DGtal
1.3.beta

Part of the Geometry package.
This part of the manual describes some applications of a new definition of digital convexity, called full convexity [74] . See Digital convexity and full digital convexity for further details on full convexity.
The following programs are related to this documentation: geometry/volumes/fullConvexityAnalysis3D.cpp , geometry/volumes/fullConvexityThinning3D.cpp , geometry/volumes/fullConvexityLUT2D.cpp , geometry/volumes/fullConvexityCollapsiblePoints2D.cpp , geometry/volumes/fullConvexityShortestPaths3D.cpp , geometry/volumes/fullConvexitySphereGeodesics.cpp .
Full convexity is stable by intersection with an halfspace with axisaligned normal vector and integer intercept. Hence if \( X \) is fully convex, and \( N_k(x) \) is a \( (2k+1)^d \) neighborhood around point x, then \( X \cap N_k(x) \) is also fully convex.
This shows that a fully convex set is locally fully convex everywhere. Reciprocally local analysis with full convexity gives information on the local geometry:
Due to the stability properties of full convexity, one may observe that \( k+1 \)convexity at x implies \( k \)convexity at x, the same holds for concavity and planarity. Taking the contraposition indicates that \( k \)atypicality implies \( k+1 \)atypicality.
Class NeighborhoodConvexityAnalyzer offers many services to check these properties in an efficient way. It stores lookup tables to perform these services efficiently in 2D for 3x3 neighborhood, and also uses memoization to speedup computations (useful in dimension greater than 2 and/or when the neighborhood is larger).
The following snippet shows how you can use it.
You may have a look at geometricAnalysis3D.cpp to have a more complete example.
Tangency or subconvexity is a derived concept from full convexity. Let \( X \subset \mathbb{Z}^d \) some arbitrary digital set. Then the digital set \( A \subset \mathbb{Z}^d \) is said to be digitally k subconvex to \( X \) whenever \( C^d_k \lbrack \mathrm{Conv}(A) \rbrack \subset C^d_k \lbrack X \rbrack \). And \( A \) is said to be fully (digitally) subconvex to \( X \) whenever it is digitally k subconvex to \( X \) for \( 0 \le k \le d \).
We also say that \( A \) is tangent to \( X \). The elements of \( A \) are said to be cotangent (in \( X \)). Note that necessarily \( A \subset X \).
A path \( \gamma \) from a to b in \( X \) is then a sequence a points \( \gamma=(x_i)_{i=0,\ldots,n} \), such that \( x_0 = a \), \( x_n = b \), and for all \( i \in \{ 0, \ldots, n1 \} \), it holds that \( \{ x_i, x_{i+1} \} \) is tangent to \( X \).
We can embed in the Euclidean space the path \( \gamma \) simply by joining its consecutive points by Euclidean straight line segments. Its length is then just the Euclidean length of its embedding.
The cotangency relations define a graph on \( X \), which can be weighted by the length of the Euclidean segments joining cotangent points. Shortest paths on this graph are of course path in the above mentioned sense.
The class TangencyComputer offers some services to check tangency and to compute shortest paths:
To use it, you should include the following headers
We use here a TangencyComputer::ShortestPaths object to compute and store all shortest paths to a given target point.
The object stores for each point:
Example fullConvexityShortestPaths3D.cpp illustrates that kind of computations, giving the distance of each object point to the specified target.
You may obtain nicer views by exporting your result to an OBJ format and then render it.
If you wish to compute only one path, generally it is faster to use breadth first traversal from both the source and the target and stop at first collision. This is already implemented in TangencyComputer::shortestPath, using two TangencyComputer::ShortestPaths objects. The snippet below shows you how it is computed internally:
Classes TangencyComputer and TangencyComputer::ShortestPaths offer a way to considerably speed up shortest paths computation if one tolerates a little bit of approximation. Several methods accept a distance parameter K. If K is greater than \( \sqrt{d} \), then shortest paths are exact (meaning all extracted paths are the shortest possible in the sense of the above mentioned algorithm), but if you give a lower value (from \( \sqrt{d} \) to 0 included), you obtain path that may be only shortest path approximations. The table below shows you the tradeoff between the speedup and the approximation error. As one can see, speedup of \( 50500 \times \) are obtained, while approximations error are very low or sometimes null.
The following methods accept this parameter:
Running the example geometry/volumes/fullConvexitySphereGeodesics.cpp shows the speedup for different choices of K parameter, as well as the induced approximate distance. In the table below, not surprisingly, the maximum observed distance may be greater in case of approximation. We also indicate if the furthest point to the source point is correct or no.
Table below: Computation times of shortest paths to a given source and maximal error onto a 3D sphere digitized with gridstep \( h \). The chosen \( K \) indicates the chosen distance parameter. Choosing \( K=\pq\ \) or \( K=\sqrt{3} \) guarantees the correctness of the output. However, decreasing \( K \) to \( 0 \) speeds up the algorithm, while the maximal relative error in the distance estimation stays very low. Each cell of the last four columns displays the computation time (in seconds), and between parenthesis: OK if the furthest point is the exact antipodal point to the source point on the sphere, and the relative error of the measured shortest path in percentage.
gridstep h  \( \#(X) \)  Max distance  \( K=\sqrt{3} \)  \( K=\sqrt{3}/4 \)  \( K=\sqrt{3}/16 \)  \( K = 0 \) 

0.25  296  2.88739  0.187 (OK)  0.068 (OK,0.000%)  0.024 (OK,0.000%)  0.013 (OK,0.000%) 
0.125  1184  2.97166  1.480 (OK)  0.470 (OK,0.000%)  0.177 (OK,0.000%)  0.082 (OK,0.226%) 
0.0625  4784  3.02389  11.744 (OK)  4.106 (OK,0.000%)  1.037 (OK,0.000%)  0.340 (OK,0.538%) 
0.03125  19256  3.08405  97.161 (OK)  39.993 (OK,0.000%)  9.122 (OK,0.005%)  2.006 (OK,0.233%) 
0.015625  77120  3.10879  1828.890 (OK)  385.114 (OK,0.000%)  78.363 (OK,0.038%)  9.446 (OK,0.289%) 