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The Propagated Skeleton: A Robust Detail-Preserving Approach: 21st IAPR International Conference, DGCI 2019, Marne-la-Vallée, France, March 26–28, 2019, Proceedings

The propagated skeleton:
a robust detail-preserving approach
B. Durix1, S. Chambon1, K. Leonard2, J.-L. Mari3, and G. Morin1
1IRIT-University of Toulouse, CNRS, France
2Occidental College, Los Angeles, CA, USA
3Aix Marseille Univ, Universite de Toulon, CNRS, LIS, Marseille, France
Abstract. A skeleton is a centered geometric representation of a shape
that describes the shape in a simple and intuitive way, typically reducing
the dimension by at least one. Skeletons are useful in shape analysis and
recognition since they provide a framework for part decomposition, are
stable under topology preserving deformation, and supply information
about the topology and connectivity of the shape. The main drawback
to skeletonization algorithms is their sensitivity to small boundary per-
turbations: noise on a shape boundary, such as pixelation, will produce
many spurious branches within a skeleton. As a result, skeletonizations
often require a second pruning step. In this article, we propose a new
2D skeletonization algorithm that directly produces a clean skeleton for
a shape, avoiding the creation of noisy branches. The approach propa-
gates a circle inside the shape, maintaining neighborhood-based contact
with the boundary and bypassing boundary oscillations below a chosen
threshold. By explicitly modeling the scale of noise via two parame-
ters that are shape-independent, the algorithm is robust to noise while
preserving important shape details. Neither preprocessing of the shape
boundary nor pruning of the skeleton is required. Our method produces
skeletons with fewer spurious branches than other state-of-the-art meth-
ods, while outperforming them visually and according to error measures
such as Hausdorff distance and symmetric difference, as evaluated on the
MPEG-7 database (1033 images).
1 Introduction
A skeleton is an internal structure that describes a shape, defined by the centers
of the maximally inscribed balls enclosed within the shape. The set of circle
centers and radii gives the medial axis, which completely describes the shape [5].
Because the skeleton can be represented by a graph where each edge represents
a feature of the shape, it is useful in applications such as shape analysis and
pattern recognition [18, 20, 4,22, 12]. To be useful in such applications, the skele-
ton should possess certain properties: centeredness within the shape, thinness,
topological equivalence with the shape, and completeness. It should also be ro-
bust to noise. The noise property is one of most difficult to respect, requiring a
distinction between noise and details of the shape. We propose a skeletonization
2 B. Durix et al.
method that possesses the above properties, and that is robust to noise on the
shape boundary.
Many skeletonization methods have been proposed [16]. Three common cat-
egories of computing a skeleton from a discrete structure are:
Thinning methods that use a morphological operator on a binary shape,
Distance map methods that consider the distance to the boundary,
Methods based on the Vorono¨ı diagram of points on the shape boundary.
Thinning methods erode shape pixels in 2D [9] or voxels in 3D [6] according
to specific rules and criteria. The resulting skeleton is a 1-pixel-wide structure.
We refer to [21] for a survey of these methods. Unfortunately, the resulting
skeleton lies on a 1-pixel-wide grid centered only up to a pixel size, thereby
inducing a loss of precision. Furthermore, the radius is not directly computed.
In contrast, our proposed skeletonization produces a structure not constrained
by the grid, and gives the radius for each skeletal point.
Distance maps are defined as the minimal distance between a point and
the shape boundary . The singularities of the distance map generate a skeleton.
One distance map skeleton uses an active contour evolving inside the shape, fol-
lowing the gradients of the distance transform [13] until the fronts from opposite
sides of the shape meet at the skeleton. One drawback to this method is that the
structure of the skeleton depends on the local speed of the active contour, which
is a function of the local curvature. Another distance map approach computes
singularities of the distance transform using image based-methods, then esti-
mates connectivity of the medial axis [15]. Because the distance map is discrete
on a discrete image, its singularities can only be computed up to a convolution
operator. The choice of this operator is crucial, and can create arbitrarily large
branches due to noise. Although our method also uses a distance map, its main
principle is to follow the boundary of the shape, which, consequently, makes it
more robust to noise.
