10944826
Recognition of Dynamic Video Contents With Global Probabilistic Models of Visual Motion
The exploitation of video data requires methods able to extract high-level information from the images. Video summarization, video retrieval, or video surveillance are examples of applications. In this paper, we tackle the challenging problem of recognizing dynamic video contents from low-level motion features. We adopt a statistical approach involving modeling, (supervised) learning, and classification issues. Because of the diversity of video content (even for a given class of events), we have to design appropriate models of visual motion and learn them from videos. We have defined original parsimonious global probabilistic motion models, both for the dominant image motion (assumed to be due to the camera motion) and the residual image motion (related to scene motion). Motion measurements include affine motion models to capture the camera motion and low-level local motion features to account for scene motion. Motion learning and recognition are solved using maximum likelihood criteria. To validate the interest of the proposed motion modeling and recognition framework, we report dynamic content recognition results on sports videos
8612227
Mixed-State Markov Random Fields for Motion Texture Modeling and Segmentation
The aim of this work is to model the apparent motion in image sequences depicting natural dynamic scenes. We adopt the mixed-state Markov random fields (MRF) models recently introduced to represent so-called motion textures. The approach consists in describing the spatial distribution of some motion measurements which exhibit mixed-state nature: a discrete component related to the absence of motion and a continuous part for measurements different from zero. We propose several significative extensions to this model. We define an original motion texture segmentation method which does not assume conditional independence of the observations for each texture and normalizing factors are properly handled. Results on real examples demonstrate the accuracy and efficiency of our method.
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.
41338736
An approach to identification of variances for radar tracking systems
An approach for the identification of the variances of the process noise W(k) and the measurement noise V(k) for radar tracking systems is presented. In the proposed algorithm, the variances are expressed as a linear combination of the covariances of the third-order difference of the measurement data. The variances can then be estimated by estimating the covariances of the third-order difference and solving the matrix equation. The two cases for the measurement noise being white and color are considered in this algorithm. Simulation results show that for both cases a steady estimation is reached after 20 samples.
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.
14229561
Time dependent directional profit model for financial time series forecasting
Goodness-of-fit is the most popular criterion for neural network time series forecasting. In the context of financial time series forecasting, we are not only concerned at how good the forecasts fit their targets, but we are more interested in profits. In order to increase the forecastability in terms of profit earning, we propose a profit based adjusted weight factor for backpropagation network training. Instead of using the traditional least squares error, we add a factor which contains the profit, direction, and time information to the error function. The results show that this new approach does improve the forecastability of neural network models, for the financial application domain.