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This measure was explained and illustrated in the context of active appearance models where difference needs to guide model fitting. Its idea is primitive, nevertheless it is effective, especially when faced with the simplest class of tasks. Pixels are compared in two images one by one, their squared grey-level difference is calculated and a sum33 over all differences is returned. This method is usually powerful if the two images compared are closely aligned and their intensity values are relatively continuous and low in contrast. In other words, MSD will tolerate only a low level of locally-situated difference, while contrariwise, MI and NMI rely on sparse dispersion of all pixels.