**3. Methods**

*Advanced Methods and New Materials for Cultural Heritage Preservation*

"Area" Pixel Scalar representing the actual number of pixels in the region "Centroid" Pixel Vector that specifies the center of mass of the region. The first

(or y-coordinate) "Eccentricity" — Scalar that specifies the eccentricity of the ellipse that has the

"Orientation" Degrees Scalar representing the angle (in ranging from −90 to 90 degrees)

"Perimeter" Pixel Scalar measuring the distance around the boundary of the region.

"Extent" — Scalar that specifies the ratio of pixels in the region to pixels in the

the bounding box "ConvexHull" Pixel Matrix that specifies the smallest convex polygon that can

"EquivDiameter" Pixel Scalar that specifies the diameter of a circle with the same area as

"MajorAxisLength" Pixel Scalar specifying the length of the major axis of the ellipse that

"MinorAxisLength" Pixel Scalar: the length of the minor axis of the ellipse that has the same

of the shape, and these must be known as a priority for the original. Usually, the comparison of artifacts is only at the level of minutiae with the establishment of decision strategies for validating or invalidating the match. The method for the minutiae-based recognition is devoted to biometric technologies, fingerprint recognition, face recognition, scar and tattoo recognition, etc., and is rigorously

Currently, the issue of automatic shape recognition has evolved in the field of artificial intelligence under the generic "machine learning" domain from statistical models toward the "deep learning" paradigm, proving spectacular performance especially in video analytics technology. These models are based on convolutional artificial neural networks that require massive pattern learning [12]. Their performance depends on the increased number of training patterns, while the uniqueness is the characteristic of the artifacts. An automatic pattern recognition system could be trained very easily to recognize a particular artwork of Monet among those of Cézanne, Renoir, or Degas, but he will not be able to learn to distinguish Monet de Monet or Monet from a fake Monet. Definitely, this remains the attribute of the human expert, assisted, of course, by advanced

"BoundingBox" — The smallest rectangle containing the region

same second moments as the region

element is the horizontal coordinate (or x-coordinate) of the center of mass, and the second element is the vertical coordinate

same second moments as the region. The eccentricity is the ratio of the distance between the foci of the ellipse and its major axis length. The value is between 0 and 1 (0 and 1 are degenerate cases; an ellipse whose eccentricity is 0 is actually a circle, while

between the x-axis and the major axis of the ellipse that has the

It computes the perimeter by calculating the distance between each adjoining pair of pixels around the border of the region

total bounding box. Computed as the area divided by the area of

contain the region. Each row of the matrix contains the x- and

has the same normalized second central moments as the region

y-coordinates of one vertex of the polygon

the region. Computed as sqrt(4\*Area/pi)

normalized second central moments as the region

an ellipse whose eccentricity is 1 is a line segment)

**Property Unit Measure definition**

**36**

**Table 2.**

*Measure definitions of shape properties.*

information processing tools.

standardized [11].
