17193716
Detecting news reporting using audio/visual information
This paper proposes an integrated approach to discriminate news reporting from everything else in broadcast news data based on both audio and visual information. The separation of news reporting segments from others not only can provide useful indices for video streams but also serves as a pre-processing step for tasks such as speaker identification and speech recognition so that only speech segments are passed on for further processing. A set of audio and visual features are adopted that aim at capturing the intrinsic properties of the underlying classes. Four types of classifiers (threshold fuzzy, Gaussian Mixture Model based, and Support Vector Machine) are tested. Some of the experimental results are presented and discussed in the paper.
11845096
Integration of audio and visual information for content-based video segmentation
A video sequence usually consists of separate scenes, and each scene includes many shots. For video understanding purposes, it is most important to detect scene breaks. To analyze the content of each scene, detection of shot breaks is also required. Usually, a scene break is associated with a simultaneous change of image, motion, and audio characteristics, while a shot break is only accompanied with changes in image or motion or both. We propose to use audio information along with image and motion information to accomplish segmentation at different levels. Promising results have been obtained with videos digitized from TV programs.
28562
Content-based indexing and retrieval-by-example in audio
This paper presents a system for audio content-based indexing and retrieval-by-example. An unsupervised method to automatically index the audio content is proposed. To realize a retrieval system where the on-line query can be performed efficiently, a new parametric distance metric is used to allow faster identification of the audio segments that are similar to the given query example. Preliminary experimental results show that the proposed new approach can serve as an effective means in content based indexing and retrieval in audio streams.
28594120
Pattern recognition using Markov random field models
In this paper, we propose Markov random field models for pattern recognition, which provide a flexible and natural framework for modelling the interactions between spatially related random variables in their neighbourhood systems. The proposed approach is superior to conventional approaches in many aspects. This paper introduces the concept of states into Markov random filed models, presents a theoretic analysis of the approach, discusses issues of designing neighbourhood system and cliques, and analyses properties of the models. We have applied our method to the recognition of unconstrained handwritten numerals. The experimental results show that the proposed approach can achieve high performance.
14780024
Fuzzy causal networks: general model, inference, and convergence
In this paper, we first propose a general framework for fuzzy causal networks (FCNs). Then, we study the dynamics and convergence of such general FCNs. We prove that any general FCN with constant weight matrix converges to a limit cycle or a static state, or the trajectory of the FCN is not repetitive. We also prove that under certain conditions a discrete state general FCN converges to its limit cycle or static state in O(n) steps, where n is the number of vertices of the FCN. This is in striking contrast with the exponential running time 2/sup n/, which is accepted widely for classic FCNs.
7545798
Verification of fault tolerance and real time
A transformational method is given for specifying and verifying fault-tolerant, real-time programs. Such a program needs to be provably correct according to both its functional and real-time requirements, despite the possible occurrence of system failures. The paper demonstrates that a suitably expressive logic for real-time systems makes it possible to naturally model the state changes caused by system failures and determine their effect on the functional and real-time properties of executions.
7323063
rCOS: Refinement of Component and Object Systems
We present a model of object-oriented and component-based refinement. For object-orientation, the model is class-based and refinement is about correct changes in the structure, methods of classes and the main program, rather than changes in the behaviour of individual objects. This allows us to prove refinement laws for both high level design patterns and low level refactoring. For component-based development, we focus on the separation of concerns of interface and functional contracts, leaving refinement of interaction protocols in future work. The model supports the specification of these aspects at different levels of abstractions and their consistency. 
 
Based on the semantics, we also provide a general definitional approach to defining different relational semantic models with different features and constraints.
1176633
Consistent code generation from UML models
Relational calculus of object systems (rCOS) is an OO-language which is equipped with an observation-oriented semantics and a refinement calculus based on the Hoare and He's unifying theories of programming (UTP). In this paper, we give syntactic definitions for class diagrams and sequence diagrams in UML 2.0. Based on these definitions, we give an algorithm for checking the consistency of a class diagram and a sequence diagram. Furthermore, we develop an algorithm to generate rCOS code from any given consistent class diagram and sequence diagram.