Vorono¨ı skeletonization is the most popular method for computing a skele-
ton. Introduced by Ogniewicz [14] et al., the interior edges of the Vorono¨ı diagram
of a discrete boundary represent the skeleton. This method is popular because
it is efficient and precise. Unfortunately, the resulting skeleton usually includes
many uninformative branches and is therefore unnecessarily complex (cf. Fig-
ure 1(a)). For more on stability, see [3]. In 3D, the complexity is compounded,
which has led to methods such as the power crust [2] to extract a simpler struc-
ture. Once a skeleton is computed, most approaches require a second step to
prune noise. We next present pruning methods, and then describe approaches
to generate a clean skeleton without pruning.
Pruning methods remove noisy portions of a skeleton after computation.
Usually, a criterion is evaluated at each skeletal point to determine if that point
should be removed. We present three popular pruning approaches. The λ-medial
axis [7], with parameter λ, removes a skeletal point if the point’s circle intersects
the boundary at tangency points that are contained in a circle of radius λ. The
θ-homotopy medial axis [19], with parameter θ, evaluates the angle between the
center of the skeletal circle and the tangency points on the boundary. If the angle
The propagated skeleton: a robust detail-preserving approach 3
is less than θ, then the associated skeletal point is removed. Finally, the scale-
axis-transform (SAT) [8] grows each skeletal circle by a multiplicative factor
s, then computes a skeleton from the new shape. The circles generating the
new skeleton are then reduced using the multiplicative factor 1/s. A variant of
SAT that ensures homotopy equivalence with the boundary grows all circles by
the factor sand removes grown circles that are contained inside another grown
circle. These three methods have the similar drawbacks. First, the homotopy
of the pruned skeleton can not be guaranteed, except for SAT variant and the
λ-medial axis, for which the connectivity of the skeleton is ensured only if λis
lower than the smallest radius of its skeleton. Second, the choice of the parameter
is difficult and often must be tuned to each shape. Finally, no distinction can be
made between noise and small shape details, which means that desirable features
of the shape may be pruned away (cf. Figures 1(c) and 1(d)).
Directly computing a clean skeleton is an appealing alternative, with two
main approaches. The first approach includes a pruning criterion within the
skeletonization. For example, Digital Euclidean Connected Skeleton (DECS) [11]
combines the ridges of the distance map and the centers of the maximal balls to
obtain a skeleton on a grid of pixels (cf. Figure 1(e)). As with thinning methods,
the resulting skeleton has the width of one pixel. Furthermore, this method
returns a skeleton that tends to be irregular. The second approach modifies the
boundary itself, as in the circular boundary representation [1]. The resulting
skeleton avoids many noisy branches, but the construction is complicated: the
conversion of the boundary into arcs requires that the boundary be represented
by polynomial splines which are then converted into circular arcs based on a
chosen parameter. In contrast, our method uses the information of the discrete
boundary directly without preprocessing.
We propose a new skeletonization method that directly produces a clean
skeleton (cf. Figure 1(f)) from boundaries affected by noise. Two common types
of noise are pixelation noise resulting from the rasterization of the shape, and
Gaussian noise on the boundary. In 2D, because most shapes are extracted from
binary shape images, we focus here on rasterization noise. Our method propa-
gates a circle inside the shape, ensuring continuous contact along the boundary.
This ”growing approach” has previously been applied only for grid-constrained
skeletons [17]. By taking into account the scale of the rasterization noise, we can
avoid creating noisy branches. Our method draws from distance map skeletons,
searching for singularities of a distance map, and Vorono¨ı skeletons, computing
the connectivity of the skeleton directly from the boundary. The method gener-
ates a clean skeleton a priori and does not require a pruning step. Contributions
of our method are twofold: we explicitly avoid noise on the boundary, and we
capture salient geometric information with fewer branches.
Section 2 describes our method, and Section 3 compares our method with the
most common skeletonization approach, the Vorono¨ı skeletonization [14], pruned
by scale-axis-transform [8], λ-medial axis [7] and θ-homotopy medial axis [19].
We also compare with DECS [11].
