**3.1 Color software analysis**

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.

### **3.2 Structural analysis**

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 having values in the same range.


#### **Table 3.** *Comparative aspects of the methods used.*
