Vector Associative Maps: Unsupervised Real-time Error-based Learning and Control of Movement Trajectories

This article describes neural network models for adaptive control of arm movement trajectories during visually guided reaching and, more generally, a framework for unsupervised real-time error-based learning. The models clarify how a child, or untrained robot, can learn to reach for objects that it sees. Piaget has provided basic insights with his concept of a circular reaction: As an infant makes internally generated movements of its hand, the eyes automatically follow this motion. A transformation is learned between the visual representation of hand position and the motor representation of hand position. Learning of this transformation eventually enables the child to accurately reach for visually detected targets. Grossberg and Kuperstein have shown how the eye movement system can use visual error signals to correct movement parameters via cerebellar learning. Here it is shown how endogenously generated arm movements lead to adaptive tuning of arm control parameters. These movements also activate the target position representations that are used to learn the visuo-motor transformation that controls visually guided reaching. The AVITE model presented here is an adaptive neural circuit based on the Vector Integration to Endpoint (VITE) model for arm and speech trajectory generation of Bullock and Grossberg. In the VITE model, a Target Position Command (TPC) represents the location of the desired target. The Present Position Command (PPC) encodes the present hand-arm configuration. The Difference Vector (DV) population continuously.computes the difference between the PPC and the TPC. A speed-controlling GO signal multiplies DV output. The PPC integrates the (DV)·(GO) product and generates an outflow command to the arm. Integration at the PPC continues at a rate dependent on GO signal size until the DV reaches zero, at which time the PPC equals the TPC. The AVITE model explains how self-consistent TPC and PPC coordinates are autonomously generated and learned. Learning of AVITE parameters is regulated by activation of a self-regulating Endogenous Random Generator (ERG) of training vectors. Each vector is integrated at the PPC, giving rise to a movement command. The generation of each vector induces a complementary postural phase during which ERG output stops and learning occurs. Then a new vector is generated and the cycle is repeated. This cyclic, biphasic behavior is controlled by a specialized gated dipole circuit. ERG output autonomously stops in such a way that, across trials, a broad sample of workspace target positions is generated. When the ERG shuts off, a modulator gate opens, copying the PPC into the TPC. Learning of a transformation from TPC to PPC occurs using the DV as an error signal that is zeroed due to learning. This learning scheme is called a Vector Associative Map, or VAM. The VAM model is a general-purpose device for autonomous real-time error-based learning and performance of associative maps. The DV stage serves the dual function of reading out new TPCs during performance and reading in new adaptive weights during learning, without a disruption of real-time operation. YAMs thus provide an on-line unsupervised alternative to the off-line properties of supervised error-correction learning algorithms. YAMs and VAM cascades for learning motor-to-motor and spatial-to-motor maps are described. YAM models and Adaptive Resonance Theory (ART) models exhibit complementary matching, learning, and performance properties that together provide a foundation for designing a total sensory-cognitive and cognitive-motor autonomous system.National Science Foundation (IRI-87-16960, IRI-87-6960); Air Force Office of Scientific Research (90-0175); Defense Advanced Research Projects Agency (90-0083

XML-based genetic rules for scene boundary detection in a parallel processing environment

Genetic programming is based on Darwinian evolutionary theory that suggests that the best solution for a problem can be evolved by methods of natural selection of the fittest organisms in a population. These principles are translated into genetic programming by populating the solution space with an initial number of computer programs that can possibly solve the problem and then evolving the programs by means of mutation, reproduction and crossover until a candidate solution can be found that is close to or is the optimal solution for the problem. The computer programs are not fully formed source code but rather a derivative that is represented as a parse tree. The initial solutions are randomly generated and set to a certain population size that the system can compute efficiently. Research has shown that better solutions can be obtained if 1) the population size is increased and 2) if multiple runs are performed of each experiment. If multiple runs are initiated on many machines the probability of finding an optimal solution are increased exponentially and computed more efficiently. With the proliferation of the web and high speed bandwidth connections genetic programming can take advantage of grid computing to both increase population size and increasing the number of runs by utilising machines connected to the web. Using XML-Schema as a global referencing mechanism for defining the parameters and syntax of the evolvable computer programs all machines can synchronise ad-hoc to the ever changing environment of the solution space. Another advantage of using XML is that rules are constructed that can be transformed by XSLT or DOM tree viewers so they can be understood by the GP programmer. This allows the programmer to experiment by manipulating rules to increase the fitness of a rule and evaluate the selection of parameters used to define a solution

Digital Color Imaging

This paper surveys current technology and research in the area of digital color imaging. In order to establish the background and lay down terminology, fundamental concepts of color perception and measurement are first presented us-ing vector-space notation and terminology. Present-day color recording and reproduction systems are reviewed along with the common mathematical models used for representing these devices. Algorithms for processing color images for display and communication are surveyed, and a forecast of research trends is attempted. An extensive bibliography is provided

Examining trade-offs between social, psychological, and energy potential of urban form