4 B. Durix et al.
(a) Skeletons com-
puted by a Vorono¨ı
(b) s= 1.7 (c) λ= 9
(d) θ= 90o(e) Skeletons computed with
(f) Skeletons computed by our method, without chang-
ing the parameters
Fig. 1. Illustration of the limits of Vorono¨ı skeletonization (a), the pruning methods
(scale-axis-transform (b), λ-medial axis (c), θ-homotopy medial axis (d)), and DECS
(e), and the advantages of ours (f ). Green parts correspond to the reconstructed shape,
and red parts show what has been lost. Vorono¨ı (a) skeletons are noisy, requiring prun-
ing. Some pruning methods struggle to differentiate between noise and shape details,
and parts can be lost while noisy branches are preserved (b),(c). Others have shape
dependent parameters (d). The DECS method returns a skeleton that is simple, but
it tends to miss some parts, and be irregular. Our method (f ) distinguishes between
noise and details with parameters depending only on the scale of the noise.
2 Propagating skeletons
We consider a shape characterized by a matrix of pixels (from a binary image, for
example), S Mh,l({0; 1}), with hthe height of the image, and lits width. We
extract a piecewise linear contour Bfrom the shape pixels. From the boundary
contour, we construct a skeleton represented by a graph whose vertices corre-
spond to centers of skeletal balls and whose edges give connectivity information,
consistent with boundary connectivity, without branches generated by pixela-
The propagated skeleton: a robust detail-preserving approach 5
tion noise. Following the philosophy of the Vorono¨ı skeleton, where vertices are
centers of maximally inscribed bitangent balls, we propagate circles inside the
shape ensuring continuous contact with the boundary on at least two sides.
Before describing the complete algorithm in Section 2.4, we present some
necessary tools. Section 2.1 defines a smooth distance estimation (SDE), used
to compute a smoothed radius of skeletal circles. Section 2.2 explains the notion
of a contact set, a key construction for robustness to noise. Finally, Section 2.3
describes the circle propagation principle that determines neighbors of a circle
given its contact sets and the SDE.
2.1 Smooth distance estimation (SDE)
In this section, we consider a rasterized boundary. Typically, radius estimation
measures the minimal Euclidean distance to a point on the contour for each point
in the interior of the shape. This leads to poor radius estimates for rasterized
boundaries, as shown in Figure 2(a).
(a) (b) (c)
Fig. 2. The limitations of the Euclidean distance function compared to our proposed
distance function. (a) The black circle, with center represented by a cross, is rasterized,
giving the blue boundary: each pixel is considered inside the binary shape if its center
is in the black circle.. The red circle has same center as the black circle with radius
given by the minimum Euclidean distance to a point of the contour. As the radius of
the blue circle is less than that of the black circle, the Euclidean estimation of the
radius is incorrect. (b) Estimated circle from the SDE, for several values of σ. In (c),
the arrows indicate from top to bottom: the original circle (r= 10), the circle
associated with σ= 1 (r= 9.89), the circle associated with σ= 0.5 (r= 9.55), the
circle associated with the classic distance function (r= 9.51). When σapproaches 0,
the radius of the circle converges to the radius from the Euclidean distance function.
To improve radius estimation, we consider more than one boundary point to
construct a smoothed version of the Euclidean distance:
dB(P) = min
B∈B kPBk.
6 B. Durix et al.
The proposed distance adds a weight to points on the boundary to capture their
relative importance to the particular circle being computed:
fB(P) = X
pB(P , B)kPBk
pB(P , B),
where pB(P , B) is a weight assigned to the point Bof the boundary. To deter-
mine the weight, we consider the distance between Band the circle C(P, ρ) with
ρ=dB(P). The larger the distance, the less influential the point should be. In
other words, the weight function should decrease rapidly as distance to the circle
increases. A semi-normal distribution with parameter σfits these requirements:
Pσ(t00) = 2
2σ2dt .
The influence of each boundary point Bon Pis then given by:
pB(P , B) = Pσ(dC(P, B)),withdC(P , B) = kPBk dB(p).
The behavior of the function, illustrated in Figure 2, is related to the values
of the parameter σ. For σ= 0, fB is equal to the Euclidean distance. This
function is also used to identify an initial point of the skeleton. To estimate a
first point P0, we randomly choose a point Q0inside the shape, then maximize
the function fB(P , Q0). Any local maximum is a singular point of the function
fB and will therefore be a skeletal point. Once P0is estimated, we identify its
contact set and begin propagation. We define contact set in the next section.
