Scheduled System Maintenance:
Some services will be unavailable Sunday, March 29th through Monday, March 30th. We apologize for the inconvenience.
By Topic

Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on

Issue 1 • Date Feb. 2009

Filter Results

Displaying Results 1 - 25 of 31
  • Table of contents

    Publication Year: 2009 , Page(s): C1 - 1
    Save to Project icon | Request Permissions | PDF file iconPDF (147 KB)  
    Freely Available from IEEE
  • IEEE Transactions on Systems, Man, and Cybernetics—Part B: Cybernetics publication information

    Publication Year: 2009 , Page(s): C2
    Save to Project icon | Request Permissions | PDF file iconPDF (37 KB)  
    Freely Available from IEEE
  • Editorial

    Publication Year: 2009 , Page(s): 2
    Save to Project icon | Request Permissions | PDF file iconPDF (27 KB) |  | HTML iconHTML  
    Freely Available from IEEE
  • Special Issue on Human Computing

    Publication Year: 2009 , Page(s): 3 - 6
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | PDF file iconPDF (97 KB) |  | HTML iconHTML  
    Freely Available from IEEE
  • Audio–Visual Active Speaker Tracking in Cluttered Indoors Environments ^{\ast }

    Publication Year: 2009 , Page(s): 7 - 15
    Cited by:  Papers (6)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (372 KB) |  | HTML iconHTML  

    We propose a system for detecting the active speaker in cluttered and reverberant environments where more than one person speaks and moves. Rather than using only audio information, the system utilizes audiovisual information from multiple acoustic and video sensors that feed separate audio and video tracking modules. The audio module operates using a particle filter (PF) and an information-theoretic framework to provide accurate acoustic source location under reverberant conditions. The video subsystem combines in 3-D a number of 2-D trackers based on a variation of Stauffer's adaptive background algorithm with spatiotemporal adaptation of the learning parameters and a Kalman tracker in a feedback configuration. Extensive experiments show that gains are to be expected when fusion of the separate modalities is performed to detect the active speaker. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Recognizing Visual Focus of Attention From Head Pose in Natural Meetings

    Publication Year: 2009 , Page(s): 16 - 33
    Cited by:  Papers (11)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2087 KB) |  | HTML iconHTML  

    We address the problem of recognizing the visual focus of attention (VFOA) of meeting participants based on their head pose. To this end, the head pose observations are modeled using a Gaussian mixture model (GMM) or a hidden Markov model (HMM) whose hidden states correspond to the VFOA. The novelties of this paper are threefold. First, contrary to previous studies on the topic, in our setup, the potential VFOA of a person is not restricted to other participants only. It includes environmental targets as well (a table and a projection screen), which increases the complexity of the task, with more VFOA targets spread in the pan as well as tilt gaze space. Second, we propose a geometric model to set the GMM or HMM parameters by exploiting results from cognitive science on saccadic eye motion, which allows the prediction of the head pose given a gaze target. Third, an unsupervised parameter adaptation step not using any labeled data is proposed, which accounts for the specific gazing behavior of each participant. Using a publicly available corpus of eight meetings featuring four persons, we analyze the above methods by evaluating, through objective performance measures, the recognition of the VFOA from head pose information obtained either using a magnetic sensor device or a vision-based tracking system. The results clearly show that in such complex but realistic situations, the VFOA recognition performance is highly dependent on how well the visual targets are separated for a given meeting participant. In addition, the results show that the use of a geometric model with unsupervised adaptation achieves better results than the use of training data to set the HMM parameters. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Group Interaction Analysis in Dynamic Context ^{\ast }

    Publication Year: 2009 , Page(s): 34 - 42
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1067 KB) |  | HTML iconHTML  

