**2. Fundamentals**

#### **2.1 The basics of colorimetry**

Color is a perception of the surrounding world through our eyes. It is the most important graphic attribute of the images. Color is a notion that is defined from several perspectives as follows [1]:


Objects that make up an image can be achromatic (no color, i.e., invisible) or chromatic (colored). For example, the white light is achromatic, and white, black, and gray are neutral colors also considered achromatic. The chromatic colors are those that reflect the nonselective sunlight or artificial light, that is, it reflects equally all the lengths of electromagnetic waves visible to the human eye. In this category are the white, black, and all the hues between them (shades of gray).

**31**

*Intelligent Image Processing and Optical Means for Archeological Artifacts Examination*

These last mentioned colors are distinguished by one characteristic feature: bright-

Color properties are defined in relation to human visual perceptual capacity and sensory psychic mechanisms. As components of light, colors have three basic

a.*Brightness or luminance*—represents the degree of intensity or amount of radiation energy reflected by a particular color. From the physical point of view, this property is determined by the amplitude of the light wave. Thus, bright colors reflect more light than dark ones. The brightest color is white, and the least bright is black. Generally, the colors at the edge of the visual spectrum (blue, purple) have a lower brightness than those at the center (yellow). A chromatic

b.*Chromatic tonality* is the attribute that refers to the qualitative perceptual scale of a color. Physically, it is given by the predominant wavelength of the light that stimulates the visual analyzer. Thus, chromatic tone refers to the colors red, yellow, green, and blue, leading to their particular attributes like bluish-green, bluish-greenish, white-yellowish, etc., also called chromatic tones or hues. From a physiological point of view, the human normal eye discriminates between 2 and 5 nm of the wavelength of light radiation, thus being able to perceive numerous chromatic tones or color hues [2]. A classic experimental study reported since 1923 by Laurens and Hamilton quoted in [3] reveals a nonlinear distribution of wavelength discrimination between 0.25 nm and 7 nm across the visible spectrum range. According to [2], for instance, on the wavelength range of 760 nm (dark red) and 390 nm (violet), between 130 and 200 chromatic tones can be normally distinguished. These colors form color families arranged around the components of the chromatic spectrum, as follows: red has 57 distinct hues, orange 12, yellow 24, green 12, blue 29, and violet 16. In total, they make up to 150 perceptible shades; this number being also referenced by [4].

c.*Saturation* is the purity or degree of blending of a color with white (total blending wavelengths in the visible spectrum), which gives the color to be more concentrated or pale (saturated). The color saturation is evaluated on a conventional scale of the distances at which a particular chromatic color is given relative to that achromatic white. From a physical point of view, saturation of colors depends on the uniformity of wavelengths perceived concurrently. A theoretically pure color is one determined by a single wavelength, the more we perceive more wavelengths while the color feeling is pale, less pure. If we perceive all the wavelengths concurrently, then we see the white. Due to the saturation property, the colors are classified as "strong" or "poor," "heavy" or "light," "bright" or "dead," and "colorful" or "sad." The saturation level affects the perceived chromatic hue. It is appreciated that by combining different degrees of saturation and roughly 200 chromatic tones, around 1700 chromatic hues can be obtained [2]. Based on physiological evidence and experimental psychology, the capabilities of the human visual analyzer to perceive the colors were estimated by different authors between 100,000 and 10 million distinctive colors [4]. These data tell us how performant should be a digital optical

color is even brighter the farther away from the black.

equipment to manipulate hues like humanlike.

*DOI: http://dx.doi.org/10.5772/intechopen.80615*

ness (illuminance).

*2.1.1 Color properties*

features as follows.

*Intelligent Image Processing and Optical Means for Archeological Artifacts Examination DOI: http://dx.doi.org/10.5772/intechopen.80615*

These last mentioned colors are distinguished by one characteristic feature: brightness (illuminance).

### *2.1.1 Color properties*

*Advanced Methods and New Materials for Cultural Heritage Preservation*

structural-chromatic set of features for an artifact at a certain time.

object (the other components being absorbed).

artifacts may deteriorate due to exposure to light.