2.2 Contact between the circle and the shape
We now define the contact set between a circle C(P, ρ), with center Pand radius
ρ=fB(P), and the contour B. We consider the parameter σfrom fB, and
introduce a new parameter, α, which defines the maximal distance from a point
to its associated circle. The contact set is the topological closure of boundary
points at a distance less than σto the circle together with points at a distance
less than αto the circle, as illustrated in Figure 3. A contact set corresponds to
consecutive boundary points where the first and last points are at most σ, and
all points are at most α, from the skeleton point. The points in the contact set
are analogous to the bitangent contact points in the continuous model, denoting
a set of points “closest” to the skeletal point. The contact sets also determine
the directions in which the circle C(P, ρ) can propagate. The parameter αallows
us to avoid noise on the boundary while simultaneously maintaining topological
consistency of the skeleton with the boundary (cf. Figure 3).
The definition of contact sets in our algorithm serves two purposes: reducing
noise and maintaining topological equivalence to the shape. First, the contact
The propagated skeleton: a robust detail-preserving approach 7
(a) (b)
Fig. 3. Contact sets for a skeletal point. In (a), we divide the area around the estimated
SDE circle into three parts, see (b) for details: the thick green part r+σ(where ris
the radius of the original circle), the thick orange part ασ, and the outer part. If
only the green is taken into account, we find 6 contact sets (in green, 3 at the top, 3
at the bottom). Adding the uncertainty zone using αmerges these into 2 contact sets
(blue lines), which prevents the creation of four noisy branches.
sets for a point designate the suitable directions for propagation without noise.
The value of α, chosen to distinguish between noise on the contour and details
of the shape, defines the maximum distance between a point of the contour and
the closest skeletal circle, which defines the Hausdorff distance between the true
boundary and the approximated boundary produced by the skeleton. Second, as
two neighboring circles must have overlapping contact sets, we are guaranteed to
consider all points of the boundary, which guarantees the topological equivalence
between skeleton and boundary.
2.3 Circle propagation
After estimating contact sets associated to a point Pof the skeleton, we prop-
agate the circle between successive pairs of contact sets. To propagate between
two successive contact sets, T1and T2, we search for the farthest circle that main-
tains contact with points of T1and T2(cf. Figure 4). This produces a nested
loop, with one loop searching for the distance, i.e. the circular arc on which there
is the next circle, and the other searching for the angle, i.e. the position of the
center on the given circular arc.
The search region for the next point of the skeleton is the portion of the
circle between the last point A1of T1and the first point A2of T2. For a given
distance d, less than the radius ρ=fB(P) of the circle, we search for an angle
θdwhere fB(Qd) is local maxima (with Qd=P+ (dcos(θd), d sin(θd))t). Not
every value of dcan describe a valid neighbor of P, since contact sets of the
neighbors must contain A1and A2. Using this property, we can validate, for
each d, the possibility of Qdas a neighbor of P. We select the highest value of
dso that Qdis a valid neighbor of P.
To estimate the value of θd, we take an initial interval θ1
0, θ2
0, where θi
designates the angle of Aion the circle, and, then, we decrease it with search by
8 B. Durix et al.
dichotomy. We compute the middle angle θibetween θ1
iand θ2
i. If the derivative
of gd(θ) = fB(P+ (dcos(θ), d sin(θ))t) is positive on θi, we update the interval
to θi, θ2
i, otherwise we update to θ0
i, θi. The search stops when (θ2
i) is
less than a given threshold θ=0,01σ
ρ. The search for the distance dis done in a
similar way, up to a threshold d= 0,01σ. These thresholds could be chosen as
machine precision, but because the scale of noise on the boundary is σ, we can
stop the search when the distance given by each interval has less influence than
the noise.
Using this algorithm, we find an approximation of a local skeleton around
the point P. The number of contact sets gives the degree of connectivity of the
skeleton at P. By construction, only the variations of the boundary more than
αfrom the circle are considered as new propagation directions. Finally, forcing
contact sets of any two successive circles to intersect guarantees that all the
points of the boundary are associated with at least one circle, and we produce a
connected skeleton. We summarize the complete algorithm in the next section.
