Our method is off by at most 3 frames in 95% cases, whereas the coordinate-based algorithm only attains this level of accuracy in 83% cases.</p
<p>Accuracy of detecting the gazed numbers using different algorithmic chains for the NG task (for o...
Population size = 100 and Generation = 300. (a) Range, (b) Average Tolerance, (c) Average Hamming Di...
<p>Comparison of the average precision rates, recall rates and F1 values for the different classific...
<p>(a) the accuracy comparison of the different methods using the first manually segmented results o...
<p>Comparison of sensitivity shown by different prediction methods on the independent datasets.</p
Only the best algorithms (highlighted in Fig 3) are shown. Accuracy is calculated as (TP+TN)/total; ...
<p>(a) represents comparison of positive detection rate. (b) shows comparison of negative detection ...
<p>The recognition precision is given under the same experimental setting to the above ones in which...
<p>Each panel compares the average error rate between CLAPPER and competing methods for a particular...
<p>Each test set corresponds to a split of the 100,000 ratings in the complete dataset into 80,000 o...
All four algorithms were tested on the isolated jumps to determine accuracy in flight time (A) and j...
<p>Grand average scores of (Blue) sensitivity, (Red) precision, and (Green) F-measure, obtained by t...
Comparison of performance obtained by our approach with other state-of-the-art algorithms.</p
Comparison of false positive ratio (FPR) and true positive ratio (TPR) for machine learning algorith...
Comparison of accuracy for different approaches, where small value indicates good performance and bo...
<p>Accuracy of detecting the gazed numbers using different algorithmic chains for the NG task (for o...
Population size = 100 and Generation = 300. (a) Range, (b) Average Tolerance, (c) Average Hamming Di...
<p>Comparison of the average precision rates, recall rates and F1 values for the different classific...
<p>(a) the accuracy comparison of the different methods using the first manually segmented results o...
<p>Comparison of sensitivity shown by different prediction methods on the independent datasets.</p
Only the best algorithms (highlighted in Fig 3) are shown. Accuracy is calculated as (TP+TN)/total; ...
<p>(a) represents comparison of positive detection rate. (b) shows comparison of negative detection ...
<p>The recognition precision is given under the same experimental setting to the above ones in which...
<p>Each panel compares the average error rate between CLAPPER and competing methods for a particular...
<p>Each test set corresponds to a split of the 100,000 ratings in the complete dataset into 80,000 o...
All four algorithms were tested on the isolated jumps to determine accuracy in flight time (A) and j...
<p>Grand average scores of (Blue) sensitivity, (Red) precision, and (Green) F-measure, obtained by t...
Comparison of performance obtained by our approach with other state-of-the-art algorithms.</p
Comparison of false positive ratio (FPR) and true positive ratio (TPR) for machine learning algorith...
Comparison of accuracy for different approaches, where small value indicates good performance and bo...
<p>Accuracy of detecting the gazed numbers using different algorithmic chains for the NG task (for o...
Population size = 100 and Generation = 300. (a) Range, (b) Average Tolerance, (c) Average Hamming Di...
<p>Comparison of the average precision rates, recall rates and F1 values for the different classific...