**2. Fundamentals**

**2.1 The basics of colorimetry**

several perspectives as follows [1]:

the visible spectrum.

visible surface, while the color of each point is determined by the punctual interaction of the object with the optical radiation in the external environment. Between these two basic features of objects, there is a univocal relationship, meaning that the shape of objects can influence the perceived color, but not vice versa. The shape and color of objects can be assessed generally by geometric measurements or photometric techniques. Experimental methods are most effective in obtaining data to the extent that they do not affect the artifact. Optical scanning is a passive experimental technique, considered noninvasive, excepting the particular situations where

Based on the optical methods, quantitative and qualitative analyses can be made on the shape and chromaticity of artifacts of any kind such as distinct archeological pieces, stamps, paintings, and other forms of decorative art like mosaics, engravings, embroideries, and artistic upholstery. Image analysis models include special mathematical functions for calculating conventional measures to characterize shapes and parameters for color evaluation. Digital image processing is widely used in everyday life with many applications in the industry, health, transport, telecommunications, social security, and military. This chapter discusses the applicative features of digital image processing in the field of artistic and historical heritage protection by proposing complementary image-based analysis techniques for a better investigation of artifacts. The concepts discussed are supported by some applicative examples. The scientific purpose of this methodology is to obtain a relevant

Color is a perception of the surrounding world through our eyes. It is the most important graphic attribute of the images. Color is a notion that is defined from

i.*Physically*: the color represents electromagnetic radiation in the optical (visible) spectrum between 375 and 760 nm, which are normally selective stimuli for retinal cones. The color of an object is given by the radiation components of the visible spectrum that are reflected by the surface of the

ii.*From a psychophysical point of view*: color is a characteristic of light that

nance or brightness, chromaticity or hue, and saturation or purity.

Objects that make up an image can be achromatic (no color, i.e., invisible) or chromatic (colored). For example, the white light is achromatic, and white, black, and gray are neutral colors also considered achromatic. The chromatic colors are those that reflect the nonselective sunlight or artificial light, that is, it reflects equally all the lengths of electromagnetic waves visible to the human eye. In this category are the white, black, and all the hues between them (shades of gray).

allows two fields of the same shape, size, and structure to be distinguished in

iii.*From the psychosensory point of view*: regardless of the stimulus used, any light sensation is characterized by certain properties or chromatic factors: illumi-

**30**

Color properties are defined in relation to human visual perceptual capacity and sensory psychic mechanisms. As components of light, colors have three basic features as follows.


#### *2.1.2 Primary systems for color representation*

Traditional color classification refers to how to obtain them. As is well known, colors are divided into the following categories:


The above description, although conventional, is applicable to the artistic combination of colors, a technique well-known by painters for a long time. In practice, the emergence and development of photographic techniques and subsequently electronic means for processing and displaying images, the color manipulation process required the emergence of specific patterns for representing, capturing and transmitting image information. As chromatic perception is an expression of the reflection phenomenon of light, which is dependent on ambient illumination and the contribution of additional light sources, color representation patterns are found as formal color calculation tools.

The International Commission of Illumination (CIE), a body established in 1913, which through its division for Vision and Color coordinates the development of standards in modern colorimetry, by the recommendation of 1931 defined the so-called standard colorimetric observer proposing two systems primary equivalent color representation [5]. In principle, these are three-stimuli color spaces based on the Maxwell's trichromatic theory [6] in accordance with spectral sensitivities of the human eye.

The *first conventional system* proposed by CIE for color representation is RGB (Red Green Blue) in which color components are wavelength functions as follows: R(λ) for λ = 700 nm, G(λ) for λ = 546.1 nm, respectively, B(λ) for λ = 435.8 nm. The representation of the RGB color space with its combinations is shown in **Figure 1**.

