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- Genre: Masters Thesis
Description
In this thesis, a FORTRAN code is rewritten in C++ with an object oriented ap-
proach. There are several reasons for this purpose. The first reason is to establish
the basis of a GPU programming. To write programs that utilize GPU hardware,
CUDA or OpenCL is used which only support C and C++. FORTRAN has a feature
that lets its programs to call C/C++ functions. FORTRAN sends relevant data to
C/C++, which in turn sends that data to OpenCL. Although this approach works,
it makes the code messy and bulky and in the end more difficult to deal with. More-
over, there is a slight performance decrease from the additional data copy. This is
the motivation to have the code entirely written in C++ to make it more uniform,
efficient and clean. The second reason is the object oriented feature of the C++. The
“abstraction”, “inheritance” and “run-time polymorphism” features of C++ provide
some form of classes and objects, the ability to build new abstractions, and some
form of run-time binding, respectively. In recent years, some of popular codes has
been rewritten in C++ which were initially in FORTRAN. One of these softwares is
LAMMPS.
In this code the level set equation is solved by RLSG method to track the interface in
two phase flow. In gas/fluid flows, the surface tension is important and only exists at
the interface. Therefore, the location and some geometric features of interface need
to be evaluated which can be achieved by solving the level set equation.
proach. There are several reasons for this purpose. The first reason is to establish
the basis of a GPU programming. To write programs that utilize GPU hardware,
CUDA or OpenCL is used which only support C and C++. FORTRAN has a feature
that lets its programs to call C/C++ functions. FORTRAN sends relevant data to
C/C++, which in turn sends that data to OpenCL. Although this approach works,
it makes the code messy and bulky and in the end more difficult to deal with. More-
over, there is a slight performance decrease from the additional data copy. This is
the motivation to have the code entirely written in C++ to make it more uniform,
efficient and clean. The second reason is the object oriented feature of the C++. The
“abstraction”, “inheritance” and “run-time polymorphism” features of C++ provide
some form of classes and objects, the ability to build new abstractions, and some
form of run-time binding, respectively. In recent years, some of popular codes has
been rewritten in C++ which were initially in FORTRAN. One of these softwares is
LAMMPS.
In this code the level set equation is solved by RLSG method to track the interface in
two phase flow. In gas/fluid flows, the surface tension is important and only exists at
the interface. Therefore, the location and some geometric features of interface need
to be evaluated which can be achieved by solving the level set equation.
ContributorsSafarkhani, Salar (Author) / Herrmann, Mrcus (Thesis advisor) / Oswald, Jay (Committee member) / Rykczewski, Konrad (Committee member) / Arizona State University (Publisher)
Created2015
Description
Video object segmentation (VOS) is an important task in computer vision with a lot of applications, e.g., video editing, object tracking, and object based encoding. Different from image object segmentation, video object segmentation must consider both spatial and temporal coherence for the object. Despite extensive previous work, the problem is still challenging. Usually, foreground object in the video draws more attention from humans, i.e. it is salient. In this thesis we tackle the problem from the aspect of saliency, where saliency means a certain subset of visual information selected by a visual system (human or machine). We present a novel unsupervised method for video object segmentation that considers both low level vision cues and high level motion cues. In our model, video object segmentation can be formulated as a unified energy minimization problem and solved in polynomial time by employing the min-cut algorithm. Specifically, our energy function comprises the unary term and pair-wise interaction energy term respectively, where unary term measures region saliency and interaction term smooths the mutual effects between object saliency and motion saliency. Object saliency is computed in spatial domain from each discrete frame using multi-scale context features, e.g., color histogram, gradient, and graph based manifold ranking. Meanwhile, motion saliency is calculated in temporal domain by extracting phase information of the video. In the experimental section of this thesis, our proposed method has been evaluated on several benchmark datasets. In MSRA 1000 dataset the result demonstrates that our spatial object saliency detection is superior to the state-of-art methods. Moreover, our temporal motion saliency detector can achieve better performance than existing motion detection approaches in UCF sports action analysis dataset and Weizmann dataset respectively. Finally, we show the attractive empirical result and quantitative evaluation of our approach on two benchmark video object segmentation datasets.
ContributorsWang, Yilin (Author) / Li, Baoxin (Thesis advisor) / Wang, Yalin (Committee member) / Cleveau, David (Committee member) / Arizona State University (Publisher)
Created2013