After identifying image pixels as
being part of the thread structure, it is necessary to assemble them into an
actual thread pattern. This is achieved by performing a registration of the
model prior, describing the desired thread pattern, to the actually detected
thread pattern. It is essential for this step to be robust against potential
outliers and/or missed stitch positions. Since the observed thread pattern will
not equal the model prior in general due to possible tissue distortions, an
adaptation of the model is necessary. The adaptation process corresponds to finding
a unique deformation vector for each model stitch point.
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A. Thread Representation
Positions of the thread appearing in the image are extracted using a blob
detection on the binary image wges(x, y). Every position found this way will in
the following be called a thread representative. The representatives are
preferably distributed in equidistant steps over the length of the thread.
Furthermore, they can be ordered to form a sequence of points, representing the
entirety of the thread. The result is displayed in Figure 5c, with the circles
visualizing the found representatives. However, it can be seen that outliers
are possible (visualized in red). Additionally, individual thread positions
might be missed. The model fitting and adaptation is not performed using each
individual stitch, since such an approach would be highly susceptible to
interferences like outliers. Instead, more abstract features are considered,
that can be robustly recognized within both the set of representatives and the
pattern model. • Characteristic points may be thread endings, points where the
thread pattern changes abruptly its direction, or the intersection of lines. •
Polygons may be formed by the linear connection of neighboring representatives.
The corresponding polygons within the pattern model are formed by the linear
connection of neighboring model stitch positions. Next to the improved
robustness, the computation complexity is heavily reduced.
B. Initial Model-Based
Registration The specimen placed inside the inspection system may be
arbitrarily rotated and shifted. For this purpose, an initial estimation of the
rough placement of the thread pattern relative to the prior model is estimated.
The estimation utilizes the generalized Hough transformation (GHT) [2] [6],
which has been proven to fulfill this task. The goal is to compute a global
shift vector minimizing the distance between model and real world thread
features.
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C. Iterative registration Based
on the initial model-based registration, the model adaptation is performed in
an iterative procedure. Each iteration consists of multiple steps. First, an
assignment between features within the model and the representatives is
established. The assignment needs to be considered separately for both feature
types. The target of a characteristic model point is a characteristic thread
representative having the same type and being at the minimum Euclidean
distance. The search for corresponding target polygons is based on the polygon
center and its direction. Once the assignments are established, a
transformation of the current model features is performed to approximate the
thread pattern. Within the first iterations, the assignments are rather
unreliable. Therefore, only a rigid registration having few transformation
parameters but many assigned feature points is determined. Over the course of
the iterations, the assignments become more reliable and the number of free
transformation parameters is increased. In the end, the assignments become
extremely reliable. Thus, individual model points are only then allowed to be
shifted towards individual thread points, resulting in a controlled deformation
of the model to adapt to the real thread pattern. Every transformation is
estimated using an energy minimization approach, independent of the degree of
freedom. There exist two types of energies, internal and external. The internal
energy is a measure of the deformation of the model. The more a model polygon
vector deviates from its original vector in size and direction, the higher the
cost. The contribution of the external energy depends on the type of feature.
If an assignment between a characteristic point in the model and its
counterpart on the thread is possible, the model point can be directly
attracted to its target. The strength of attraction is independent of the
direction and depends only on the Euclidean distance between both. For most
cases, an exact assignment from model polygons to individual thread polygons is
not useful due to the high number of them that causes confusability. However, a
direction of attraction can be determined. Therefore the external energy for
polygons and the points spanning those polygons is computed in dependance on
the direction of the model polygon normal at its center point and the
projection of that normal onto a thread polygon vector.
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