2364735
Embodied categorisation for vision-guided mobile robots
This paper outlines a philosophical and psycho-physiological basis for embodied perception, and develops a framework for conceptual embodiment of vision-guided robots. We argue that categorisation is important in all stages of robot vision. Further, that classical computer vision is unsuitable for this categorisation, however, through conceptual embodiment, active perception can be effective. We present a methodology for developing vision-guided robots that applies embodiment, explicitly and implicitly, in categorising visual data to facilitate efficient perception and action. Finally, we present systems developed using this methodology, and demonstrate that embodied categorisation can make algorithms more efficient and robust.
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.
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.
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.
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.
18154847
Dynamic domination in fuzzy causal networks
This paper presents a dynamic domination theory for fuzzy causal networks (FCN). There are three major contributions. First, we propose a new inference procedure based on dominating sets. Second, we introduce the concepts of dynamic and minimal dynamic dominating sets (DDS and MDDS) in an FCN. To reflect changes of dominance with time, we also introduce the concept of a dynamic dominating process (DDP) that has significant implications in many real-world problems. We pay a special attention to the minimal dynamic dominating process (MDDP) and develop rules for generating DDP and MDDP. Third, we investigate dynamic dominating sets with extended feedback, which we call effective dynamic dominating sets (EDDS), and related effective dynamic dominating process (EDDP). This study unveils a very important phenomenon in FCN: At any time t, either an EDDS exists or there is a dramatic change of the states of vertices. In the latter case we also identify the special structure of the sub-FCN induced by active vertices.
11533242
Multi-robot mobility enhanced hop-count based localization in ad hoc networks
The localization problem is important in mobile robots and wireless sensor network and has been studied for many years. Among many localization methods, the hop-count based approach is simple and scalable; however, the localization accuracy is not satisfactory if the node density is low. To solve this problem, in this paper a multi-robot approach is proposed to utilize the cooperation and mobility of the robots to improve the node distribution (density), thus enhancing the hop-count based localization. By an auction-based task allocation scheme, the robots can negotiate with the static sensor nodes and then select the most suitable robots to move to the area of sparse node density, thus increasing the localization accuracy for the static sensor nodes. On the other hand, the robots also can localize themselves with the help of the static sensor nodes. The efficacy of this approach is shown by simulation.
34215309
Jini-enabled high performance computing
The vision of computational grids promises an exciting future for high performance computing (HPC). In the project presented we make a small but practical step toward the grand vision of pervasive HPC by using some of the prevailing Internet technologies to enable access of HPC anytime and anywhere. Specifically, the project focuses on HPC that could be accessed by using the World Wide Web. We have conducted experiments based on engineering/scientific applications. Examples include simulations of automated guided vehicles (hereafter AGVs for short) in container port operations, N-body and molecular simulations. We initially build systems based on an ad-hoc approach, whose merits and demerits are identified. Then, we employ Jini technology to reconstruct the system architecture and present a more efficient and robust model. Performance studies are also presented. The objectives are to explore and address relevant issues, evaluate various approaches, demonstrate a workable version and conduct experiments with actual Web-enabled applications. The result is that now we are able to partially realize the dream of using the HPCs anytime and anywhere through devices such as a wireless/mobile computer, as long as they are armed with a common Web browser.
5135154
Linking UML models of design and requirement
Both a UML model of requirement and a UML model of a design are defined as a pair of class diagram and a family of sequence diagrams. We then give an unified semantics for models of requirements and designs. We define the consistency between a design class diagram and the interaction diagrams and show how the removal of inconsistency can be treated as a model refinement. We then formally define the correctness of UML model of design with respect to the model of requirement.
17111860
Texture segmentation using directional empirical mode decomposition
In this paper the technique of directional empirical mode decomposition (DEMD) and its application to texture segmentation are presented. Empirical mode decomposition (EMD) decomposes signals by sifting and then analyzes the instantaneous frequency of the obtained components called intrinsic mode functions (IMF). As a new form of extending 1D EMD to the 2D case, DEMD considers the directional frequency and envelope at each point. One type of 2D Hilbert transform is introduced to compute the analytical functions for the frequency and envelope. The technique of selecting directions for DEMD based on texture's Wold theory is also presented. Experimental results indicate the effectiveness of the method for texture segmentation.
4908568
Insignificant shadow detection for video segmentation
To prevent moving cast shadows from being misunderstood as part of moving objects in change detection based video segmentation, this paper proposes a novel approach to the cast shadow detection based on the edge and region information in multiple frames. First, an initial change detection mask containing moving objects and cast shadows is obtained. Then a Canny edge map is generated. After that, the shadow region is detected and removed through multiframe integration, edge matching, and region growing. Finally, a post processing procedure is used to eliminate noise and tune the boundaries of the objects. Our approach can be used for video segmentation in indoor environment. The experimental results demonstrate its good performance.