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Applications of computers to dance.
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Computer Graphics↗
Cellular neural networks for real-time DNA microarray analysis.
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Minimal-memory bit-vector architecture for computational mathematical morphology using subspace projections.
Computational mathematical morphology (CMM) is a nonlinear filter representation particularly amenable to real-time image processing. A windowed, translation-invariant filter is represented by a set of less-than-or-equal decisions that are executed by a parallel arrangement of comparators. In the state-of-the-art implementation, each pixel value of a windowed observation is indexed into separate lookup tables to retrieve a set of bit vectors which are "anded" together to produce a bit vector with a unique nonzero bit. The position of that bit is used to look up a filter value in a table. The number of stored bit vectors is proportional to the number of image gray levels. An architecture for CMM is presented that uses a minimal number of bit vectors so that required memory is less sensitive to the number of gray levels. The number of pixels in the observation window is the dimension of the image space. In the proposed architecure, basis elements are projected to subspaces of the image space and only bit vectors unique to each subspace are stored. Each projection corresponds to a subspace partition. Filter memory is greatly reduced by using intermediate lookup tables to map observations to unique bit vectors. We investigate two possible projection strategies: A fixed, singleton architecture, in which each subspace is one dimension, and a minimal architecture, in which a large number of subspace projections are searched for, one with minimal memory. Insensitivity to the number of gray levels is demonstrated through simulated, random-image space tessellations. We also present memory savings in a digital photocopier application.
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Introduction to the special issue on virtual reality environments in behavioral sciences.
Virtual reality (VR) is usually described in biology and in medicine as a collection of technologies that allow people to interact efficiently with three-dimensional (3-D) computerized databases in real time using their natural senses. This definition lacks any reference to head-mounted displays (HMDs) and instrumented clothing such as gloves or suits. In fact, less than 10% of VR healthcare applications in medicine are actually using any immersive equipment. However, if we focus our attention on behavioral sciences, where immersion is used by more than 50% of the applications, VR is described as an advanced form of human- computer interface that allows the user to interact with and become immersed in a computer-generated environment. This difference outlines a different vision of VR shared by psychologists, psychotherapists, and neuropsychologists: VR provides a new human-computer interaction paradigm in which users are no longer simply external observers of images on a computer screen but are active participants within a computer-generated 3-D virtual world. This special issue investigates this vision, presenting some of the most interesting applications actually developed in the area. Moreover, it discusses the clinical principles, human factors, and technological issues associated with the use of VR in the behavioral sciences.
Behavioral Sciences↗
Shear modulus estimation using parallelized partial volumetric reconstruction.
Magnetic resonance elastography can be limited by the computationally intensive nonlinear inversion schemes that are sometimes employed to estimate shear modulus from externally induced internal tissue displacements. Consequently, we have developed a parallelized partial volume reconstruction approach to overcome this limitation. In this paper, we report results from experiments conducted on breast phantoms and human volunteers to validate the proposed technique. More specifically, we demonstrate that computational cost is linearly related to the number of subzones used during image recovery and that both subzone parallelization and partial volume domain reduction decrease execution time accordingly. Importantly, elastograms computed based on the parallelized partial volume technique are not degraded in terms of either image quality or accuracy relative to their full volume counterparts provided that the estimation domain is sufficiently large to negate the effects of boundary conditions. The clinical results presented in this paper are clearly preliminary; however, the parallelized partial volume reconstruction approach performs sufficiently well to warrant more in-depth clinical evaluation.
Breast Neoplasms↗
Hierarchical learning architecture with automatic feature selection for multiclass protein fold classification.
The structure classification of proteins plays a very important role in bioinformatics, since the relationships and characteristics among those known proteins can be exploited to predict the structure of new proteins. The success of a classification system depends heavily on two things: the tools being used and the features considered. For the bioinformatics applications, the role of appropriate features has not been paid adequate importance. In this investigation we use three novel ideas for multiclass protein fold classification. First, we use the gating neural network, where each input node is associated with a gate. This network can select important features in an online manner when the learning goes on. At the beginning of the training, all gates are almost closed, i.e., no feature is allowed to enter the network. Through the training, gates corresponding to good features are completely opened while gates corresponding to bad features are closed more tightly, and some gates may be partially open. The second novel idea is to use a hierarchical learning architecture (HLA). The classifier in the first level of HLA classifies the protein features into four major classes: all alpha, all beta, alpha + beta, and alpha/beta. And in the next level we have another set of classifiers, which further classifies the protein features into 27 folds. The third novel idea is to induce the indirect coding features from the amino-acid composition sequence of proteins based on the N-gram concept. This provides us with more representative and discriminative new local features of protein sequences for multiclass protein fold classification. The proposed HLA with new indirect coding features increases the protein fold classification accuracy by about 12%. Moreover, the gating neural network is found to reduce the number of features drastically. Using only half of the original features selected by the gating neural network can reach comparable test accuracy as that using all the original features. The gating mechanism also helps us to get a better insight into the folding process of proteins. For example, tracking the evolution of different gates we can find which characteristics (features) of the data are more important for the folding process. And, of course, it also reduces the computation time.