**33**

**Figure 2.**

*HSV color space in cylindrical coordinates.*

*Intelligent Image Processing and Optical Means for Archeological Artifacts Examination*

tions of RGB system components expressed in a matrix form as follows [7]:

The *second system* defined by CIE is XYZ whose functions are linear transforma-

0.490 0.310 0.020 0.177 0.813 0.011

*Derivative representation systems* have emerged as a result of the diversification of the technical means of capturing and processing images that required the use of other rules for representing basic components. Thus, color television systems were imposed by NTSC and PAL standards by transforming the primary RGB space into three specific terms: luminance and two chrominance components. For example,

0.300 0.590 0.110 −0.148 −0.291 0.483

Another derivative color space was developed by Kodak under the name of PhotoYCC and consists in transforming the RGB reference space into the Luminance—Chroma—Chroma (YCC) components in three steps: a gamma correction, a linear transformation described in matrix, and a binary quantization on 8

*Perceptual representation systems* attempt to describe how human observers feel and express colors. Starting from empirical analyzes, it has been observed that people notice very well the properties of colors: brightness, hue and saturation. Perceptual representation systems attempt to approximate, through mathematical formulas, the psycho-physical effect of the three chromatic properties. Basic perceptual system HSV (Hue Saturation Luminance), also known in different versions as HVC (Hue, Value, Chroma), HSI (Hue, Saturation, Intensity) or HSL (Hue Saturation Luminance) is a more complex nonlinear transformation concretized by a rotation of the RGB chromatic space followed by a cylindrical or spherical coordinate transformation. There are several formulas for evaluating the three components of the HSV system proposed by different authors [8]. The generic representation of the HSV perceptual color space in cylindrical coordinates is shown in **Figure 2**. Conventionally, the numerical range for HSV components is the range [0, 1]. The hexagon has sides equal to 1 and is located at elevation V = 1. The conventional position of the white component is at the center of the hexagon, where

0.526 −0.518 0.096)(

<sup>0</sup> 0.010 0.990)(

*R G*

> *R G*

*<sup>B</sup>*) (1)

*<sup>B</sup>*) (2)

*DOI: http://dx.doi.org/10.5772/intechopen.80615*

*X Y <sup>Z</sup>*) <sup>=</sup> (

*Y U <sup>V</sup>*) <sup>=</sup> (

bit. This system is used to store images on PhotoCDs.

for the PAL standard matrix transformation is as follows [7]:

(

(

*Conventional representation of the primary RGB space and its chromatic derivatives.*

*Intelligent Image Processing and Optical Means for Archeological Artifacts Examination DOI: http://dx.doi.org/10.5772/intechopen.80615*

The *second system* defined by CIE is XYZ whose functions are linear transformations of RGB system components expressed in a matrix form as follows [7]:

is or  $\mathbf{RGB}$  system components expressed in a matrix form as follows (1):

$$
\begin{pmatrix} X \\ Y \\ Z \end{pmatrix} = \begin{pmatrix} 0.490 & 0.310 & 0.020 \\ 0.177 & 0.813 & 0.011 \\ 0 & 0.010 & 0.990 \end{pmatrix} \begin{pmatrix} R \\ G \\ B \end{pmatrix} \tag{1}
$$

*Derivative representation systems* have emerged as a result of the diversification of the technical means of capturing and processing images that required the use of other rules for representing basic components. Thus, color television systems were imposed by NTSC and PAL standards by transforming the primary RGB space into three specific terms: luminance and two chrominance components. For example, for the PAL standard matrix transformation is as follows [7]:

## 5.2.3.1. \*\*c.\*\*: \_uamanance\_ and two \_dmonmannance\_. \_Ior\_. \_Camp\_, the PAL standard matrix transformation is as follows [7]: 
$$\begin{pmatrix} Y \\ U \\ V \end{pmatrix} = \begin{pmatrix} 0.300 & 0.590 & 0.110 \\ -0.148 & -0.291 & 0.483 \\ 0.526 & -0.518 & 0.096 \end{pmatrix} \begin{pmatrix} R \\ G \\ B \end{pmatrix} \tag{2}$$

Another derivative color space was developed by Kodak under the name of PhotoYCC and consists in transforming the RGB reference space into the Luminance—Chroma—Chroma (YCC) components in three steps: a gamma correction, a linear transformation described in matrix, and a binary quantization on 8 bit. This system is used to store images on PhotoCDs.

