This protocol offers new opportunities involving the analysis of paintings of faces. It supports the users step by step along the whole data analysis process. This protocol has two main advantages.
The users can tailor the analysis according to their preferences, and for the first time, spectrum manipulation has been introduced to analyze the hyperspectral data. Hyperspectral reflectance imaging is successfully used to study skin disease or tumor diagnosis. Even if a protocol was born in the counter heritage field it can be applied to clinical health dataset as well.
With the support of art experts, perform a preliminary inspection of the painted surface to identify the main features of the painting. Make a note of the pictorial techniques used by the artist, the different brush strokes of paint on the canvas, and qualitatively estimate the characteristics of the brush strokes with particular attention to their size. Create ad hoc samples where the brush strokes show characteristics similar to those applied by the artist by mimicking the pictorial technique used by the artist.
Acquire the hyperspectral data and check whether the spatial resolution of the hypercubes can distinguish the different brushstrokes on the RGB images of the painted surface. Run PointSel, the isolated measuring point selection code to manually select some reference spectra on the surfaces of the test samples. Type the command line including the semicolon in the terminal window and press enter to run the code.
Select the measurement points by clicking the interactive window that one by one shows the two dimensional RGB images of the fields of view. Run SAM_Standard, the standard SAM maps evaluation code to extract the SAM maps using the whole spectra. Type the command line, including semicolon in the terminal window, then press enter to run the code.
SAM maps are saved as PNG images in the current work folder. Check whether the obtain similarity maps display the details of the brush strokes used to realize the test samples. If not, restart the process by readjusting the distance between the test sample surface and the acquisition equipment.
According to the evaluation obtained by the test samples set the distance between the surface of the painting under investigation and the acquisition equipment. Perform IO of the hyperspectral data by organizing, reading and managing the hypercubes. Run the HS FileLister code to store the list of the files containing the hypercubes and the related information into two variables at the disposal of the algorithm.
Run the HS_Crop PNG code to select the portion of each FOV to be used in the analysis of the data. Next, run the PointSel code and click within the displayed interactive window to identify the reference spectra as isolated measuring points over the surface of the monitored areas. Type the command line including the semicolon in the terminal window and press enter to run ReticularSel, the Reticular selection code.
This automatically selects the reference spectra as a regular reticulum of measuring points superimposed to the surface of the monitored areas. This selection method makes the analysis very time consuming as the number of references is large. Type the command line including the semicolon in the terminal window and press enter to run SaveImPoint.
This saves a location at the selected measurement point superimposed to the pictures of the fields of view. Run Spectra_Importer, the external references importer code to create a variable containing references from data sets and databases independent from the hypercubes acquired on quarto stato. Note that the spectra have different size with respect to those obtained with the hyperspectral camera.
Run the SAM complete code to evaluate the similarity maps. Feed the code with the desired pre-processing option entering zero or one in the dialogue box. Zero to require the spectra normalization only or one to require that after the normalization the spectra are derived one time.
enter this sequence of numbers corresponding to the desired columns of the references matrix into the dialogue box by typing the numbers separated by a white space. Press Enter to continue. Set the method to zero for no manipulation of the data.
One to require manual selection of the wavelength ranges of the spectra to be considered before starting the analysis or two to require the algorithm to order the data based on a specific criterion and before the evaluation of the SAM maps. To select the end members for the SAM analysis, the algorithm either retrieves the references spectra among the hypercubes by manually selecting some isolated measuring points or automatically samples the painting surface, providing a particular selection of measuring points within one or more FOVs. The algorithm can also compare the hypercubes with external spectra like the ones obtained by a portable FORS miniature spectrometer.
When the pre-processed references appear on an interactive window, one or more wavelength intervals to be analyzed can be manually selected. In automatic selection, the algorithm computes a maximum variance within the desired references and orders spectra according to this criterion. If the maximum variance corresponds to the nth wavelength the content of the nth component of each pre-processed spectrum will be moved to the first position of a rearranged hypervector and so on.
Following the automatic manipulation, the algorithm applies a floating threshold to the variance values and evaluates the SAM maps at the increasing threshold which results in a total of two n plus one sets of maps where n is a number of values assumed by the threshold. The obtained similarity maps provide new insights into the details of the mapped area. They can help to compare the samples and the references.
The possibility to customize the analysis and to exploit any spectrum as reference, expands the user horizons but simultaneously asks the user for a careful evaluation of their choices This approach allows the use of spectrum manipulation as an analysis tool, therefore computer vision and statistical studies can help deepen the knowledge about the possibility of this matter.