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Approach and applications of constrained ICA.
This paper presents the technique of constrained independent component analysis (cICA) and demonstrates two applications, less-complete ICA, and ICA with reference (ICA-R). The cICA is proposed as a general framework to incorporate additional requirements and prior information in the form of constraints into the ICA contrast function. The adaptive solutions using the Newton-like learning are proposed to solve the constrained optimization problem. The applications illustrate the versatility of the cICA by separating subspaces of independent components according to density types and extracting a set of desired sources when rough templates are available. The experiments using face images and functional MR images demonstrate the usage and efficacy of the cICA.
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Neural network-based adaptive dynamic surface control for a class of uncertain nonlinear systems in strict-feedback form.
The dynamic surface control (DSC) technique was developed recently by Swaroop et al. This technique simplified the backstepping design for the control of nonlinear systems in strict-feedback form by overcoming the problem of "explosion of complexity." It was later extended to adaptive backstepping design for nonlinear systems with linearly parameterized uncertainty. In this paper, by incorporating this design technique into a neural network based adaptive control design framework, we have developed a backstepping based control design for a class of nonlinear systems in strict-feedback form with arbitrary uncertainty. Our development is able to eliminate the problem of "explosion of complexity" inherent in the existing method. In addition, a stability analysis is given which shows that our control law can guarantee the uniformly ultimate boundedness of the solution of the closed-loop system, and make the tracking error arbitrarily small.
Algorithms↗
A model selection algorithm for a posteriori probability estimation with neural networks.
This paper proposes a novel algorithm to jointly determine the structure and the parameters of a posteriori probability model based on neural networks (NNs). It makes use of well-known ideas of pruning, splitting, and merging neural components and takes advantage of the probabilistic interpretation of these components. The algorithm, so called a posteriori probability model selection (PPMS), is applied to an NN architecture called the generalized softmax perceptron (GSP) whose outputs can be understood as probabilities although results shown can be extended to more general network architectures. Learning rules are derived from the application of the expectation-maximization algorithm to the GSP-PPMS structure. Simulation results show the advantages of the proposed algorithm with respect to other schemes.
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A Hopfield neural network for image change detection.
This paper outlines an optimization relaxation approach based on the analog Hopfield neural network (HNN) for solving the image change detection problem between two images. A difference image is obtained by subtracting pixel by pixel both images. The network topology is built so that each pixel in the difference image is a node in the network. Each node is characterized by its state, which determines if a pixel has changed. An energy function is derived, so that the network converges to stable states. The analog Hopfield's model allows each node to take on analog state values. Unlike most widely used approaches, where binary labels (changed/unchanged) are assigned to each pixel, the analog property provides the strength of the change. The main contribution of this paper is reflected in the customization of the analog Hopfield neural network to derive an automatic image change detection approach. When a pixel is being processed, some existing image change detection procedures consider only interpixel relations on its neighborhood. The main drawback of such approaches is the labeling of this pixel as changed or unchanged according to the information supplied by its neighbors, where its own information is ignored. The Hopfield model overcomes this drawback and for each pixel allows a tradeoff between the influence of its neighborhood and its own criterion. This is mapped under the energy function to be minimized. The performance of the proposed method is illustrated by comparative analysis against some existing image change detection methods.
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Prune-able fuzzy ART neural architecture for robot map learning and navigation in dynamic environments.
Mobile robots must be able to build their own maps to navigate in unknown worlds. Expanding a previously proposed method based on the fuzzy ART neural architecture (FARTNA), this paper introduces a new online method for learning maps of unknown dynamic worlds. For this purpose the new Prune-able fuzzy adaptive resonance theory neural architecture (PAFARTNA) is introduced. It extends the FARTNA self-organizing neural network with novel mechanisms that provide important dynamic adaptation capabilities. Relevant PAFARTNA properties are formulated and demonstrated. A method is proposed for the perception of object removals, and then integrated with PAFARTNA. The proposed methods are integrated into a navigation architecture. With the new navigation architecture the mobile robot is able to navigate in changing worlds, and a degree of optimality is maintained, associated to a shortest path planning approach implemented in real-time over the underlying global world model. Experimental results obtained with a Nomad 200 robot are presented demonstrating the feasibility and effectiveness of the proposed methods.
Algorithms↗
On adaptive learning rate that guarantees convergence in feedforward networks.