*Perceptual representation systems* attempt to describe how human observers feel and express colors. Starting from empirical analyzes, it has been observed that people notice very well the properties of colors: brightness, hue and saturation. Perceptual representation systems attempt to approximate, through mathematical formulas, the psycho-physical effect of the three chromatic properties. Basic perceptual system HSV (Hue Saturation Luminance), also known in different versions as HVC (Hue, Value, Chroma), HSI (Hue, Saturation, Intensity) or HSL (Hue Saturation Luminance) is a more complex nonlinear transformation concretized by a rotation of the RGB chromatic space followed by a cylindrical or spherical coordinate transformation. There are several formulas for evaluating the three components of the HSV system proposed by different authors [8]. The generic representation of the HSV perceptual color space in cylindrical coordinates is shown in **Figure 2**. Conventionally, the numerical range for HSV components is the range [0, 1]. The hexagon has sides equal to 1 and is located at elevation V = 1. The conventional position of the white component is at the center of the hexagon, where

**Figure 2.** *HSV color space in cylindrical coordinates.*

*Advanced Methods and New Materials for Cultural Heritage Preservation*

Traditional color classification refers to how to obtain them. As is well known,

• *Basic colors* (also called primary or fundamental colors) are those that by mixing them can be obtained all the other colors. These are red, yellow, and blue (RYB). The chromatic pattern to represent images on electronic systems is red,

• *Composite colors* are those that result from the mixture of base colors two by two. There are composed colors of degree I, colors composed of grade II (which results from the mixture of those of first degree, two by two), and so on.

• *Complementary colors* are those that mixed in appropriate proportions (which

The above description, although conventional, is applicable to the artistic combination of colors, a technique well-known by painters for a long time. In practice, the emergence and development of photographic techniques and subsequently electronic means for processing and displaying images, the color manipulation process required the emergence of specific patterns for representing, capturing and transmitting image information. As chromatic perception is an expression of the reflection phenomenon of light, which is dependent on ambient illumination and the contribution of additional light sources, color representation patterns are found

The International Commission of Illumination (CIE), a body established in 1913, which through its division for Vision and Color coordinates the development of standards in modern colorimetry, by the recommendation of 1931 defined the so-called standard colorimetric observer proposing two systems primary equivalent color representation [5]. In principle, these are three-stimuli color spaces based on the Maxwell's trichromatic theory [6] in accordance with spectral sensitivities of

The *first conventional system* proposed by CIE for color representation is RGB (Red Green Blue) in which color components are wavelength functions as follows: R(λ) for λ = 700 nm, G(λ) for λ = 546.1 nm, respectively, B(λ) for λ = 435.8 nm. The representation of the RGB color space with its combinations is

*Conventional representation of the primary RGB space and its chromatic derivatives.*

are found in the spectrum) give a neutral color (white or gray).

*2.1.2 Primary systems for color representation*

green, and blue (RGB).

as formal color calculation tools.

the human eye.

shown in **Figure 1**.

colors are divided into the following categories:

**32**

**Figure 1.**

it is intersected by the Value axis. Thus, the white is not characterized by hue (it is "immune" to the variable H!); it has obvious saturation S = 0, and the value is maximum V = 1. The pure colors have the saturation equal to 1. The hue H is an angular coordinate on 360° and is conventionally represented on the [0, 1] range, so that, each color of the hexagon peaks is at a distance of 1/6 on the definition interval: Red → 0, Yellow → 0.1666, Green → 0.3333, etc.

Alongside the CIE, the International Color Consortium (ICC) is a focus group that promotes new concepts and provides technical notes in the field of color standards and their representation with various techniques and electronic means [9].

### **2.2 About forms and their classification**

#### *2.2.1 Shape and geometry indicators*

Shape is the outer appearance of an object that does not take into account its size. The shape of objects is perceived by their edges or contours. In a geometric sense, the shape of objects is described by properties that allow a classification of objects according to their appearance. Depending on their form and geometry, objects in nature can fit into a variety of hierarchically organized classes. The hierarchy of shapes is generated by the type of primitive graphics that describe the outline of objects, and their properties: their number and relative position, similarity or congruence so that by customizations or generalizations, very complex forms can be characterized. **Table 1** presents taxonomy of forms that can be used to characterize artifacts.

