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Table 4 shows that our method performs better than the other methods, achieving a recognition rate of 94.76 % in clothing-change situations.
Experimental results have shown that our method performs better than other methods, achieving a 94.76 % recognition rate in clothing change situations and 91.53 % when bag being carried on the CASIA-B dataset.
By thoroughly analyzing and optimizing each step of the detection pipeline we propose an architecture that outperforms traditional methods, achieving a task accuracy close to that of state-of-the-art approaches, while requiring a low computational time.
We also benchmark our performance on the standard VISOB database, where we outperform the state of the art methods, achieving a TPR of 99.5% at a FPR of 0.001%.
Having previously only used spray paint in designs, where few colors were available, ColorOrgy now employs predominately brush methods, achieving a smooth finish with a spray gun.
Surveys are, therefore, not considered a valid source for the estimation of per capita consumption of alcohol (Stockwell et al., 2004), despite some recent recall methods achieving a high coverage of alcohol sales data (Stockwell et al., 2008).
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IsoEM significantly outperforms the other methods, achieving an r2 values of over.96 for all datasets.
The experimental results show that our methods achieve a better time and load balancing results.
The results show that both methods achieve a similar geometric result.
As shown in Figure 11, the proposed methods achieved a high overall accuracy and the highest precision rates.
Both the proposed re-parameterization methods achieve a good degree of output prediction at a greatly decreased computational cost.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com