Area

During the individual module testing, the ‘gold standard’ images were repetitively tested in order to ensure the image processing could produce an identical result. This was to verify the algorithm functioned as designed on individual images. However, during this round of testing, the results from the series of images will be compared to the ‘gold standard’. Since each image will be slightly dissimilar, due to the fluctuation of light and noise, the values are likely to vary. Table 8 shows the results for the four scenarios tested.

Yellow Blue

Circle Square Rectangle Circle Square Rectangle

μ σ μ σ μ σ μ σ μ σ μ σ

Low Light 48 8 113 14 270 11 48 9 119 11 268 15

Ideal (Sequential) 57 5 120 8 300 2 55 4 130 4 280 2

Ideal (Random) 69 7 151 16 293 6 65 30 140 22 280 27

High Light 72 13 152 20 300 29 69 24 143 20 287 21

Table 8 - Results for the detected areas for each robot patch within 4 different lighting scenarios

As expected the averages and ranges differ depending on the light level used, with higher averages found in images with brighter light, and oppositely, low averages in images with low light levels. Yet, generally all ranges are relatively small and the averages for the shapes are distinct and do not encroach on each other. This ensures that each shape is represented with a unique area that is easier to distinguish during the robot recognition module.

Unsurprisingly, the series of images taken sequentially under ideal lighting conditions produced the lowest range, likely due to the relatively consistent lighting conditions. In contrast, the other three scenarios showed wider ranges, likely caused by the difference in environment. Interestingly, the average areas also scaled with the light levels. Since the ideal colour thresholds were recalibrated for each scenario, it was expected that the averages would stay relatively similar. Even though this was not the case, the ranges still manage to remain fairly constant.

94 With the difference in area detected among the different lighting levels, even under optimised thresholds, special care will be needed for processes requiring a specified area. Obviously, an automatic thresholding algorithm would be useful for keeping the ranges low, although doing so will not stop the averages from drifting between light levels. This could be fixed by

investigating different colour segmentation methods that are less susceptible to changes in light. However, since it does not appear that the difference in area has caused any serious limitations within the algorithm there is no need to redesign it for this case study. Although, this does highlight an area for further research in order to improve the reliability and functionality of the robot soccer algorithm.

3.19.2. Centre of Gravity

It is possible that the variations in the area could adversely affect the calculated centre of gravity for each robot. Large ranges in the coordinates would obviously call into question the accuracy for the positions of the robots, as well as potentially causing problems with the association module. Table 9 and Table 10 show the results for the centre of gravity for the X and Y coordinates respectively within the frame.

Yellow Blue

Circle Square Rectangle Circle Square Rectangle

μ σ μ σ μ σ μ σ μ σ μ σ

Low Light _{563 2 419 0 385 0 117 2 272 1 316 0} Ideal (Sequential) 562 1 420 1 385 1 117 0 272 0 316 0

Ideal (Random) 562 2 420 0 384 0 117 1 272 0 316 0

High Light 562 1 420 1 384 0 117 1 273 1 316 0

Table 9 - Centre of gravity results for X coordinates

Yellow Blue

Circle Square Rectangle Circle Square Rectangle

μ σ μ σ μ σ μ σ μ σ μ σ

Low Light 236 1 353 0 234 1 236 2 109 1 235 0

Ideal (Sequential) 236 2 353 1 234 0 236 0 110 0 235 1

Ideal (Random) 237 0 353 1 234 0 236 2 110 0 236 1

High Light 236 1 354 0 234 0 237 1 110 0 236 0

Table 10 - Centre of gravity results for Y coordinates

From these results, it is clear that the difference in the areas does not overly affect the accuracy of the positioning coordinates. In a worst-case scenario, the robot coordinates seem to be accurate to within 2 pixels, which equates to roughly 6mm in physical dimensions. Furthermore, the calculated averages are all within 1 pixel of each other. What is most surprising is that the values are so similar across all of the different lighting conditions and time intervals, despite the difference in area and noise level within the image. This suggests that the calculated positions are rather robust, and even though the colour segmentation is somewhat susceptible to light changes this doesn’t greatly affect the calculated positions.

95 An observation can be made that the circles show the greatest range compared to the other shapes. Circles have the smallest area and are therefore slightly more sensitive to noise. This is amplified by their positions on the board. Each circle is placed in the far edges of the field where the light levels are lower and the lens distortion is greater. Based on the area

differences, and the known colour segmentation problems with components around the edges of the field, this larger range is not surprising.

The question that stands out however is how such accurate positions are able to be extracted based on the widely varying areas. The most logical assumption is that the difference in area occurs equally around the edges of the shape. By doing so, the value of the area will change but the centre will still remain the same. This holds true to the observations made while manually tuning the colour thresholds. When the thresholds are incorrect the edges are the first to degrade, while the centre of the object remains relatively intact. This is a good sign for the resilience of the algorithm as a whole, as a number of the object processing stages are reliant on the accuracy of the extracted centre of gravities for each component.

The main limitation with these results is there is no way of verifying how close the averages within the frame are to the physical coordinates, and therefore how accurate the averages are. The coordinates calculated are with respect to the image frame. However, since the

conversion to physical coordinates is outside of the scope of this case study, knowing the accuracy of the coordinates within the frame is more important.