#### *2.2.2 Measures of shape properties*

Shape analysis is based on the detection and labeling of distinct regions on the artifact image. Based on these regions, the objects presented in that image evaluating certain properties commonly called shape measurements are estimated. Software utilities for image analysis provide a broad set of measures that can be used to characterize distinct objects once they have been detected in an image. For example, MATLAB programming environment contains powerful toolboxes for video analysis and image processing. Some of the measures used by MATLAB [10] are summarized in **Table 2**.

#### *2.2.3 Shape recognition*

Shape recognition is a decision process that is accomplished by a simple direct comparison action of a sample object with the reference object or by a more complex process of classifying template sets and subsequent reporting of unknown objects to these class sets. In the current work of examining artifacts, it is mostly to acknowledge direct comparison with the original. Here are the typical situations of authentication and restoration-reconditioning of artifacts in which knowledge of exact information about the original is essential.

When looking at artifacts with an unknown author is the question of framing the artwork at a certain time or in an artistic current, then recognition based on classification becomes important.

Comparison techniques include two methods of testing the match between the current object and the original: global matching and mathematical matching (based on significant properties). Global matching is applied based on artifact

**35**

**Table 1.**

*Intelligent Image Processing and Optical Means for Archeological Artifacts Examination*

**Subclass defined by relative position of some elements (angles, for instance)**

Equilateral Equilateral Any —

Trapeze Isosceles Right trapeze — Pentagon Convex Regular/irregular

Nonconvex Regular/irregular

Convex Regular/irregular Nonconvex Regular/irregular

Opened ladders Regular ladder

Zigzag shape Regular

Triangle Right triangle Isosceles

Quadrilateral Parallelogram Square

Single segment Horizontal/vertical/inclined —

**Subclass defined by the similarity of the elements (ref. to sides)**

Any

Rectangle Diamond

Irregular ladder

Irregular

images by checking the superimposition of the composite in detail. Matching details involves image processing and extraction of properties that give the artifact uniqueness—called *minutiae*. The calculation of minutiae provides the measures

Semi-disc

*DOI: http://dx.doi.org/10.5772/intechopen.80615*

**Class Subclass** 

**defined by the number of elements**

Hexa/…octo/… decagon…

Connected segments

Circle/disc Circular crown Ellipse Ovoid Cardioids

Circle arc Ellipse arc Parabolic arc Hyperbolic arc Spiral Evolvent Cubic Sinusoid Cycloid

Circular crown sector Semi-crown

**Graphic primitive (base element)**

Line Polygons

(closed chain)

Open chain

curve

Open curve

sector

Ellipse sector

*Taxonomic hierarchy of 2D forms.*

Curve Closed

Combined Circle


*Intelligent Image Processing and Optical Means for Archeological Artifacts Examination DOI: http://dx.doi.org/10.5772/intechopen.80615*

#### **Table 1.**

*Advanced Methods and New Materials for Cultural Heritage Preservation*

→ 0, Yellow → 0.1666, Green → 0.3333, etc.

**2.2 About forms and their classification**

*2.2.1 Shape and geometry indicators*

*2.2.2 Measures of shape properties*

are summarized in **Table 2**.

exact information about the original is essential.

classification becomes important.

*2.2.3 Shape recognition*

means [9].

artifacts.

it is intersected by the Value axis. Thus, the white is not characterized by hue (it is "immune" to the variable H!); it has obvious saturation S = 0, and the value is maximum V = 1. The pure colors have the saturation equal to 1. The hue H is an angular coordinate on 360° and is conventionally represented on the [0, 1] range, so that, each color of the hexagon peaks is at a distance of 1/6 on the definition interval: Red

Alongside the CIE, the International Color Consortium (ICC) is a focus group that promotes new concepts and provides technical notes in the field of color standards and their representation with various techniques and electronic

Shape is the outer appearance of an object that does not take into account its size. The shape of objects is perceived by their edges or contours. In a geometric sense, the shape of objects is described by properties that allow a classification of objects according to their appearance. Depending on their form and geometry, objects in nature can fit into a variety of hierarchically organized classes. The hierarchy of shapes is generated by the type of primitive graphics that describe the outline of objects, and their properties: their number and relative position, similarity or congruence so that by customizations or generalizations, very complex forms can be characterized. **Table 1** presents taxonomy of forms that can be used to characterize