    Computer understanding of human actions and interactions is one of the key research issues in human computing. In this regard, context plays an essential role in semantic understanding of human behavioral and social signals from sensor data. This paper put forward an event-based dynamic context model to address the problems of context awareness in the analysis of group interaction scenarios. Event-driven multilevel dynamic Bayesian network is correspondingly proposed to detect multilevel events, which underlies the context awareness mechanism. Online analysis can be achieved, which is superior over previous works. Experiments in our smart meeting room demonstrate the effectiveness of our approach. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Sensible Organizations: Technology and Methodology for Automatically Measuring Organizational Behavior

    Publication Year: 2009 , Page(s): 43 - 55
    Cited by:  Papers (36)  |  Patents (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (617 KB) |  | HTML iconHTML  

    We present the design, implementation, and deployment of a wearable computing platform for measuring and analyzing human behavior in organizational settings. We propose the use of wearable electronic badges capable of automatically measuring the amount of face-to-face interaction, conversational time, physical proximity to other people, and physical activity levels in order to capture individual and collective patterns of behavior. Our goal is to be able to understand how patterns of behavior shape individuals and organizations. By using on-body sensors in large groups of people for extended periods of time in naturalistic settings, we have been able to identify, measure, and quantify social interactions, group behavior, and organizational dynamics. We deployed this wearable computing platform in a group of 22 employees working in a real organization over a period of one month. Using these automatic measurements, we were able to predict employees' self-assessments of job satisfaction and their own perceptions of group interaction quality by combining data collected with our platform and e-mail communication data. In particular, the total amount of communication was predictive of both of these assessments, and betweenness in the social network exhibited a high negative correlation with group interaction satisfaction. We also found that physical proximity and e-mail exchange had a negative correlation of r = -0.55 (p 0.01), which has far-reaching implications for past and future research on social networks. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Learning Situation Models in a Smart Home

    Publication Year: 2009 , Page(s): 56 - 63
    Cited by:  Papers (18)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1399 KB) |  | HTML iconHTML  

    This paper addresses the problem of learning situation models for providing context-aware services. Context for modeling human behavior in a smart environment is represented by a situation model describing environment, users, and their activities. A framework for acquiring and evolving different layers of a situation model in a smart environment is proposed. Different learning methods are presented as part of this framework: role detection per entity, unsupervised extraction of situations from multimodal data, supervised learning of situation representations, and evolution of a predefined situation model with feedback. The situation model serves as frame and support for the different methods, permitting to stay in an intuitive declarative framework. The proposed methods have been integrated into a whole system for smart home environment. The implementation is detailed, and two evaluations are conducted in the smart home environment. The obtained results validate the proposed approach. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Automatic Temporal Segment Detection and Affect Recognition From Face and Body Display

    Publication Year: 2009 , Page(s): 64 - 84
    Cited by:  Papers (17)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1478 KB) |  | HTML iconHTML  

    Psychologists have long explored mechanisms with which humans recognize other humans' affective states from modalities, such as voice and face display. This exploration has led to the identification of the main mechanisms, including the important role played in the recognition process by the modalities' dynamics. Constrained by the human physiology, the temporal evolution of a modality appears to be well approximated by a sequence of temporal segments called onset, apex, and offset. Stemming from these findings, computer scientists, over the past 15 years, have proposed various methodologies to automate the recognition process. We note, however, two main limitations to date. The first is that much of the past research has focused on affect recognition from single modalities. The second is that even the few multimodal systems have not paid sufficient attention to the modalities' dynamics: The automatic determination of their temporal segments, their synchronization to the purpose of modality fusion, and their role in affect recognition are yet to be adequately explored. To address this issue, this paper focuses on affective face and body display, proposes a method to automatically detect their temporal segments or phases, explores whether the detection of the temporal phases can effectively support recognition of affective states, and recognizes affective states based on phase synchronization/alignment. The experimental results obtained show the following: 1) affective face and body displays are simultaneous but not strictly synchronous; 2) explicit detection of the temporal phases can improve the accuracy of affect recognition; 3) recognition from fused face and body modalities performs better than that from the face or the body modality alone; and 4) synchronized feature-level fusion achieves better performance than decision-level fusion. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • A Novel Active Heads-Up Display for Driver Assistance

