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The Variable Markov Oracle: Algorithms for Human Gesture Applications
This article introduces the Variable Markov Oracle (VMO) data structure for multivariate time series indexing. VMO can identify repetitive fragments and find sequential similarities between observations. VMO can also be viewed as a combination of online clustering algorithms with variable-order Markov constraints. The authors use VMO for gesture query-by-content and gesture following. A probabilistic interpretation of the VMO query-matching algorithm is proposed to find an analogy to the inference problem in a hidden Markov model (HMM). This probabilistic interpretation extends VMO to be not only a data structure but also a model for time series. Query-by-content experiments were conducted on a gesture database that was recorded using a Kinect 3D camera, showing state-of-the-art performance. The query-by-content experiments' results are compared to previous works using HMM and dynamic time warping. Gesture following is described in the context of an interactive dance environment that aims to integrate human movements with computer-generated graphics to create an augmented reality performance
Motion planning for geometric models in data visualization
Interaktivní geometrické modely pro simulaci přírodních jevů (LH11006)Pokročilé grafické a počítačové systémy (SGS-2016-013)A finding of path is an important task in many research areas and it is
a common problem solved in a wide range of applications. New problems of
finding path appear and complex problems persist, such as a real-time plan-
ning of paths for huge crowds in dynamic environments, where the properties
according to which the cost of a path is evaluated as well as the topology
of paths may change. The task of finding a path can be divided into path
planning and motion planning, which implicitly respects the collision with
surroundings in the environment.
Within the first group this thesis focuses on path planning on graphs for
crowds. The main idea is to group members of the crowd by their common
initial and target positions and then plan the path for one representative
member of each group. These representative members can be navigated by
classic approaches and the rest of the group will follow them. If the crowd can
be divided into a few groups this way, the proposed approach will save a huge
amount of computational and memory demands in dynamic environments.
In the second area, motion planning, we are dealing with another problem.
The task is to navigate the ligand through the protein or into the protein,
which turns out to be a challenging problem because it needs to be solved in
3D with the collision detection
Data Stream Clustering: Challenges and Issues
Very large databases are required to store massive amounts of data that are
continuously inserted and queried. Analyzing huge data sets and extracting
valuable pattern in many applications are interesting for researchers. We can
identify two main groups of techniques for huge data bases mining. One group
refers to streaming data and applies mining techniques whereas second group
attempts to solve this problem directly with efficient algorithms. Recently
many researchers have focused on data stream as an efficient strategy against
huge data base mining instead of mining on entire data base. The main problem
in data stream mining means evolving data is more difficult to detect in this
techniques therefore unsupervised methods should be applied. However,
clustering techniques can lead us to discover hidden information. In this
survey, we try to clarify: first, the different problem definitions related to
data stream clustering in general; second, the specific difficulties
encountered in this field of research; third, the varying assumptions,
heuristics, and intuitions forming the basis of different approaches; and how
several prominent solutions tackle different problems. Index Terms- Data
Stream, Clustering, K-Means, Concept driftComment: IMECS201
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