## 5 Results and Conclusions  
A calibration process in order to obtain a homogenous illumination for monochrome cycloramas has been developed. This workflow uses HDR images and a custom tool called CYIA that, using a new interpretation of the [PERSON]'s median cut algorithm for color image quantization results, makes it possible for a non-expert user to check the homogeneity of a cyclorama and detect the illumination artifacts that could affect the quality of the chroma-keying process. The importance of the homogeneity of the cyclorama illumination in the chroma-keying process and the improvements that can be achieved in the quality of the resulting composite images are shown in Figure 14.  
Several tests have been performed using 64, 128, 256, 512 and 1024 regions subdivisions in order to detect possible differences in the calibration process results both from a statistical and visual point of view. Statistically, there is a common tendency in the relation between the standard deviation  
Figure 14: Composite image using a proper cyclorama calibration. Note the defined edges of the actor’s green jersey and the absence of artifacts on the composition (**a**). Composite image with a non-homogeneous background lighting (**b**). Note the transparencies in the green jersey, the blurred edges and the halos appearing in the neck of the actor. Modifying the chroma-keying threshold in order to reduce the undesired transparencies increases the halos and makes different artifacts appear in the background due to the illumination differences in the cyclorama. of the region's areas and the size of the ideal regions. The percentage of the ideal area represented by the standard deviation tends to increase slightly with the growth of the number of subdivisions. This is due to the decrease in the size of the regions and it is proportional in all the tests, with all the graphs showing very similar slopes (Figures 10 and 13). As this increase is similar in all cases, it can be obviated, as the data from two different configurations should be compared using the same number of subdivisions in order to get solid conclusions. From the visual point of view the impression is that the information tends to be clearer as the number of regions increases. However, when the regions tend to be too small, the size differences tend to be less noticeable. In the range we used, the 512 subdivisions case tends to be the clearer configuration for the visual interpretation of the results, as there are enough regions to easily detect accumulations, but their size is big enough to clearly see the size difference between the over lighted regions and the shadowed ones. The nearness correspondences between the ideal centroids do not show a direct relation between the number of regions and the percentage of centroids with more than one correspondence, showing that, from the statistical point of view, this approach is only useful when more obvious light artifacts are present in the cyclorama illumination. However, the two types of images used in the tests proved to be very useful to visually detect lighting artifacts. The subdivided image is very precise when dealing with big artifacts in which the illumination differences create big accumulations of small regions in the over lighted areas of the cyclorama. On the other hand, the image showing the closer centroids correspondence eases the detection of more global variations, as in the shadowed areas many centroids will not have a correspondent while in the over lighted regions each of them will have several. This makes it fast and easy to interpret the images and design the needed changes in the illumination configuration. The distance between the centroids and the geometric center of the regions showed to be too small to be detected visually, as it represents, in average, less than 6% of the side of the ideal region in the test cases with bigger artifacts.