    Publication Year: 2009 , Page(s): 85 - 93
    Cited by:  Papers (27)  |  Patents (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1406 KB) |  | HTML iconHTML  

    In this paper, we introduce a novel laser-based wide-area heads-up windshield display which is capable of actively interfacing with a human as part of a driver assistance system. The dynamic active display (DAD) is a unique prototype interface that presents safety-critical visual icons to the driver in a manner that minimizes the deviation of his or her gaze direction without adding to unnecessary visual clutter. As part of an automotive safety system, the DAD presents alerts in the field of view of the driver only if necessary, which is based upon the state and pose of the driver, vehicle, and environment. This paper examines the effectiveness of DAD through a comprehensive comparative experimental evaluation of a speed compliance driver assistance system, which is implemented on a vehicular test bed. Three different types of display protocols for assisting a driver to comply with speed limits are tested on actual roadways, and these are compared with a conventional dashboard display. Given the inclination, drivers who are given an overspeed warning alert reduced the time required to slow down to the speed limit by 38% (p < 0.01) as compared with the drivers not given the alert. Additionally, certain alerts decreased distraction levels by reducing the time spent looking away from the road by 63% (p < 0.01). Ultimately, these alerts demonstrate the utility and promise of the DAD system. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Robust Stability for Uncertain Delayed Fuzzy Hopfield Neural Networks With Markovian Jumping Parameters

    Publication Year: 2009 , Page(s): 94 - 102
    Cited by:  Papers (89)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (199 KB) |  | HTML iconHTML  

    This paper is concerned with the problem of the robust stability of nonlinear delayed Hopfield neural networks (HNNs) with Markovian jumping parameters by Takagi-Sugeno (T-S) fuzzy model. The nonlinear delayed HNNs are first established as a modified T-S fuzzy model in which the consequent parts are composed of a set of Markovian jumping HNNs with interval delays. Time delays here are assumed to be time-varying and belong to the given intervals. Based on Lyapunov-Krasovskii stability theory and linear matrix inequality approach, stability conditions are proposed in terms of the upper and lower bounds of the delays. Finally, numerical examples are used to illustrate the effectiveness of the proposed method. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Robust Adaptive Control of Cooperating Mobile Manipulators With Relative Motion

    Publication Year: 2009 , Page(s): 103 - 116
    Cited by:  Papers (10)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (710 KB) |  | HTML iconHTML  

    In this paper, coupled dynamics are presented for two cooperating mobile robotic manipulators manipulating an object with relative motion in the presence of uncertainties and external disturbances. Centralized robust adaptive controls are introduced to guarantee the motion, and force trajectories of the constrained object converge to the desired manifolds with prescribed performance. The stability of the closed-loop system and the boundedness of tracking errors are proved using Lyapunov stability synthesis. The tracking of the constraint trajectory/force up to an ultimately bounded error is achieved. The proposed adaptive controls are robust against relative motion disturbances and parametric uncertainties and are validated by simulation studies. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Generalized Rough Sets, Entropy, and Image Ambiguity Measures

    Publication Year: 2009 , Page(s): 117 - 128
    Cited by:  Papers (16)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (600 KB) |  | HTML iconHTML  

    Quantifying ambiguities in images using fuzzy set theory has been of utmost interest to researchers in the field of image processing. In this paper, we present the use of rough set theory and its certain generalizations for quantifying ambiguities in images and compare it to the use of fuzzy set theory. We propose classes of entropy measures based on rough set theory and its certain generalizations, and perform rigorous theoretical analysis to provide some properties which they satisfy. Grayness and spatial ambiguities in images are then quantified using the proposed entropy measures. We demonstrate the utility and effectiveness of the proposed entropy measures by considering some elementary image processing applications. We also propose a new measure called average image ambiguity in this context. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Dual Adaptive Dynamic Control of Mobile Robots Using Neural Networks

