We explore the performance of two algorithms to screen loci of equal activity in dynamic speckle images. Dynamic speckle images are currently applied to several applications in medicine, biology, agriculture and other disciplines. Nevertheless, no objective standard has been proposed so far to evaluate the performance of the algorithms, which must be then relied on subjective appreciations. We use two case studies of activity that do not bear the biologic inherent variability to test the methods: “Generalized Differences” and “Fujii”, looking for a standard to evaluate their performance in an objective way. As study cases, we use the drying of paint on an (assumed) unknown topography, namely the surface of a coin, and the activity due to pre heating a piece of paper that hides writings in the surface under the paper. A known object of simple topography is included in the image, besides of the painted coin, consisting in a paint pool where the depth is a linear function of its position. Both algorithms are applied to the images and the intensity profile of the results along the linear region of the pool activity is used to estimate the depth of the geometric topography hidden under paint in the coin. The accuracy of the result is used as a merit estimation of the corresponding algorithm. In the other experiment, a hidden dark bar printed on paper is covered with one or two paper leaves, slightly pre heated with a lamp and activity images registered and processed with both algorithms. The intensity profile of the activity images is used to estimate which method gives a better description of the bar edges images and their deterioration. Experimental results are shown.