62113571
Learned probabilistic image motion models for event detection in videos
We present new probabilistic motion models of interest for the detection of relevant dynamic contents (or events) in videos. We separately handle the dominant image motion assumed to be due to the camera motion and the residual image motion related to scene motion. These two motion components are then represented by different probabilistic models which are further recombined for the event detection task. The motion models associated to pre-identified classes of meaningful events are learned from a training set of video samples. The event detection scheme proceeds in two steps which exploit different kinds of information and allow us to progressively select the video segments of interest using maximum likelihood (ML) criteria. The efficiency of the proposed approach is demonstrated on sports videos.
18387908
Extraction of Semantic Dynamic Content from Videos with Probabilistic Motion Models
The exploitation of video data requires to extract information at a rather semantic level, and then, methods able to infer “concepts” from low-level video features. We adopt a statistical approach and we focus on motion information. Because of the diversity of dynamic video content (even for a given type of events), we have to design appropriate motion models and learn them from videos. We have defined original and parsimonious probabilistic motion models, both for the dominant image motion (camera motion) and the residual image motion (scene motion). These models are learnt off-line. Motion measurements include affine motion models to capture the camera motion, and local motion features for scene motion. The two-step event detection scheme consists in pre-selecting the video segments of potential interest, and then in recognizing the specified events among the pre-selected segments, the recognition being stated as a classification problem. We report accurate results on several sports videos.
108001
Cournot Equilibrium in Price-Capped Two-Settlement Electricity Markets
We compare two alternative mechanisms for capping prices in two-settlement electricity markets. With sufficient lead time and competitive entry opportunities, forward market prices are implicitly capped by competitive pressure of potential entry that will occur when forward prices rise above a certain level. Another more direct approach is to cap spot prices through regulatory intervention. In this paper we explore the implications of the two alternative mechanisms in a two settlement Cournot equilibrium framework. We formulate the market equilibrium as a stochastic equilibrium problem with equilibrium constraints (EPEC) capturing congestion effects, probabilistic contingencies and market power. As an illustrative test case we use the 53-bus Belgian electricity network with representative generator cost but hypothetical demand and ownership assumptions. When compared to two-settlement systems without price caps we find that either of the price capping alternatives results in reduced forward contracting. Furthermore the reduction in spot prices due to forward contracting is smaller.
6507492
Induction of Classification Rules by Granular Computing
A granular computing model is used for learning classification rules by considering the two basic issues: concept formation and concept relationships identification. A classification rule induction method is proposed. Instead of focusing on the selection of a suitable partition, i.e., a family of granules defined by values of an attribute, in each step, we concentrate on the selection of a single granule. This leads to finding a covering of the universe, which is more general than partition based methods. For the design of granule selection heuristics, several measures on granules are suggested.
10942933
Estimation of 2D Displacement Field Based on Affine Geometric Invariance and Scene Constraints
In the paper, a novel approach using affine transfer and scene constraint for estimation of 2D displacement field was developed. In this approach, we derived a system of 5 linear equations for computing corresponding point of any image point via the utilisation of affine invariant. Subsequently, the characteristics of such a linear system was thoroughly studied, and the way to obtain a reliable solution of the system via robust least squares (RLS) approach, in conjunction with total least squares technique, was proposed. In addition, through the interpretation of the approach from both geometric point of view and numerical point of view, we gave the limitation of the algorithm. The limitation was then relaxed to a certain extent by using full fundamental matrix. These findings were further verified through the experimental results.
6738443
Web-based Support Systems for Sustainable Communities
This paper studies Web-based support systems for sustainable communities. A sustainable community is a community that respects the needs of both nature and future generations. A sustainable community activity is a collaborative process of solving environmental, economic and societal problems. We present an architecture of Web-based support systems for sustainable communities. The architecture is based on multitier, component-based structure. Each component provides distinct information services to support sustainable community activities. Users can access these services through standard Web browsers anytime, anywhere. It is argued that a Web-based support system can provide comprehensive and extensible services for diversified sustainable community activities.
18829938
An Enhanced Support Vector Machine Model for Intrusion Detection
Design and implementation of intrusion detection systems remain an important research issue in order to maintain proper network security. Support Vector Machines (SVM) as a classical pattern recognition tool have been widely used for intrusion detection. However, conventional SVM methods do not concern different characteristics of features in building an intrusion detection system. We propose an enhanced SVM model with a weighted kernel function based on features of the training data for intrusion detection. Rough set theory is adopted to perform a feature ranking and selection task of the new model. We evaluate the new model with the KDD dataset and the UNM dataset. It is suggested that the proposed model outperformed the conventional SVM in precision, computation time, and false negative rate