    Publication Year: 2009 , Page(s): 129 - 141
    Cited by:  Papers (11)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (685 KB) |  | HTML iconHTML  

    This paper proposes two novel dual adaptive neural control schemes for the dynamic control of nonholonomic mobile robots. The two schemes are developed in discrete time, and the robot's nonlinear dynamic functions are assumed to be unknown. Gaussian radial basis function and sigmoidal multilayer perceptron neural networks are used for function approximation. In each scheme, the unknown network parameters are estimated stochastically in real time, and no preliminary offline neural network training is used. In contrast to other adaptive techniques hitherto proposed in the literature on mobile robots, the dual control laws presented in this paper do not rely on the heuristic certainty equivalence property but account for the uncertainty in the estimates. This results in a major improvement in tracking performance, despite the plant uncertainty and unmodeled dynamics. Monte Carlo simulation and statistical hypothesis testing are used to illustrate the effectiveness of the two proposed stochastic controllers as applied to the trajectory-tracking problem of a differentially driven wheeled mobile robot. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Intelligent Robust Tracking Control for a Class of Uncertain Strict-Feedback Nonlinear Systems

    Publication Year: 2009 , Page(s): 142 - 155
    Cited by:  Papers (8)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (351 KB) |  | HTML iconHTML  

    This paper addresses the problem of designing robust tracking controls for a large class of strict-feedback nonlinear systems involving plant uncertainties and external disturbances. The input and virtual input weighting matrices are perturbed by bounded time-varying uncertainties. An adaptive fuzzy-based (or neural-network-based) dynamic feedback tracking controller will be developed such that all the states and signals of the closed-loop system are bounded and the trajectory tracking error should be as small as possible. First, the adaptive approximators with linearly parameterized models are designed, and a partitioned procedure with respect to the developed adaptive approximators is proposed such that the implementation of the fuzzy (or neural network) basis functions depends only on the state variables but does not depend on the tuning approximation parameters. Furthermore, we extend to design the nonlinearly parameterized adaptive approximators. Consequently, the intelligent robust tracking control schemes developed in this paper possess the properties of computational simplicity and easy implementation. Finally, simulation examples are presented to demonstrate the effectiveness of the proposed control algorithms. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • An Optimal-Control Model of Vision–Gait Interaction in a Virtual Walkway

    Publication Year: 2009 , Page(s): 156 - 166
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (879 KB) |  | HTML iconHTML  

    The specific aim of this paper is to model the vision-posture coupling behavior, which is important for astronauts to stabilize their locomotion in partial gravities as the national aeronautics and space administration plans for manned missions to the moon and mars . As such, an optimal scheme is assumed in postural-control processes to stabilize visual optical flows. An experiment was conducted, in which human subjects attended a visual-gait tracking task. In tracking control, head position errors can be used to regulate inputs so that appropriate compensatory changes can be obtained. The ldquooptimalrdquo scheme describes a compromise between postural adjusting efforts and tracking errors. The results show that the proposed optimal-control model describes the gait tracking process more reliably than McRuer's crossover model of the human-plant compensatory behaviors. In practice, if the tracking goal is to be roughly right rather than precisely wrong, this paper also provides the experimental data regarding the human tolerance and achievable performance under various unloading conditions and tracking difficulties. This information and related experimental setup could also be applied to postsurgery gait rehabilitation. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Multisensor-Based Human Detection and Tracking for Mobile Service Robots

    Publication Year: 2009 , Page(s): 167 - 181
    Cited by:  Papers (54)
    Multimedia
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1496 KB) |  | HTML iconHTML  

