform human observers as to the cause of an edge when limited but sufficient visual information is available.