From Elevation Maps To Contour Lines: SVM and Decision Trees to Detect Violin Width Reduction

Abstract

We explore the automatic detection of violin width reduction using 3D photogrammetric meshes. We compare SVM and Decision Trees applied to a geometry-based raw representation built from elevation maps with a more targeted, feature-engineered approach relying on parametric contour lines fitting. Although elevation maps occasionally achieve strong results, their performance does not surpass that of the contour-based inputs.

Figures (7)

• Figure 1: Impact of the reduction of the width of the sound box on the contour lines.
• Figure 2: Elevation map sampled every 0.25$\times$ 0.25 and zone of interest (red rectangle). The two red lines on the scale bar indicate the minimum and maximum elevations of the violin sound board.
• Figure 3: Elevation maps resampled on a $5 \times 10$ square grid. The top line shows the resampling of a non reduced instrument (inv. no 2833) while the bottom line illustrates the resampling of a reduced instrument (inv. no 2846). The left-hand side highlights relative resampling and the right-hand side absolute resampling. Zones with zero elevation (no intersection between the grid and the mesh) are left in white. The two red lines on each scale bar indicate the minimum and maximum elevations of the violin sound boards.
• Figure 4: Impact of the parameters on the contour lines fitting.
• Figure 5: Fitting of contour lines of the sound board of an instrument (left) and profile of the parameters $\alpha$, $\beta$, $\gamma$ and $\delta$ for each level (right).