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WSEAS TRANSACTIONS
on BUSINESS and ECONOMICS
Issue 11, Volume 4, November 2007
Print
ISSN: 1109-9526
E-ISSN: 2224-2899 |
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Title of the Paper: An Integrated Two-Phased
Decision Support System for Resource Allocation
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Authors: Chang-Kyo Suh
Abstract: This paper concerns the design of decision support systems (DSSs)
which help financial managers in evaluating proposals for strategic and
long-range planning. With the proposed two-phased DSS, projects are first
selected from a given pool according to greedy heuristics based on the
project’s preferences as well as the project’s efficiency. Then, integer
programming with an approximation algorithm is used in the second phase to
re-evaluate those proposed projects which met the first phase criteria.
Keywords: Decision Support System, Resource Allocation, Analytic Hierarchy
Process, Project Preference
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Title of the Paper: Dynamic Gesture
Recognition Based on Dynamic Bayesian Networks
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Authors: Wei-Hua Andrew Wang, Chun-Liang
Tung
Abstract: Techniques for recognizing and matching dynamic human gestures are becoming increasingly
important with the CCTV surveillance system. To provide consistent dynamic gesture recognition system,
Hierarchical Dynamic Vision System (HDVS) which based on dynamic Bayesian networks (DBNs) is proposed
for automatically identifying human gestures in this paper. DBNs, directed graphical models of stochastic
process, generalize HMM by representing the hidden and observed state in terms of state variables in which
can have more complex interdependencies than HMMs systems do. In this paper, hierarchical hidden Markov
model (HHMM) is used as the underlying topology in the proposed dynamic system to recognize human
gestures with motion trajectories in an indoor scene. A hierarchical HMM, represented by DBN, is structured
multi-level stochastic processes. In the low-level processing, both motion trajectories and motion directions
generated from hand part is used as features after watershed segmentation. In the high-level processing,
human gestures are automatically recognized form the inference of HHMM-DBNs. In this paper, we focus on
the following aspects of both system modeling and high-level processing: (1) Completed DBNs structure with
HHMM, (2) approaches to human gesture recognition.
Keywords: Hierarchical Dynamic Vision System, dynamic Bayesian network
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