Shape analysis is based on the detection and labeling of distinct regions on the artifact image. Based on these regions, the objects presented in that image evaluating certain properties commonly called shape measurements are estimated. Software utilities for image analysis provide a broad set of measures that can be used to characterize distinct objects once they have been detected in an image. For example, MATLAB programming environment contains powerful toolboxes for video analysis and image processing. Some of the measures used by MATLAB [10]

Shape recognition is a decision process that is accomplished by a simple direct comparison action of a sample object with the reference object or by a more complex process of classifying template sets and subsequent reporting of unknown objects to these class sets. In the current work of examining artifacts, it is mostly to acknowledge direct comparison with the original. Here are the typical situations of authentication and restoration-reconditioning of artifacts in which knowledge of

When looking at artifacts with an unknown author is the question of framing the artwork at a certain time or in an artistic current, then recognition based on

Comparison techniques include two methods of testing the match between the current object and the original: global matching and mathematical matching (based on significant properties). Global matching is applied based on artifact

**34**

*Taxonomic hierarchy of 2D forms.*

images by checking the superimposition of the composite in detail. Matching details involves image processing and extraction of properties that give the artifact uniqueness—called *minutiae*. The calculation of minutiae provides the measures


#### **Table 2.**

*Measure definitions of shape properties.*

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 standardized [11].

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 information processing tools.

**37**

**Table 3.**

*Intelligent Image Processing and Optical Means for Archeological Artifacts Examination*

Automatic image processing tools are based on the interpretation of the pixel value that is designated by the primitive pixel graphics. The value of a pixel refers to its chromaticity and is measured by the so-called color index. An important problem is that the color index is a conventional measure that depends on the type of digital image: color, grayscale, or black-and-white. In general, real object images can be captured as color images in the RGB system and then converted to other image types by conversion or indexing. By indexing, color images require a lower amount of data due to the fact that the three RGB values are aggregated in one, but this involves numerical approximations. Thus, image indexing is done with the loss of original information about the value of the color components. Typically, grayscale and black-and-white conversions are used for image analysis, resulting in so-called binary images. These types of images can also be indexed, considering a gray-level reference threshold. For black-and-white images, there are two default indexing values: 0 and 1. Virtually, all digital image analysis methods apply to preliminary indexed images for which these methods have a degree of relativity and a conventional character. The morphological analysis of the objects, respectively, the forms and the composition of the shapes in a picture is also made on the basis of color, being affected by the weaknesses of this method. Investigating artifacts, however, requires a higher level of precision and the use of analytical tools to provide discriminators in accordance with human visual perceptiveness. We therefore show interest in an intelligent combination of using color-based digital image analysis techniques using both the RGB primary space and the HVS perceptual system.

The issue of decomposing an image into component objects based on regions (the so-called region-based segmentation) is not trivial because of the ambiguity and relativity of the criteria. The principle of region detection is based on the application of a connectivity criterion for pixels of the indexed or binary image of the studied artifact. However, the method is dependent on the result of the decision about the pixel value, that is, the intensity (level) of gray at the point considered. Therefore, the result of the analysis depends on the enlightenment of the artifact. Some issues specific to the two methods are presented in **Table 3**. Thus, the standard methodology for analyzing forms that make up the artifact's image includes choosing the illumination pattern, setting thresholds for the pixel value selection range, and applying the connectivity criterion based on an adjacent rule of pixels

**Color analysis Structural analysis** Principle Independent Based on chromatic differences to detect edges

Uses more complex algorithms based on filtering

The result is generally affected by uncertainty

spatial light distribution

Evaluation of the conventional

pixel value

Precision Accurate numerical evaluations in color space

*DOI: http://dx.doi.org/10.5772/intechopen.80615*

**3.1 Color software analysis**

**3.2 Structural analysis**

having values in the same range.

**Description Method**

*Comparative aspects of the methods used.*

Computing effort

**3. Methods**

*Intelligent Image Processing and Optical Means for Archeological Artifacts Examination DOI: http://dx.doi.org/10.5772/intechopen.80615*