    One of fundamental issues for service robots is human-robot interaction. In order to perform such a task and provide the desired services, these robots need to detect and track people in the surroundings. In this paper, we propose a solution for human tracking with a mobile robot that implements multisensor data fusion techniques. The system utilizes a new algorithm for laser-based leg detection using the onboard laser range finder (LRF). The approach is based on the recognition of typical leg patterns extracted from laser scans, which are shown to also be very discriminative in cluttered environments. These patterns can be used to localize both static and walking persons, even when the robot moves. Furthermore, faces are detected using the robot's camera, and the information is fused to the legs' position using a sequential implementation of unscented Kalman filter. The proposed solution is feasible for service robots with a similar device configuration and has been successfully implemented on two different mobile platforms. Several experiments illustrate the effectiveness of our approach, showing that robust human tracking can be performed within complex indoor environments. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • A Constraint-Based Evolutionary Learning Approach to the Expectation Maximization for Optimal Estimation of the Hidden Markov Model for Speech Signal Modeling

    Publication Year: 2009 , Page(s): 182 - 197
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (941 KB) |  | HTML iconHTML  

    This paper attempts to overcome the tendency of the expectation-maximization (EM) algorithm to locate a local rather than global maximum when applied to estimate the hidden Markov model (HMM) parameters in speech signal modeling. We propose a hybrid algorithm for estimation of the HMM in automatic speech recognition (ASR) using a constraint-based evolutionary algorithm (EA) and EM, the CEL-EM. The novelty of our hybrid algorithm (CEL-EM) is that it is applicable for estimation of the constraint-based models with many constraints and large numbers of parameters (which use EM) like HMM. Two constraint-based versions of the CEL-EM with different fusion strategies have been proposed using a constraint-based EA and the EM for better estimation of HMM in ASR. The first one uses a traditional constraint-handling mechanism of EA. The other version transforms a constrained optimization problem into an unconstrained problem using Lagrange multipliers. Fusion strategies for the CEL-EM use a staged-fusion approach where EM has been plugged with the EA periodically after the execution of EA for a specific period of time to maintain the global sampling capabilities of EA in the hybrid algorithm. A variable initialization approach (VIA) has been proposed using a variable segmentation to provide a better initialization for EA in the CEL-EM. Experimental results on the TIMIT speech corpus show that CEL-EM obtains higher recognition accuracies than the traditional EM algorithm as well as a top-standard EM (VIA-EM, constructed by applying the VIA to EM). View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • BLGAN: Bayesian Learning and Genetic Algorithm for Supporting Negotiation With Incomplete Information

    Publication Year: 2009 , Page(s): 198 - 211
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1050 KB) |  | HTML iconHTML  

    Automated negotiation provides a means for resolving differences among interacting agents. For negotiation with complete information, this paper provides mathematical proofs to show that an agent's optimal strategy can be computed using its opponent's reserve price (RP) and deadline. The impetus of this work is using the synergy of Bayesian learning (BL) and genetic algorithm (GA) to determine an agent's optimal strategy in negotiation (N) with incomplete information. BLGAN adopts: (1) BL and a deadline-estimation process for estimating an opponent's RP and deadline and (2) GA for generating a proposal at each negotiation round. Learning the RP and deadline of an opponent enables the GA in BLGAN to reduce the size of its search space (SP) by adaptively focusing its search on a specific region in the space of all possible proposals. SP is dynamically defined as a region around an agent's proposal P at each negotiation round. P is generated using the agent's optimal strategy determined using its estimations of its opponent's RP and deadline. Hence, the GA in BLGAN is more likely to generate proposals that are closer to the proposal generated by the optimal strategy. Using GA to search around a proposal generated by its current strategy, an agent in BLGAN compensates for possible errors in estimating its opponent's RP and deadline. Empirical results show that agents adopting BLGAN reached agreements successfully, and achieved: (1) higher utilities and better combined negotiation outcomes (CNOs) than agents that only adopt GA to generate their proposals, (2) higher utilities than agents that adopt BL to learn only RP, and (3) higher utilities and better CNOs than agents that do not learn their opponents' RPs and deadlines. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Robust Navigation in an Unknown Environment With Minimal Sensing and Representation

    Publication Year: 2009 , Page(s): 212 - 229
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1686 KB) |  | HTML iconHTML  