(a) (b) (c)
Fig. 4. Propagation of a circle, for 2 contact sets in (a) and 3 contact sets in (b). In
(a), as the propagated circle comes from the left, we can propagate the red circle to
the right. The blue circle is validated when its contact sets (blue rectangles) intersect
the contact sets of the red circle (red rectangles). In the case of 3 contact sets (b), we
have two possible directions for the propagation, and both are explored. If there is a
small hole in the shape (c) that is completely covered by a contact set, the algorithm
still considers that the contact set has a beginning and end and will execute correctly.
2.4 Main algorithm
Algorithm 1 summarizes our approach, which uses two parameters, σand α
where σmodels the noise on the boundary and αgives a threshold on the
Hausdorff distance between the shape modeled by the skeleton and the input
shape, in order to discriminate between noise and details on the shape. We have
chosen σ=2/2'0.7 and α= 32/2'2.1 and these choices are explained in
Figure 5.
The algorithm ends when the list lcis empty. The algorithm can account
for loops in the skeleton: For each Qithat results from propagation of P, we
The propagated skeleton: a robust detail-preserving approach 9
Fig. 5. Justification of the choices of σand α.σmodels the rasterization of the bound-
ary, so it can vary from 2/2 to +2/2. Thus σ=2/2. αmodels random noise:
if there is a 1-pixel error on the boundary (like for the red line), the maximal distance
between the boundary and the rasterized boundary is 32/2. Thus α= 32/2.
Algorithm 1: Propagating skeleton algorithm
Parameters: σ: Noise on the boundary
α>σ: Maximum distance to the shape generated by the skeleton
Data: S: Binary grid representing the shape
B: Boundary of the shape (with 4-adjacency connectivity)
Result: S: Shape skeleton
1Estimation of the point P0, local maxima of fB (cf. Section 2.1)
2lc {P0}
3while lc6=do
4Removing the first element Pof lc
5Construction of C(P, fB (P)), maximal circle with center Pand radius
6Estimation of the tangency points between C(P, fB(P)) and B, with noise
parameter σand maximal distance to the skeleton α(cf. Section 2.2)
7Estimation by propagation of Q1,...,Qnthe neighbor circles of
C(P, fB (P)) (cf. Section 2.3)
8Checking for junctions between Q1,...,Qnand lc
9Adding Q1,...,Qn(not used in junction) in lc
10 end
check if there is a neighbor in the current center list lc. If so, we interrupt the
propagation from Qiand add a loop-closing edge.
The complexity is on the order of N2log2(S/d) log2(2π/θ), where Nis the
number of boundary points and Stheir maximum pairwise distance. Note that
the number of skeletal points is of the same order as the boundary points, and
that the cost of propagation is the cost of the nested loop and the distance
function. The complexity of the loop to search for the distance to the next
center is log2(dm/d), where dmis the maximal radius of a skeletal ball which
is bounded above by S. The complexity of the loop searching for the angle is
log2(θm/θ), where θmis the maximal angle between the extremities of two
contact sets, bounded above by 2π. The complexity of the SDE is of order N.
10 B. Durix et al.
We have described a skeletonization algorithm obtained by propagating cen-
ters of skeletal balls under the constraint that each circle maintains contact with
the boundary. The resulting skeleton is centered, thin, topologically equivalent
to the original shape, and complete. It is also robust to noise. In the next section,
we show skeletonization results and quantify the strengths of this method.
3 Results and evaluation
To evaluate our algorithm, we compare it to DECS [11], a grid-based method,
and to pruned versions of the classical Vorono¨ı skeletonization algorithm [14].
For pruning, we choose the three most common methods: scale-axis-transform
[8], λ-medial axis [7] and θ-homotopy medial axis [19].
We use three evaluation criteria. The first is the Hausdorff distance between
the initial shape and the shape reconstructed by the skeleton. This measures
the influence of the parameter α, which sets a maximum distance to the origi-
nal boundary. The second criterion measures the difference between the initial
surface Sand the reconstructed surface S0:
where A(SS0) is the area of the symmetric shape difference, and A(S) is the
area of the original shape. These criteria do not assess noise reduction, so our
third criterion counts the number of branches of the skeleton. Taken together,
we evaluate the simplicity of the considered skeleton in providing a given level of
shape fidelity. We perform tests on 1033 images from the MPEG-7 images [10].