This paper investigates new learning algorithms (LF I and LF II) based on Lyapunov function for the training of feedforward neural networks. It is observed that such algorithms have interesting parallel with the popular backpropagation (BP) algorithm where the fixed learning rate is replaced by an adaptive learning rate computed using convergence theorem based on Lyapunov stability theory. LF II, a modified version of LF I, has been introduced with an aim to avoid local minima. This modification also helps in improving the convergence speed in some cases. Conditions for achieving global minimum for these kind of algorithms have been studied in detail. The performances of the proposed algorithms are compared with BP algorithm and extended Kalman filtering (EKF) on three bench-mark function approximation problems: XOR, 3-bit parity, and 8-3 encoder. The comparisons are made in terms of number of learning iterations and computational time required for convergence. It is found that the proposed algorithms (LF I and II) are much faster in convergence than other two algorithms to attain same accuracy. Finally, the comparison is made on a complex two-dimensional (2-D) Gabor function and effect of adaptive learning rate for faster convergence is verified. In a nutshell, the investigations made in this paper help us better understand the learning procedure of feedforward neural networks in terms of adaptive learning rate, convergence speed, and local minima.
Algorithms↗
Analysis of artificial neural networks for pattern-based adaptive control.
Adaptive pattern-based control strategies adapt their parameters from an analysis of response patterns exhibited by the system. This work presents an analysis of a class of artificial neural network (ANN) pattern-based adaptive control. It provides conditions under which the adaptive algorithm will converge, and it also characterizes the closed-loop stability properties. In addition, a method for monitoring the adaptation is also proposed. Several simulation examples illustrate our findings.
Algorithms↗
Run-length chain coding and scalable computation of a shape's moments using reconfigurable optical buses.
The main contribution of this paper is the design of several efficient algorithms for modified run-length chain coding and for computing a shape's moments on arrays with reconfigurable optical buses. The proposed algorithms are based on the boundary representation of an object. Instead of using chain code, the boundary can be represented by a modified run-length chain code, where each entity represents a line segment (two adjacent corner pixels). The sequential nature of the chain code makes it difficult to be parallelized. We first propose two constant time algorithms for boundary extraction and run-length chain coding. To the authors' knowledge, these are the most time efficient algorithms yet published. Based on the modified run-length chain coding, and the advantages of both optical transmission and electronic computation, a constant time parallel algorithm for computing a shape's moments using N x N processors is proposed. Additionally, instead of using N x N processors, a scalable moment algorithm using r x r processors is also derived, where r < N. Based on the product of time and the number of processors used, both proposed parallel algorithms are time and cost optimal.
Algorithms↗
On combining support vector machines and simulated annealing in stereovision matching.
This paper outlines a method for solving the stereovision matching problem using edge segments as the primitives. In stereovision matching, the following constraints are commonly used: epipolar, similarity, smoothness, ordering, and uniqueness. We propose a new strategy in which such constraints are sequentially combined. The goal is to achieve high performance in terms of correct matches by combining several strategies. The contributions of this paper are reflected in the development of a similarity measure through a support vector machines classification approach; the transformation of the smoothness, ordering and epipolar constraints into the form of an energy function, through an optimization simulated annealing approach, whose minimum value corresponds to a good matching solution and by introducing specific conditions to overcome the violation of the smoothness and ordering constraints. The performance of the proposed method is illustrated by comparative analysis against some recent global matching methods.
Algorithms↗
Evolving binary classifiers through parallel computation of multiple fitness cases.
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 explicitly using parallel computation in the case of cellular programming or implicitly taking advantage of the intrinsic parallelism of bitwise operators on standard sequential architectures in the case of genetic programming. The approach was tested on a digit recognition problem and compared with a reference classifier.
Algorithms↗
A generic and scalable pipeline for GPU tetrahedral grid rendering.
Recent advances in algorithms and graphics hardware have opened the possibility to render tetrahedral grids at interactive rates on commodity PCs. This paper extends on this work in that it presents a direct volume rendering method for such grids which supports both current and upcoming graphics hardware architectures, large and deformable grids, as well as different rendering options. At the core of our method is the idea to perform the sampling of tetrahedral elements along the view rays entirely in local barycentric coordinates. Then, sampling requires minimum GPU memory and texture access operations, and it maps efficiently onto a feed-forward pipeline of multiple stages performing computation and geometry construction. We propose to spawn rendered elements from one single vertex. This makes the method amenable to upcoming Direct3D 10 graphics hardware which allows to create geometry on the GPU. By only modifying the algorithm slightly it can be used to render per-pixel iso-surfaces and to perform tetrahedral cell projection. As our method neither requires any pre-processing nor an intermediate grid representation it can efficiently deal with dynamic and large 3D meshes.
Algorithms↗