    This paper presents muNav, a novel approach to navigation which, with minimal requirements in terms of onboard sensory, memory, and computational power, exhibits way-finding behaviors in very complex environments. The algorithm is intrinsically robust, since it does not require any internal geometrical representation or self-localization capabilities. Experimental results, performed with both simulated and real robots, validate the proposed theoretical approach. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Local Synchronization of a Complex Network Model

    Publication Year: 2009 , Page(s): 230 - 241
    Cited by:  Papers (48)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (415 KB) |  | HTML iconHTML  

    This paper introduces a novel complex network model to evaluate the reputation of virtual organizations. By using the Lyapunov function and linear matrix inequality approaches, the local synchronization of the proposed model is further investigated. Here, the local synchronization is defined by the inner synchronization within a group which does not mean the synchronization between different groups. Moreover, several sufficient conditions are derived to ensure the local synchronization of the proposed network model. Finally, several representative examples are given to show the effectiveness of the proposed methods and theories. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Dynamic View Planning by Effective Particles for Three-Dimensional Tracking

    Publication Year: 2009 , Page(s): 242 - 253
    Cited by:  Papers (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1495 KB) |  | HTML iconHTML  

    In this paper, we propose a new approach to dynamically manage the viewpoint of a vision system for optimal 3-D tracking using particle techniques. We adopt the effective sample size in the proposed particle filter as a criterion for evaluating tracking performance and employ it to guide the view-planning process for finding the best viewpoint configuration. In our approach, the vision system is designed and configured to achieve the largest number of effective particles, which minimizes tracking error by revealing the system to a better swarm of importance samples and interpreting posterior states in a better way. Superiorities of our method are shown by comparison with the resampling particle filter and other view-planning methods. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Evolving Logic Networks With Real-Valued Inputs for Fast Incremental Learning

    Publication Year: 2009 , Page(s): 254 - 267
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1229 KB) |  | HTML iconHTML  

    In this paper, we present a neural network structure and a fast incremental learning algorithm using this network. The proposed network structure, named evolving logic networks for real-valued inputs (ELN-R), is a data structure for storing and using the knowledge. A distinctive feature of ELN-R is that the previously learned knowledge stored in ELN-R can be used as a kind of building block in constructing new knowledge. Using this feature, the proposed learning algorithm can enhance the stability and plasticity at the same time, and as a result, the fast incremental learning can be realized. The performance of the proposed scheme is shown by a theoretical analysis and an experimental study on two benchmark problems. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Learning Atomic Human Actions Using Variable-Length Markov Models

    Publication Year: 2009 , Page(s): 268 - 280
    Cited by:  Papers (12)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1417 KB) |  | HTML iconHTML  

    Visual analysis of human behavior has generated considerable interest in the field of computer vision because of its wide spectrum of potential applications. Human behavior can be segmented into atomic actions, each of which indicates a basic and complete movement. Learning and recognizing atomic human actions are essential to human behavior analysis. In this paper, we propose a framework for handling this task using variable-length Markov models (VLMMs). The framework is comprised of the following two modules: a posture labeling module and a VLMM atomic action learning and recognition module. First, a posture template selection algorithm, based on a modified shape context matching technique, is developed. The selected posture templates form a codebook that is used to convert input posture sequences into discrete symbol sequences for subsequent processing. Then, the VLMM technique is applied to learn the training symbol sequences of atomic actions. Finally, the constructed VLMMs are transformed into hidden Markov models (HMMs) for recognizing input atomic actions. This approach combines the advantages of the excellent learning function of a VLMM and the fault-tolerant recognition ability of an HMM. Experiments on realistic data demonstrate the efficacy of the proposed system. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.

Aims & Scope

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics focuses on cybernetics, including communication and control across humans, machines and organizations at the structural or neural level

 

This Transaction ceased production in 2012. The current retitled publication is IEEE Transactions on Cybernetics.

Full Aims & Scope

Meet Our Editors

Editor-in-Chief
Dr. Eugene Santos, Jr.
Thayer School of Engineering
Dartmouth College