Urban planners are often challenged with the task of developing design solutions which must meet multiple, and often contradictory, criteria. In this paper, we investigated the trade-offs between social, psychological, and energy potential of the fundamental elements of urban form: the street network and the building massing. Since formal methods to evaluate urban form from the psychological and social point of view are not readily available, we developed a methodological framework to quantify these criteria as the first contribution in this paper. To evaluate the psychological potential, we conducted a three-tiered empirical study starting from real world environments and then abstracting them to virtual environments. In each context, the implicit (physiological) response and explicit (subjective) response of pedestrians were measured. To quantify the social potential, we developed a street network centrality-based measure of social accessibility. For the energy potential, we created an energy model to analyze the impact of pure geometric form on the energy demand of the building stock. The second contribution of this work is a method to identify distinct clusters of urban form and, for each, explore the trade-offs between the select design criteria. We applied this method to two case studies identifying nine types of urban form and their respective potential trade-offs, which are directly applicable for the assessment of strategic decisions regarding urban form during the early planning stages

Wavelet-Based Enhancement Technique for Visibility Improvement of Digital Images

Image enhancement techniques for visibility improvement of color digital images based on wavelet transform domain are investigated in this dissertation research. In this research, a novel, fast and robust wavelet-based dynamic range compression and local contrast enhancement (WDRC) algorithm to improve the visibility of digital images captured under non-uniform lighting conditions has been developed. A wavelet transform is mainly used for dimensionality reduction such that a dynamic range compression with local contrast enhancement algorithm is applied only to the approximation coefficients which are obtained by low-pass filtering and down-sampling the original intensity image. The normalized approximation coefficients are transformed using a hyperbolic sine curve and the contrast enhancement is realized by tuning the magnitude of the each coefficient with respect to surrounding coefficients. The transformed coefficients are then de-normalized to their original range. The detail coefficients are also modified to prevent edge deformation. The inverse wavelet transform is carried out resulting in a lower dynamic range and contrast enhanced intensity image. A color restoration process based on the relationship between spectral bands and the luminance of the original image is applied to convert the enhanced intensity image back to a color image. Although the colors of the enhanced images produced by the proposed algorithm are consistent with the colors of the original image, the proposed algorithm fails to produce color constant results for some pathological scenes that have very strong spectral characteristics in a single band. The linear color restoration process is the main reason for this drawback. Hence, a different approach is required for tackling the color constancy problem. The illuminant is modeled having an effect on the image histogram as a linear shift and adjust the image histogram to discount the illuminant. The WDRC algorithm is then applied with a slight modification, i.e. instead of using a linear color restoration, a non-linear color restoration process employing the spectral context relationships of the original image is applied. The proposed technique solves the color constancy issue and the overall enhancement algorithm provides attractive results improving visibility even for scenes with near-zero visibility conditions. In this research, a new wavelet-based image interpolation technique that can be used for improving the visibility of tiny features in an image is presented. In wavelet domain interpolation techniques, the input image is usually treated as the low-pass filtered subbands of an unknown wavelet-transformed high-resolution (HR) image, and then the unknown high-resolution image is produced by estimating the wavelet coefficients of the high-pass filtered subbands. The same approach is used to obtain an initial estimate of the high-resolution image by zero filling the high-pass filtered subbands. Detail coefficients are estimated via feeding this initial estimate to an undecimated wavelet transform (UWT). Taking an inverse transform after replacing the approximation coefficients of the UWT with initially estimated HR image, results in the final interpolated image. Experimental results of the proposed algorithms proved their superiority over the state-of-the-art enhancement and interpolation techniques

The Use of Infographics to Assess Context Processing

Among high-order cognitive functions is the use of context to enhance comprehension of language or visual scenes. Although use of context is known to be impaired in certain clinical populations (e.g., schizophrenia), no existing test adequately assesses this construct. To fill this gap, we developed and attempted to validate a test of context use that employed Infographics (information graphics), which requires the use of context to interpret visual displays. The primary hypothesis was that interpreting Infographics would be sensitive to context processing. We further hypothesized that different levels of cognitive processing (requiring basic perceptual, real-world application, or verbal reasoning), as well as different categories of Infographics (Data Display, Maps, Diagrams, or Timelines) would tap differential cognitive functions. Forty Infographics test items were developed based upon design principles of Infographics. Following development of items, the Infographics test, as well as a battery of neuropsychological tests, were administered to 161 participants. Overall, results revealed that our Infographics did target context. However, the test also places significant demands on verbal reasoning and similar cognitive functions apply to each level of cognitive processing. Finally, results indicated that similar cognitive functions applied to all categories of Infographics, with the exception of the three of the categories of Data Display, Maps, and Diagrams, which were associated with graphical literacy skills, whereas Timeline was not. In sum, we present data that a newly developed Infographics test is a valuable tool to assess context, and may be applied to evaluate individual differences among healthy individuals, as well as to evaluate impairment in patients with specific clinical diagnoses. However, test performance is not specific to context processing and the test is also sensitive to other high-order cognitive functions, including verbal reasoning