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.
10848842
Vision Guided Circumnavigating Autonomous Robots
In this paper, we propose a system for vision guided autonomous circumnavigation, allowing robots to navigate around objects of arbitrary pose. The system performs knowledge-based object recognition from an intensity image using a canonical viewer-centred model. A path planned from a geometric model then guides the robot in circum-navigating the object. This system can be used in many applications where robots have to recognize and manipulate objects of unknown pose and placement. Such applications occur in a variety of contexts such as factory automation, underwater and space exploration, and nuclear power station maintenance. We also define a canonical-view graph to model objects, which is a viewer-centred representation.
207320245
Hidden Markov models with factored Gaussian mixtures densities
We present a factorial representation of Gaussian mixture models for observation densities in hidden Markov models (HMMs), which uses the factorial learning in the HMM framework. We derive the reestimation formulas for estimating the factorized parameters by the Expectation Maximization (EM) algorithm and propose a novel method for initializing them. To compare the performances of the proposed models with that of the factorial hidden Markov models and HMMs, we have carried out extensive experiments which show that this modelling approach is effective and robust.
7502395
Dynamic image sequence analysis using fuzzy measures
In this paper, we present an image understanding system using fuzzy sets and fuzzy measures. This system is based on a symbolic object-oriented image interpretation system. We apply a simple, powerful three-dimensional (3-D) recursive filter to tracking moving objects in a dynamic image sequence. This filter has a time-varying 3-D frequency-planar passband that is adapted in a feedback system to automatically track moving objects. However, as objects in the image sequence are not well-defined and are engaged in dynamic activities, their shapes and trajectories in most cases can be described only vaguely. In order to handle these uncertainties, we use fuzzy measures to capture subtle variations and manage the uncertainties involved. This enables us to develop an image understanding system that produces a very natural output. We demonstrate the effectiveness of our system with complex real traffic scenes.
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.
45372122
Interrogating the structure of fuzzy cognitive maps
Abstract Causal algebra in fuzzy cognitive maps (FCMs) plays a critical role in the analysis and design of FCMs. Improving causal algebra in FCMs to model complicated situations has been one of the major research topics in this area. In this paper we propose a dynamic causal algebra in FCMs which can improve FCMs' inference and representation capability. The dynamic causal algebra shows that the indirect, strongest, weakest and total effects a vertex influences another in the FCM not only depend on the weights along all directed paths between the two vertices but also the states of the vertices on the directed paths. Therefore, these effects are nonlinear dynamic processes determined by initial conditions and propagated in the FCM to reach a static or cyclic pattern. We test our theory with a simple example.
14342655
Towards a Rigorous Approach to UML-Based Development
We discuss the promises and problems of UML-based development. We then suggest a framework in which UML can be used precisely and more disciplined so as to solve the problems and meet the promises better.
5176095
Pattern-based texture metamorphosis
In this paper we study texture metamorphosis, or how to generate texture samples that smoothly transform from a source texture image to a target. We propose a pattern-based approach to specify the feature correspondence between two textures, based on the observation that man), texture images have stochastically distributed patterns which are similar to each other First, the user selects a pattern in the source and target textures, and establishes the "local feature correspondence" between these two patterns by specifying landmarks. Then, repeated patterns are automatically detected and localized in the source and target textures. The "pattern correspondence" between two textures is formulated as an integer programming problem and solved using the Hungarian algorithm. Finally, we obtain a warp function between two textures by combining "local feature correspondence" and "pattern correspondence". Experiments demonstrate that our technique produces visually appealing morphing sequences, with moderate amount of user interaction.
3758350
An Analytical Approach to the Performance Evaluation of Master-Slave Computational Models
Abstract Many stochastic models and analysis techniques have been proposed in the literature during the last two decades for the performance evaluation of parallel and distributed systems. However, few of them are directly applicable to practical systems which are generally too complex. In this paper, we analyze performances of the master–slave computational model, one of the most commonly used models in parallel and distributed computations. We propose a hybrid analytical approach by using techniques from the theories of both stochastic task graphs and queueing networks. We apply this method to the analysis of a computational chemistry application running on a Transputer based system. The proposed method turns out to be not only very efficient in time for large systems but also very accurate compared to measuring.
29521719
Improving the Stability Characteristics of Asynchronous Traffic in FDDI Token Ring
The Fiber Distributed Data Interface (FDDI) token ring protocol supports two classes of traffic : synchronous and asynchronous. The time constraint mechanism of FDDI garantees the transmission delay of synchronous traffic. A Target Token Rotation Time (TTRT) being fixed, the FDDI protocol ensures that the token rotation time is always bounded above by twice TTRT. In this paper, we consider the stability issues of the asynchronous traffic in the FDDI protocol. We obtain sufficient and necessary stability conditions which indicates how the choice of TTRT affects the stability of the system. We suggest an improvement of the FDDI protocol for which the token rotation time is also bounded by twice TTRT. We show that the stability conditions of the new protocol are weaker (and thus it enables to transmit more asynchronous packets). This protocol is also easier to implement.
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.