State-of the-art robust 3D watermarking schemes already withstand combinations of a wide variety of attacks (e.g. noise
addition, simplification, smoothing, etc). Nevertheless, there are practical limitations of existing 3D watermarking
methods due to their extreme sensitivity to cropping. Spread Transform Dither Modulation (STDM) method is an
extension of Quantization Index Modulation (QIM). Besides the simplicity and the trade-off between high capacity and
robustness provided by QIM methods, it is also resistant against re-quantization. This paper focuses on two state-of-the-art
techniques which offer different and complementary advantages, respectively QIM-based 3D watermarking and
feature point-based watermarking synchronization. The idea is to combine both in such a way that the new scheme
would benefit from the advantages of both techniques and compensate for their respective fragilities. The resulting
scheme does not make use of the original 3D model in detection but of some parameters as side-information. We show
that robustness against cropping and other common attacks is achieved provided that at least one feature point as well as
its corresponding local neighborhood is retrieved.
Structural features extraction is essential in various molecular biology applications such as functional classification
or binding site prediction for molecular docking. In the literature, methods to study the topology and the
accessibility of molecule surfaces exist. Some of them are based on the 3D Delaunay triangulation of the set
of points formed by the atoms center. In this paper, we propose to investigate the spectral properties of this
triangulation by computing and analyzing the first eigenvector of its adjacency matrix. This technique is already
used in graph theory to extract core features and to compare networks, 3D meshes, or any set of points and edges.
Tests were performed, providing two promising results. First, the correlation between eigenvectors computed
from a molecular complex and one of its component is much higher than between structure independent molecules.
It allows to find common sub-structures between molecules even after small conformation changes, because no
distance is considered, but only the adjacency of the Delaunay triangulation. Second, the value of the eigenvector
at indexes corresponding to binding site atoms is higher than for other surface atoms. As this feature is correlated
with no other important geometric or physicochemical binding site properties (curvature, depth, hydrogen bonds
capacity, ...), it can be integrated in a larger process aiming to localize binding sites.
Polygon meshes are collections of vertices, edges and faces defining surfaces in a 3D environment. Computing
geometric features on a polygon mesh is of major interest for various applications. Among these features, the
geodesic distance is the distance between two vertices following the surface defined by the mesh. In this paper,
we propose an algorithm for fast geodesic distance approximation using mesh decimation and front propagation.
This algorithm is appropriated when a fast geodesic distances computation is needed and when no fine precision
Active site prediction, well-known for drug design and medical diagnosis, is a major step in the study and prediction
of interactions between proteins. The specialized literature provides studies of common physicochemical
and geometric properties shared by active sites. Among these properties, this paper focuses on the travel depth
which takes a major part in the binding with other molecules. The travel depth of a point on the protein solvent
excluded surface (SES) can be defined as the shortest path accessible for a solvent molecule between this point
and the protein convex hull.
Existing algorithms providing an estimation of this depth are based on the sampling of a bounding box volume
surrounding the studied protein. These techniques make use of huge amounts of memory and processing time
and result in estimations with precisions that strongly depend on the chosen sampling rate. The contribution of
this paper is a surface-based algorithm that only takes samples of the protein SES into account instead of the
whole volume. We show this technique allows a more accurate prediction, at least 50 times faster.
A validation of this method is also proposed through experiments with a statistical classifier taking as inputs
the travel depth and other physicochemical and geometric measures for active site prediction.