See Table 1 for the averages obtained for each of the criteria and methods.
3.1 Analysis of the results
Our method produces a simpler skeleton than the Vorono¨ı skeleton while main-
taining good shape fidelity. The scale-axis-transform pruning and λ-medial axis,
in comparison, require many more branches to maintain a similar or lower fi-
delity. The θ-homotopy medial axis maintains fidelity with a reasonable number
of branches, but our method outperforms it in the Hausdorff distance and pro-
duces a similar area difference with comparable number of branches. This shows
that our method prioritizes the most informative branches both globally and lo-
cally when compared to other Vorono¨ı-based methods. For certain shapes, such
as the stingray, this results in a significant difference in shape quality. With simi-
lar numbers of branches, DECS gives better precision in terms of area difference,
but ours is better for Hausdorff distance.
Computation time of our algorithm averages 982ms for the MPEG-7 dataset
on a i7-4800MQ CPU at 2.70GHz. By comparison, the mean computation for
the full Vorono¨ı is 47ms and filtering with the scale-axis-transform takes between
451ms and 1118ms. The λ-medial axis takes around 86ms, and θ-medial axis
The propagated skeleton: a robust detail-preserving approach 11
Vorono¨ı skeletonization
All s λ θ DECS Ours
1.1 1.2 1.5 1 2 4 60o80o90o
Mean Hausdorff (px) 02.2 3.3 6.8 2.5 4.9 7.5 8.12.0
Median Hausdorff (px) 0 1.92.7 6.31.0 6.0 4.62.1
Mean Area (%) 3.0 3.1 2.9 3.7 3.3 4.1
Median Area (%) 2.2 2.5 2.1 2.5 2.73.5 4.22.1 3.2
Mean Branches 655 196 96 26 432 192 60 36 16 10 9 16
Median Branches 537 115 49 17 355 157 49 19 11 7 7 12
Table 1. Comparison between propagation skeletonization (with σ= 0.7 and α= 2.1),
Vorono¨ı skeletonization with three different pruning methods and DECS [11] on 1033
images from [10]. Evaluation is done with three criteria: the Hausdorff distance in
pixels, the area difference in percentages, and the number of branches. Red scores
indicate where our algorithm is better, and blue where others are, showing the relation
between the number of kept branches and the information lost.
takes between 788ms and 1171ms, depending on choices for their respective
parameters. The execution time of the DECS is 16ms.
We note the influence of the parameters σand α. Lower values for these
parameters produce more spurious branches, while higher values produce simpler
skeletons that are less accurate. These values can then be tuned appropriately
to image resolution or application.
4 Conclusion
We present a new skeletonization method that avoids creating uninformative
branches by explicitly modeling boundary noise. The method relies on two pa-
rameters that are straightforward to choose and do not require hand-tuning for
each shape: σ, the scale of noise on the boundary, and α, distinguishing between
noise and detail on the shape. We compare our method with the Vorono¨ı skele-
tonization pruned with three popular methods, and DECS. We provide quan-
titative evaluation showing that our method models the shape better in fewer
branches in terms of Hausdorff distance.
This work could be extended quite naturally. We focus here on rasterized
shapes, but we can generalize our approach to boundary representations with
other types of noise. We can also accommodate lack of connectivity on the bound-
ary, such as for point clouds, by redefining contact sets to recognize the directions
in which we can propagate. Finally, we can use a similar method to compute 3D
skeletons by propagation.
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... There are three different approaches to addressing the problem caused by noise: the skeletonizationbased approach, the pruning-based approach, and the scalespace-based approach. The skeletonization-based approach concentrates on the improved robustness of the skeletonization method with respect to noise, such as [10]- [15]. The merit of this kind of method is that it can reduce the effects of border noise and does not require extra operations, which has a lower computational cost. ...
