## 4 Experiments
### Extracting Foot Parameters
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Number of Models**} & \multicolumn{2}{c}{**Manual Measurement**} & \multicolumn{2}{c}{**SIFT**} & \multicolumn{2}{c}{**SIFT, AKAZE**} & \multicolumn{2}{c}{**SIFT, AKAZE, DSP-SIFT**} \\ \cline{2-9}  & **FL** & **FW** & **FL** & **FW** & **FL** & **FW** & **FL** & **FW** \\  & **(mm)** & **(mm)** & **(mm)** & **(mm)** & **(mm)** & **(mm)** & **(mm)** & **(mm)** \\ \hline
1 & 265 & 123 & 264.5/(0.5) & 122.9/(0.1) & 264.7/(0.3) & 122.9 (0.1) & 264.9/(0.1) & 122.9/(0.1) \\
2 & 247 & 98 & 247.3/(0.3) & 98.8/(0.8) & 247.1/(0.1) & 98.5/(0.5) & 247.2/(0.2) & 98.4/(0.4) \\
3 & 242 & 95 & 243.2/(1.2) & 97.3/(2.3) & 243.1/(1.1) & 96.9/(1.9) & 242.9/(0.9) & 96.3/(1.3) \\
4 & 285 & 125 & 288.3/(3.3) & 127.1/(2.1) & 287.7/(2.7) & 126.8/(1.8) & 286.9/(1.9) & 126.4/(1.4) \\
5 & 235 & 93 & 235.2/(0.2) & 93.4/(0.4) & 235.2/(0.2) & 93.3/(0.3) & 235.1/(0.1) & 93.2/(0.2) \\
6 & 261 & 119 & 262.5/(1.5) & 120.9/(1.9) & 262.1/(1.1) & 120.5/(1.5) & 261.5/(0.5) & 120.3/(1.3) \\
7 & 238 & 91 & 237.5/(0.5) & 90.7/(0.3) & 237.7/(0.3) & 90.8/(0.2) & 237.8/(0.2) & 90.9/(0.1) \\
8 & 235 & 92 & 239.5/(4.5) & 92.9/(0.9) & 238.7/(3.7) & 92.7/(0.7) & 238.4/(3.4) & 92.5/(0.5) \\
9 & 269 & 121 & 268.1/(0.9) & 121.7/(0.7) & 268.5/(0.5) & 121.5/(0.5) & 268.8/(0.2) & 121.5/(0.5) \\
10 & 272 & 127 & 274.3/(2.3) & 127.9/(0.9) & 274.1/(2.1) & 127.7/(0.7) & 273.8/(1.8) & 127.6/(0.6) \\ \hline \multicolumn{9}{c}{Avg. Time (min)} & \multicolumn{2}{c}{3.31} & \multicolumn{2}{c}{9.58} & \multicolumn{2}{c}{**11.29**} \\ \multicolumn{9}{c}{Standard deviation (mm)} & \multicolumn{2}{c}{1.14} & \multicolumn{2}{c}{0.99} & \multicolumn{2}{c}{**0.87**} \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of the influence of informative feature extraction/comparison algorithms on foot parameters. Research on the Effect of Different Image Resolutions on the Processing Efficiency and Quality of 3D Models  
To increase the efficiency of data processing, it was decided to use a number of 2D images with reduced resolutions of two, three, and four times relative to the original. Table 4 shows experiments using a combination of the feature-extraction and -matching algorithms SIFT, DSP-SIFT, and AKAZE. The limit on the number of features was set to equal 10,000 per image. The created depth maps were scaled down two times relative to the original image. The average time to create 3D models and the standard deviations of the studied samples were calculated. The best standard deviation of 0.95 mm was obtained using a combination of the SIFT, AKAZE, and DSP-SIFT feature-extraction algorithms and 2D image series at 480 \(\times\) 270 (pixels); the average time to create one model, taking into account series generation and the image segmentation process, was 1 min 35 s. Three-dimensional models obtained at this resolution are smoother, but a large part of their features is lost due to a reduction in the number of reconstructed points in the sparse cloud. Figures 9 and 10 show an example of a reconstructed 3D model with a resolution of 480 \(\times\) 270 (pixels) of a series of images.