A. Giachetti et al., "Robust Automatic Labelling of Anatomical Landmarks on 3D Body Scans", in Proc. of 3rd Int. Conf. on 3D Body Scanning Technologies, Lugano, Switzerland, 2012, pp. 127-132, http://dx.doi.org/10.15221/12.127.
Robust Automatic Labelling of Anatomical Landmarks on 3D Body Scans
Andrea GIACHETTI 1, Umberto CASTELLANI 1, Christian LOVATO 2, Carlo ZANCANARO 2
1 Department of Computer Science, University of Verona, Verona, Italy;
2 Department of Neurological, Neuropsychological, Morphological and Movement Sciences, University of Verona, Verona, Italy
In this paper we analyze the performance of a pipeline for the extraction and semantic labelling of geometrically salient points on acquired human body models, improving the quality of the results and discussing its robustness against pose and body type variations.
Following the approach introduced in , we use a heat diffusion approach automatically detecting points as maxima of the autodiffusion function and using supervised classification to assign them a semantic label related to the anatomical part where the point is located. The resulting map can be used to perform measurements or to detect pose. The motivation of this approach is related to the fact that landmarks used in traditional anthropometry are not easily identified in digital models because they are localized by palpation and are not geometrically salient. An anatomical measurement system should be instead based on purely geometrical or image based landmarks. It is therefore interesting to find points of this kind that can be recognized in different subjects.
The use of heat diffusion analysis enables a robust salient point detection at different levels of detail and the creation of rich point descriptors not depending only on local geometry but also on global context, invariant with respect to articulated deformations (pose variation) and sufficiently stable against changes in body type.
In this work we improved our previous semantic labelling approach based on heat diffusion by selecting optimal point descriptors and feature-space distances and applying a hierarchical coarse to fine correction performing the classification at different scales and propagating the assigned labels from the coarser scale to the finer ones. Furthermore we tested the method on a collection of models representing different body types and with approximately fixed or largely different poses in order to demonstrate the robustness of the pipeline.
Experimental results show that this approach can be used to recognize robustly at least a selection of landmarks on subjects with different body types and independently on pose and could therefore applied for automatic anthropometric analysis.
Salient points, Heat diffusion, 3d body scanning
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