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This work describes a new method for the temporal segmentation of human actions based on a 2D inter-frame similarity plot. This similarity matrix contains relevant information for the analysis of cyclic and symmetric human activities, where the motion performed during the first semi-cycle is repeated in the opposite direction during the second semi-cycle. Thus, the pattern associated to aperiodic activity in the similarity matrix is rectangular and decomposable into elementary units. We propose a morphology-based approach for the detection and analysis of activity patterns. Pattern extraction is further used for the detection of the temporal boundaries of the cyclic symmetric activities. Result evaluation approach is based on a statistical estimation of the ground truth segmentation and on a confidence ratio for temporal segmentations. Research reported in This work was supported by a discovery grant of the National Sciences and Engineering Research Council of Canada.