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Extracting stable skeletons from noisy images is a challenging problem since the skeletonization method is prone to be affected by inner and border noise. Although many methods have been proposed in the past for increasing the antinoise ability of skeletonization methods, most of them either only overcome border noise or, at the cost of lost topology, degrade the effects of two noises. In this paper, we propose a skeleton extraction framework to enhance the robustness of the existing skeletonization method against both inner and border noise. In our approach, we first use the different scales of Gaussian filters to smooth the input image and obtain multiple representations. Then, binarization and skeletonization were performed to produce a series of binary images and a series of skeletal images. Next, we use our measure on these binary and skeletal images to find the most suitable skeleton. Since our measure considers both the skeleton image changes and binary image changes caused by using a filter, the selected skeleton is sufficiently robust and has all the necessary skeletal branches. The inner noise experiment and border noise experiment are conducted for comparison. From the perspective of the measure of the rate of variation in the skeleton, the proposed framework can reduce the inner noise by approximately 92% and the border noise by approximately 40%. In addition, the experiment on static hand gesture recognition has demonstrated that the introduction of our framework can increase up to 11% mean recognition accuracy.
... In In recent work [22], a 2D point cloud dataset with ground truth is proposed for skeleton extraction by deep learning. They use a 2D skeletonization algorithm [23] to generate the skeleton and manually adjust it as the ground true skeleton. Our tree-structured dataset is 3d unorganized point cloud models constructed from the corresponding ground truth skeletons. ...
Curve skeleton extraction from unorganized point cloud is a fundamental task of computer vision and three-dimensional data preprocessing and visualization. A great amount of work has been done to extract skeleton from point cloud. but the lack of standard datasets of point cloud with ground truth skeleton makes it difficult to evaluate these algorithms. In this paper, we construct a brand new tree-structured point cloud dataset, including ground truth skeletons, and point cloud models. In addition, four types of point cloud are built on clean point cloud: point clouds with noise, point clouds with missing data, point clouds with different density, and point clouds with uneven density distribution. We first use tree editor to build the tree skeleton and corresponding mesh model. Since the implicit surface is sufficiently expressive to retain the edges and details of the complex branches model, we use the implicit surface to model the triangular mesh. With the implicit surface, virtual scanner is applied to the sampling of point cloud. Finally, considering the challenges in skeleton extraction, we introduce different methods to build four different types of point cloud models. This dataset can be used as standard dataset for skeleton extraction algorithms. And the evaluation between skeleton extraction algorithms can be performed by comparing the ground truth skeleton with the extracted skeleton.
This paper introduces a novel approach to generate visually promising skeletons automatically without any manual tuning. In practice, it is challenging to extract promising skeletons directly using existing approaches. This is because they either cannot fully preserve shape features, or require manual intervention, such as boundary smoothing and skeleton pruning, to justify the eye-level view assumption. We propose an approach here that generates backbone and dense skeletons by shape input, and then extends the backbone branches via skeleton grafting from the dense skeleton to ensure a well-integrated output. Based on our evaluation, the generated skeletons best depict the shapes at levels that are similar to human perception. To evaluate and fully express the properties of the extracted skeletons, we introduce two potential functions within the high-order matching protocol to improve the accuracy of skeleton-based matching. These two functions fuse the similarities between skeleton graphs and geometrical relations characterized by multiple skeleton endpoints. Experiments on three high-order matching protocols show that the proposed potential functions can effectively reduce the number of incorrect matches.
Conference Paper
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We present SkelNetOn 2019 Challenge and Deep Learning for Geometric Shape Understanding workshop to utilize existing and develop novel deep learning architectures for shape understanding. We observed that unlike traditional segmentation and detection tasks, geometry understanding is still a new area for deep learning techniques. SkelNetOn aims to bring together researchers from different domains to foster learning methods on global shape understanding tasks. We aim to improve and evaluate the state-of-the-art shape understanding approaches, and to serve as reference benchmarks for future research. Similar to other challenges in computer vision, SkelNetOn proposes three datasets and corresponding evaluation methodologies; all coherently bundled in three competitions with a dedicated workshop co-located with CVPR 2019 conference. In this paper, we describe and analyze characteristics of datasets, define the evaluation criteria of the public competitions, and provide baselines for each task.
