IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)

Issue 3 • June 2005

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Displaying Results 1 - 25 of 31
  • Table of contents

    Publication Year: 2005, Page(s):c1 - 389
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  • IEEE Transactions on Systems, Man, and Cybernetics—Part B: Cybernetics publication information

    Publication Year: 2005, Page(s): c2
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  • Policies and Procedures

    Publication Year: 2005, Page(s): 390
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  • Introduction to the Special Issue on Learning in Computer Vision and Pattern Recognition

    Publication Year: 2005, Page(s):391 - 396
    Cited by:  Papers (9)
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  • Learning semantic scene models from observing activity in visual surveillance

    Publication Year: 2005, Page(s):397 - 408
    Cited by:  Papers (128)  |  Patents (4)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (4501 KB) | HTML iconHTML

    This paper considers the problem of automatically learning an activity-based semantic scene model from a stream of video data. A scene model is proposed that labels regions according to an identifiable activity in each region, such as entry/exit zones, junctions, paths, and stop zones. We present several unsupervised methods that learn these scene elements and present results that show the efficie... View full abstract»

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  • Visual learning by coevolutionary feature synthesis

    Publication Year: 2005, Page(s):409 - 425
    Cited by:  Papers (38)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1600 KB) | HTML iconHTML

    In this paper, a novel genetically inspired visual learning method is proposed. Given the training raster images, this general approach induces a sophisticated feature-based recognition system. It employs the paradigm of cooperative coevolution to handle the computational difficulty of this task. To represent the feature extraction agents, the linear genetic programming is used. The paper describe... View full abstract»

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  • Evolutionary optimization of a hierarchical object recognition model

    Publication Year: 2005, Page(s):426 - 437
    Cited by:  Papers (17)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (825 KB) | HTML iconHTML

    A major problem in designing artificial neural networks is the proper choice of the network architecture. Especially for vision networks classifying three-dimensional (3D) objects this problem is very challenging, as these networks are necessarily large and therefore the search space for defining the needed networks is of a very high dimensionality. This strongly increases the chances of obtaining... View full abstract»

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  • Visual learning by imitation with motor representations

    Publication Year: 2005, Page(s):438 - 449
    Cited by:  Papers (46)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1832 KB) | HTML iconHTML

    We propose a general architecture for action (mimicking) and program (gesture) level visual imitation. Action-level imitation involves two modules. The viewpoint transformation (VPT) performs a "rotation" to align the demonstrator's body to that of the learner. The visuo-motor map (VMM) maps this visual information to motor data. For program-level (gesture) imitation, there is an additional module... View full abstract»

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  • Active concept learning in image databases

    Publication Year: 2005, Page(s):450 - 466
    Cited by:  Papers (16)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1479 KB) | HTML iconHTML

    Concept learning in content-based image retrieval systems is a challenging task. This paper presents an active concept learning approach based on the mixture model to deal with the two basic aspects of a database system: the changing (image insertion or removal) nature of a database and user queries. To achieve concept learning, we a) propose a new user directed semi-supervised expectation-maximiz... View full abstract»

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  • Face detection using spectral histograms and SVMs

    Publication Year: 2005, Page(s):467 - 476
    Cited by:  Papers (54)  |  Patents (3)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (3577 KB) | HTML iconHTML

    We present a face detection method using spectral histograms and support vector machines (SVMs). Each image window is represented by its spectral histogram, which is a feature vector consisting of histograms of filtered images. Using statistical sampling, we show systematically the representation groups face images together; in comparison, commonly used representations often do not exhibit this ne... View full abstract»

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  • Learning from examples in the small sample case: face expression recognition

    Publication Year: 2005, Page(s):477 - 488
    Cited by:  Papers (94)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1893 KB) | HTML iconHTML

    Example-based learning for computer vision can be difficult when a large number of examples to represent each pattern or object class is not available. In such situations, learning from a small number of samples is of practical value. To study this issue, the task of face expression recognition with a small number of training images of each expression is considered. A new technique based on linear... View full abstract»

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  • Kernel pooled local subspaces for classification

    Publication Year: 2005, Page(s):489 - 502
    Cited by:  Papers (6)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1512 KB) | HTML iconHTML

    We investigate the use of subspace analysis methods for learning low-dimensional representations for classification. We propose a kernel-pooled local discriminant subspace method and compare it against competing techniques: kernel principal component analysis (KPCA) and generalized discriminant analysis (GDA) in classification problems. We evaluate the classification performance of the nearest-nei... View full abstract»

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  • Self-organizing maps for learning the edit costs in graph matching

    Publication Year: 2005, Page(s):503 - 514
    Cited by:  Papers (19)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (898 KB) | HTML iconHTML

    Although graph matching and graph edit distance computation have become areas of intensive research recently, the automatic inference of the cost of edit operations has remained an open problem. In the present paper, we address the issue of learning graph edit distance cost functions for numerically labeled graphs from a corpus of sample graphs. We propose a system of self-organizing maps (SOMs) t... View full abstract»

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  • Self-organizing topological tree for online vector quantization and data clustering

    Publication Year: 2005, Page(s):515 - 526
    Cited by:  Papers (20)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1097 KB) | HTML iconHTML

    The self-organizing maps (SOM) introduced by Kohonen implement two important operations: vector quantization (VQ) and a topology-preserving mapping. In this paper, an online self-organizing topological tree (SOTT) with faster learning is proposed. A new learning rule delivers the efficiency and topology preservation, which is superior of other structures of SOMs. The computational complexity of th... View full abstract»

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  • A learning-based method for image super-resolution from zoomed observations

