Semantic analysis of field sports video using a petri-net of audio-visual concepts

The most common approach to automatic summarisation and highlight detection in sports video is to train an automatic classifier to detect semantic highlights based on occurrences of low-level features such as action replays, excited commentators or changes in a scoreboard. We propose an alternative approach based on the detection of perception concepts (PCs) and the construction of Petri-Nets which can be used for both semantic description and event detection within sports videos. Low-level algorithms for the detection of perception concepts using visual, aural and motion characteristics are proposed, and a series of Petri-Nets composed of perception concepts is formally defined to describe video content. We call this a Perception Concept Network-Petri Net (PCN-PN) model. Using PCN-PNs, personalized high-level semantic descriptions of video highlights can be facilitated and queries on high-level semantics can be achieved. A particular strength of this framework is that we can easily build semantic detectors based on PCN-PNs to search within sports videos and locate interesting events. Experimental results based on recorded sports video data across three types of sports games (soccer, basketball and rugby), and each from multiple broadcasters, are used to illustrate the potential of this framework

Incremental Integer Linear Programming Models for Petri Nets Reachability Problems

http://www.intechopen.com/books/petri_net_theory_and_application

Scenario-based modeling in industrial information systems

This manuscript addresses the creation of scenario-based models to reason about the behavior of existing industrial information systems. In our approach the system behavior is modeled in two steps that gradually introduce detail and formality. This manuscript addresses the first step, where text-based descriptions, in the form of structured rules, are used to specify how the system is or should be regulated. Those rules can be used to create behavioral snapshots, which are collections of scenario-based descriptions that represent different instances of the system behavior. Snapshots are specified in an intuitive and graphical notation that considers the elements from the problem domain and permit designers to discuss and validate the externally observable behavior, together with the domain experts. In the second step (not fully covered in this manuscript), the system behavior is formalized with an executable model. This formal model, which in our approach is specified using the Colored Petri Net (CP-nets) language, allows the system internal behavior to be animated, simulated, and optimized. The insights gained by experimenting with the formal model can be subsequently used for reengineering the existing system

Building safe software

Murphy is a set of techniques and tools under investigation for their potential in enhancing the safety of software. This paper describes some of the work which has been done and some which is planned

On functional module detection in metabolic networks

Functional modules of metabolic networks are essential for understanding the metabolism of an organism as a whole. With the vast amount of experimental data and the construction of complex and large-scale, often genome-wide, models, the computer-aided identification of functional modules becomes more and more important. Since steady states play a key role in biology, many methods have been developed in that context, for example, elementary flux modes, extreme pathways, transition invariants and place invariants. Metabolic networks can be studied also from the point of view of graph theory, and algorithms for graph decomposition have been applied for the identification of functional modules. A prominent and currently intensively discussed field of methods in graph theory addresses the Q-modularity. In this paper, we recall known concepts of module detection based on the steady-state assumption, focusing on transition-invariants (elementary modes) and their computation as minimal solutions of systems of Diophantine equations. We present the Fourier-Motzkin algorithm in detail. Afterwards, we introduce the Q-modularity as an example for a useful non-steady-state method and its application to metabolic networks. To illustrate and discuss the concepts of invariants and Q-modularity, we apply a part of the central carbon metabolism in potato tubers (Solanum tuberosum) as running example. The intention of the paper is to give a compact presentation of known steady-state concepts from a graph-theoretical viewpoint in the context of network decomposition and reduction and to introduce the application of Q-modularity to metabolic Petri net models

Implicit Incremental Model Analyses and Transformations

When models of a system change, analyses based on them have to be reevaluated in order for the results to stay meaningful. In many cases, the time to get updated analysis results is critical. This thesis proposes multiple, combinable approaches and a new formalism based on category theory for implicitly incremental model analyses and transformations. The advantages of the implementation are validated using seven case studies, partially drawn from the Transformation Tool Contest (TTC)