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Thinning or skeletonization is a process for reducing foreground regions in a binary image to a skeletal remnant that largely preserves the extent and connectivity of the original region while throwing away most of the original foreground. Thinning is commonly used in digital image processing, pattern recognition, image analysis and not least, in signature verification. The goal of this paper is to introduce the most common thinning methodologies and propose a method to evaluate their performance, especially in the field of signature recognition. The proposed evaluation method is intended to be objective, therefore it takes into account various properties of a thinned skeleton and compares them to those of an ideal reference image. Fifteen different algorithms have been implemented and rated using this method, the results showed that different kinds of skeletonization techniques have different benefits and drawbacks, however none was found to give perfect results.
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A robust and efficient method in 2D and 3D for the calculation of skeletons for arbitrary objects is presented. The method is based on the calculation of the distance function with respect to the object boundary. This is combined, in a post processing step, with a new indicator to identify the skeleton, which coincides with the singularity set of the distance map. The indicator is defined as a suitable function of certain local momenta of this distance map and allows a robust and accurate computation of the distance from the skeleton set. This distance is then extended, again via the level set method, onto the whole space. Several applications in 2D and 3D are presented.
Abstract The skeleton is an essential shape descriptor providing a compact representation of a shape that can be used in the context of real object recognition. However, due to the discretization, the required properties to use it for graph matching (homotopy to the shape, consequently connectivity, thinness, robustness to noise) may be difficult to obtain simultaneously. In this paper, we propose a new skeletonization algorithm having all these properties, based on the Euclidean distance map. More precisely, the algorithm cleverly combines the centers of maximal balls included in the shape and the ridges of the distance map. Post-processing is then applied to thin and prune the resulting skeleton. We compare the proposed method to three fairly recent methods and demonstrate its good properties.
Medial axis is known to be unstable for nonsmooth objects. For an open set O, we define the weak feature size, wfs, minimum distance between O(c) and the critical points of the function distance to O(c). We introduce the "lambda-medial axis" M(lambda) of O, a subset of the medial axis of O which captures the homotopy type of O when lambda < wfs. We show that, at least for some "regular" values of lambda, M(lambda) remains stable under Hausdorff distance perturbations of O(c). (c) 2005 Elsevier Inc. All rights reserved.
a b s t r a c t We propose a novel significance measure for skeleton pruning, called bending potential ratio (BPR), in which the decision regarding whether a skeletal branch should be pruned or not is based on the context of the boundary segment that corresponds to the branch. By considering this contextual information, we can better evaluate the contribution of the boundary segment to the overall shape, which generally depends on its particular location within the whole contour (i.e., a segment may be considered to be insignificant in one place while it may be considered as a feature elsewhere). The BPR is a measure of the significance of contour segments in such context, and depicts the bending potential of a contour segment. Unlike other significance measures that only contain local shape information, the BPR evaluates both local and global shape information. Thus, it is insensitive to local boundary deformation. In addition, we also present a scheme for skeleton growing, which integrates pruning based on the BPR measurement. Our experiments demonstrate that the skeletons obtained by the proposed algorithm are medially placed and connected. We also demonstrate that shapes reconstructed from these skeletons are very close to the original shapes. Moreover, the BPR measure yields a natural multi-scale skeletal representation.
We present a simple, efficient, and stable method for computing—with any desired precision—the medial axis of simply connected planar domains. The domain boundaries are assumed to be given as polynomial spline curves. Our approach combines known results from the field of geometric approximation theory with a new algorithm from the field of computational geometry. Challenging steps are (1) the approximation of the boundary spline such that the medial axis is geometrically stable, and (2) the efficient decomposition of the domain into base cases where the medial axis can be computed directly and exactly. We solve these problems via spiral biarc approximation and a randomized divide & conquer algorithm.
Skeletons provide a synthetic and thin representation of objects. Therefore, they are useful for shape description. Recent papers have proposed to approximate the skeleton of continuous shapes using the Voronoi graph of boundary points. An original formulation is presented here, using the notion of polyballs (we call polyball any finite union of balls). A preliminary work shows that their skeletons consist of simple components (line segments in 2D and polygons in 3D). An efficient method for simplifying 3D continuous skeletons is also presented. The originality of our approach consists in simplifying the shape without modifying its topology and in including these modifications on the skeleton. Depending on the desired result, we propose two strategies which lead to either surfacical skeletons or wireframe skeletons. Two angular criteria are proposed that allow us to build a size-invariant hierarchy of simplified skeletons.