Anxiety is a widely studied psychiatric disorder, and is thought to be a complex and multidimensional phenomenon. Sensitive behavioral discrimination of animal models of anxiety is crucial for the elucidation of the behavioral components of anxiety and the physiological processes that mediate them. Commonly used behavior paradigms of anxiety usually include only a few automatically collected measures, which do not exhaust the behavioral richness exhibited by animals, and therefore might miss important differences between preparations. The aim of the present work was to expand the repertoire of automatically collected measures in a classical test of anxiety - behavior in relation to the wall in the Open Field. We present an algorithm, based on the SEE (Software for the Exploration of Exploration) strategy, which automatically partitions the mouse path into intrinsically defined patterns of movement near the wall and in the center. These patterns are used to design new endpoints, which provide an articulated description of various aspects of behavior near the wall and in the center. 16 new endpoints were designed using data from C57BL/6J and DBA/2J mice tested in 3 laboratories. The strain differences in all endpoints were evaluated on another data set in order to assess their validity, and were found to remain stable. 10 out of the 16 endpoints were found to discriminate between the two strains in a replicable manner. The entire set of endpoints can be used on various genetic and pharmacological models of anxiety with good prospects of providing fine discrimination in a replicable manner.