In this paper, a general methodology is introduced for the determination of potential prototype curves used for the drawing of prehistoric wall paintings. The approach includes 1) preprocessing of the wall-paintings contours to properly partition them, according to their curvature, 2) choice of prototype curves families, 3) analysis and optimization in 4-manifold for a first estimation of the form of these prototypes, 4) clustering of the contour parts and the prototypes to determine a minimal number of potential guides, and 5) further optimization in 4-manifold, applied to each cluster separately, in order to determine the exact functional form of the potential guides, together with the corresponding drawn contour parts. The methodology introduced simultaneously deals with two problems: 1) the arbitrariness in data-points orientation and 2) the determination of one proper form for a prototype curve that optimally fits the corresponding contour data. Arbitrariness in orientation has been dealt with a novel curvature based error, while the proper forms of curve prototypes have been exhaustively determined by embedding curvature deformations of the prototypes into 4-manifolds. Application of this methodology to celebrated wall paintings excavated at Tyrins, Greece, and the Greek island of Thera manifests that it is highly probable that these wall paintings were drawn by means of geometric guides that correspond to linear spirals and hyperbolae. These geometric forms fit the drawings’ lines with an exceptionally low average error, less than 0.39 mm. Hence, the approach suggests the existence of accurate realizations of complicated geometric entities more than 1,000 years before their axiomatic formulation in the Classical Ages.

A novel methodology is introduced here that exploits 2D images of arbitrary elastic body deformation instances so as to quantify mechanoelastic characteristics that are deformation invariant. Determination of such characteristics allows for developing methods offering an image of the undeformed body. General assumptions about the mechanoelastic properties of the bodies are stated which lead to two different approaches for obtaining bodies' deformation invariants. One was developed to spot a deformed body's neutral line and its cross sections, while the other solves deformation PDEs by performing a set of equivalent image operations on the deformed body images. Both of these processes may furnish a body-undeformed version from its deformed image. This was confirmed by obtaining the undeformed shape of deformed parasites, cells (protozoa), fibers, and human lips. In addition, the method has been applied to the important problem of parasite automatic classification from their microscopic images. To achieve this, we first apply the previous method to straighten the highly deformed parasites, and then, apply a dedicated curve classification method to the straightened parasite contours. It is demonstrated that essentially different deformations of the same parasite give rise to practically the same undeformed shape, thus confirming the consistency of the introduced methodology. Finally, the developed pattern recognition method classifies the unwrapped parasites into six families, with an accuracy rate of 97.6 percent.

In this paper a new general methodology is introduced for the determination of the method of construction of prehistoric wall-paintings. The approach consists of algorithms that perform a) preprocessing of the contours of the figures appearing in the wall-paintings, b) determination of pattern repetitions in the contours of the depicted entities, c) classification of these repeated patterns into proper geometric prototypes, d) curve fitting. By means of this methodology, the authors demonstrated that a number of wall-paintings excavated at Mycenae and in Aegean islands, made before the 13th century B.C., were drawn via the use of guides. These stencils - guides were very precise geometric figures and in particular linear spirals and hyperbolae, thus indicating knowledge of constructing these complicated schemes more than 1000 years before the appearance of classical age mathematics.

A novel methodology is introduced here that exploits microscopic images of domestic animalspsila parasites in arbitrary deformation instances, so as to verify assumptions about their mechanoelastic properties. The obtained knowledge of these properties manifests parasite body characteristics that are deformation invariant, thus allowing for unwrapping them. Next, we have stated differential equations that govern the parasite body deformation and we have solved them by performing a set of equivalent image operations on the deformed body images. This process furnishes the parasite undeformed version from its deformed image. The method has been applied to a dataset of 193 microscopic images of highly deformed parasites. It is demonstrated that different orientations and deformations of the same parasite give rise to practically the same undeformed shape, thus confirming the consistency of the approach.