2,254 research outputs found
Extracting 3D parametric curves from 2D images of Helical objects
Helical objects occur in medicine, biology, cosmetics, nanotechnology, and engineering. Extracting a 3D parametric curve from a 2D image of a helical object has many practical applications, in particular being able to extract metrics such as tortuosity, frequency, and pitch. We present a method that is able to straighten the image object and derive a robust 3D helical curve from peaks in the object boundary. The algorithm has a small number of stable parameters that require little tuning, and the curve is validated against both synthetic and real-world data. The results show that the extracted 3D curve comes within close Hausdorff distance to the ground truth, and has near identical tortuosity for helical objects with a circular profile. Parameter insensitivity and robustness against high levels of image noise are demonstrated thoroughly and quantitatively
DIY Human Action Data Set Generation
The recent successes in applying deep learning techniques to solve standard
computer vision problems has aspired researchers to propose new computer vision
problems in different domains. As previously established in the field, training
data itself plays a significant role in the machine learning process,
especially deep learning approaches which are data hungry. In order to solve
each new problem and get a decent performance, a large amount of data needs to
be captured which may in many cases pose logistical difficulties. Therefore,
the ability to generate de novo data or expand an existing data set, however
small, in order to satisfy data requirement of current networks may be
invaluable. Herein, we introduce a novel way to partition an action video clip
into action, subject and context. Each part is manipulated separately and
reassembled with our proposed video generation technique. Furthermore, our
novel human skeleton trajectory generation along with our proposed video
generation technique, enables us to generate unlimited action recognition
training data. These techniques enables us to generate video action clips from
an small set without costly and time-consuming data acquisition. Lastly, we
prove through extensive set of experiments on two small human action
recognition data sets, that this new data generation technique can improve the
performance of current action recognition neural nets
Fast and robust curve skeletonization for real-world elongated objects
We consider the problem of extracting curve skeletons of three-dimensional,
elongated objects given a noisy surface, which has applications in agricultural
contexts such as extracting the branching structure of plants. We describe an
efficient and robust method based on breadth-first search that can determine
curve skeletons in these contexts. Our approach is capable of automatically
detecting junction points as well as spurious segments and loops. All of that
is accomplished with only one user-adjustable parameter. The run time of our
method ranges from hundreds of milliseconds to less than four seconds on large,
challenging datasets, which makes it appropriate for situations where real-time
decision making is needed. Experiments on synthetic models as well as on data
from real world objects, some of which were collected in challenging field
conditions, show that our approach compares favorably to classical thinning
algorithms as well as to recent contributions to the field.Comment: 47 pages; IEEE WACV 2018, main paper and supplementary materia
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