Posterior Predictive Check - Writing Detection

B.2 Writing Detection

B.2.3 Posterior Predictive Check

FigureB.5shows the posterior distributions for the model parameters and they are all unimodal (centre around a single value) suggesting that our chains have converged to the same posterior.

b_muPerfect_DiagramsYes b_muPerfect_visReduced sd_filename__muPartial_Interceptsd_filename__muPerfect_Intercept b_muPartial_DiagramsYes b_muPartial_visReduced b_muPerfect_LightingLow b_muPerfect_ProjectorTextYes

b_muPartial_Intercept b_muPerfect_Intercept b_muPartial_LightingLow b_muPartial_ProjectorTextYes

-0.8 -0.4 0.0 0.4 0.8 -0.25 0.00 0.25 0.50 0.75 3 4 5 3 4 5 6

0.0 0.5 1.0 1.5 2.0 2.5 -1.5 -1.2 -0.9 -0.6-15 -10 -5 0 0 5 10 15

-2 -1 0 -5 -4 -3 -2 -1 -8 -6 -4 2.5 3.0

Figure B.5.: Densities of all model parameters for writing detection.

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0 2500 5000 7500 10000

Failure Partial Perfect

Outcome

Count

y_rep

Posterior Predictive Check

Figure B.6.: Posterior predictive check plot showing the actual datayand the synthetic drawsyrepfor writing detection.

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Writing Detection Performance Plots by Environmental Factor

C

The plots below show the overall performance graphs of the writing detection module under various environmental conditions. The plots were generated by filtering all frames in the testing dataset by the presence and absence of each factor (diagrams, low lighting, projector text and reduced visibility).

In each figure, (a) and (c) shows the plot with varying area recall constrainttr while fixing tp= 0.4and (b) and (d) varying area precision constrainttpwhile fixingtr= 0.8. Each figure title indicates the condition represented and sub-plots (a) and (b) indicate the environmental factor being present while (c) and (d) indicates the absence of it.

The presence of diagrams (see Figure C.1) and reduced visibility (see FigureC.4) had the greatest impact on performance. In the presence of figures there is an almost linear decrease in precision as the recall constraint increases in the presence of figures (see FigureC.1a). In contrast, we observe a gradual decrease in precision and only declines sharply after about80%

of the recall constraint is reached (see FigureC.1c), in the absence of figures. A very similar observation is seen in during frames with reduced visibility as shown in FigureC.4.

The presence or absence of projector text or low lighting had very similar curves, suggesting that these factors had less of an impact on the detection accuracy when compared to the visibility of the board content and the presence of diagrams.

FigureC.1aandC.1bindicate a lower overall precision and recall in the presence of diagrams.

FigureC.1calso shows a more gradual decline in accuracy as the area recall quality constraint approaches1and only begins to decrease rapidly aftertr>0.8. In contrast, FigureC.1ashows a more constant decline that is almost linear, further indicating that in the presence of diagrams, the majority of detections are smaller than the GT. This is a limitation of the GT only providing a single tightest bounding box per board as discussed previously and shown in Figure4.3bin Chapter4.

FigureC.4candC.4dindicate a higher overall precision and recall that is sustained for a larger range of quality constraints when there is no reduction in visibility as opposed to a reduction in visibility as shown in FigureC.4aandC.4b. The reduced visibility graphs show an almost linear

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decrease that indicates that most of the detected rectangles are smaller than the GT rectangles.

This is plausible since the GT annotation would enclose all text correctly regardless of reduced visibility.

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0.0 0.2 0.4 0.6 0.8 1.0

0.2 0.4 0.6 0.8 1.0