    Publication Year: 2005, Page(s):527 - 537
    Cited by:  Papers (27)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2298 KB) | HTML iconHTML

    We propose a technique for super-resolution imaging of a scene from observations at different camera zooms. Given a sequence of images with different zoom factors of a static scene, we obtain a picture of the entire scene at a resolution corresponding to the most zoomed observation. The high-resolution image is modeled through appropriate parameterization, and the parameters are learned from the m... View full abstract»

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  • Object detection via feature synthesis using MDL-based genetic programming

    Publication Year: 2005, Page(s):538 - 547
    Cited by:  Papers (23)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1439 KB) | HTML iconHTML

    In this paper, we use genetic programming (GP) to synthesize composite operators and composite features from combinations of primitive operations and primitive features for object detection. The motivation for using GP is to overcome the human experts' limitations of focusing only on conventional combinations of primitive image processing operations in the feature synthesis. GP attempts many uncon... View full abstract»

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  • Evolving binary classifiers through parallel computation of multiple fitness cases

    Publication Year: 2005, Page(s):548 - 555
    Cited by:  Papers (7)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (312 KB) | HTML iconHTML

    This paper describes two versions of a novel approach to developing binary classifiers, based on two evolutionary computation paradigms: cellular programming and genetic programming. Such an approach achieves high computation efficiency both during evolution and at runtime. Evolution speed is optimized by allowing multiple solutions to be computed in parallel. Runtime performance is optimized expl... View full abstract»

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  • A criterion for optimizing kernel parameters in KBDA for image retrieval

    Publication Year: 2005, Page(s):556 - 562
    Cited by:  Papers (24)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (459 KB) | HTML iconHTML

    A criterion is proposed to optimize the kernel parameters in kernel-based biased discriminant analysis (KBDA) for image retrieval. Kernel parameter optimization is performed by optimizing the kernel space such that the positive images are well clustered while the negative ones are pushed far away from the positives. The proposed criterion measures the goodness of a kernel space, and the optimal ke... View full abstract»

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  • Image transform bootstrapping and its applications to semantic scene classification

    Publication Year: 2005, Page(s):563 - 570
    Cited by:  Papers (14)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1506 KB) | HTML iconHTML

    The performance of an exemplar-based scene classification system depends largely on the size and quality of its set of training exemplars, which can be limited in practice. In addition, in nontrivial data sets, variations in scene content as well as distracting regions may exist in many testing images to prohibit good matches with the exemplars. Various boosting schemes have been proposed in machi... View full abstract»

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  • EM in high-dimensional spaces

    Publication Year: 2005, Page(s):571 - 577
    Cited by:  Papers (3)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1962 KB) | HTML iconHTML

    This paper considers fitting a mixture of Gaussians model to high-dimensional data in scenarios where there are fewer data samples than feature dimensions. Issues that arise when using principal component analysis (PCA) to represent Gaussian distributions inside Expectation-Maximization (EM) are addressed, and a practical algorithm results. Unlike other algorithms that have been proposed, this alg... View full abstract»

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  • Robust fusion of uncertain information

    Publication Year: 2005, Page(s):578 - 586
    Cited by:  Papers (26)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1309 KB) | HTML iconHTML

    A technique is presented to combine n data points, each available with point-dependent uncertainty, when only a subset of these points come from N≪n sources, where N is unknown. We detect the significant modes of the underlying multivariate probability distribution using a generalization of the nonparametric mean shift procedure. The number of detected modes automatically defines N, while the b... View full abstract»

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  • Recursive three-dimensional model reconstruction based on Kalman filtering

    Publication Year: 2005, Page(s):587 - 592
    Cited by:  Papers (14)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (629 KB) | HTML iconHTML

    A recursive two-step method to recover structure and motion from image sequences based on Kalman filtering is described in this paper. The algorithm consists of two major steps. The first step is an extended Kalman filter (EKF) for the estimation of the object's pose. The second step is a set of EKFs, one for each model point, for the refinement of the positions of the model features in the three-... View full abstract»

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  • A kernel autoassociator approach to pattern classification

    Publication Year: 2005, Page(s):593 - 606
    Cited by:  Papers (25)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (911 KB) | HTML iconHTML

    Autoassociators are a special type of neural networks which, by learning to reproduce a given set of patterns, grasp the underlying concept that is useful for pattern classification. In this paper, we present a novel nonlinear model referred to as kernel autoassociators based on kernel methods. While conventional nonlinear autoassociation models emphasize searching for the nonlinear representation... View full abstract»

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  • Eliminating false matches for the projective registration of free-form surfaces with small translational motions

    Publication Year: 2005, Page(s):607 - 624
    Cited by:  Papers (6)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2514 KB) | HTML iconHTML

    In this paper, we make a detailed study of two rigid-motion constraints. The importance of these two constraints is twofold: first, they reveal the inherent relationship between the three-dimensional-two-dimensional (3-D-2-D) point correspondences and the motion parameters of interest; second, they can be used to measure the traditional ICP criterion established point match qualities based on whic... View full abstract»

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  • Sample-sort simulated annealing

    Publication Year: 2005, Page(s):625 - 632
    Cited by:  Papers (18)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (332 KB) | HTML iconHTML

    A simulated annealing (SA) algorithm called Sample-Sort that is artificially extended across an array of samplers is proposed. The sequence of temperatures for a serial SA algorithm is replaced with an array of samplers operating at static temperatures and the single stochastic sampler is replaced with a set of samplers. The set of samplers uses a biased generator to sample the same distribution o... View full abstract»

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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 Transactions 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