Catálogo de publicaciones - libros

Poses and gestures are an important part of the nonverbal inter-human communication. In the last years many different methods for estimating poses and gestures in the field of Human-Machine-Interfaces were developed. In this paper for the first time we present an experimental comparison of several re-implemented Neural Network based approaches for a demanding visual instruction task on a mobile system. For the comparison we used several Neural Networks (Neural Gas, SOM, LLM, PSOM and MLP) and a k-Nearest-Neighbourhood classificator on a common data set of images, which we recorded on our mobile robot under real world conditions. For feature extraction we use Gaborjets and the features of a special histogram on the image. We also compare the results of the different approaches with the results of human subjects who estimated the target point of a pointing pose. The results obtained demonstrate that a cascade of MLPs is best suited to cope with the task and achieves results equal to human subjects.

In this paper, a SVM regression based method is proposed for background estimation and foreground detection. Incoming frames are treated as time series and a fixed-scale working-set selecting strategy is specifically designed for real-time background estimation. Experiments on two representat-ive videos demonstrate the application potential of the proposed algorithm and also reveal some problems underlying it. Both the positive and negative reports from our study offer some useful information for researchers both in the field of image processing and that of machine learning.

This study deals with image superresolution problems simultaneously with accompanying image registration problems. The goal of superresolution is to generate a high resolution image by integrating low-resolution degraded observed images. We propose a Bayesian approach whose prior is modeled as a compound Gaussian Markov random field (MRF). This approach is advantageous in preserving discontinuity in the original image, in comparison to the existing single-layer Gaussian MRF models. Maximum-marginalized-likelihood estimation of the registration parameters is carried out by a variational EM algorithm where hidden variables are marginalized out and the posterior distribution is approximated by a factorized trial distribution. High resolution image estimates are obtained as by-products of the EM algorithm. Experiments show that our Bayesian approach with two-layer compound models exhibits better performance in terms of mean square error and visual quality than the single-layer model.

This paper presents an adaptive neurofuzzy controller for tip position tracking control of a single link flexible manipulator. The controller has a self- organizing fuzzy neural structure in which fuzzy rules are generated during the control process using an online learning algorithm. In order to demonstrate the superior performance of the proposed controller, the results are compared with those obtained by using the proportional-derivative (PD) and neural network controllers. Moreover, since the proposed controller requires no a priori knowledge about the system, it can efficiently cope with the uncertainties such as payload mass variations.

This paper details a computationally efficient (suboptimal) nonlinear Model Predictive Control (MPC) algorithm with structured neural models and discusses its application to a polymerisation reactor. Thanks to the nature of the model it is not used recursively, the prediction error is not propagated. The model is used on-line to determine a local linearisation and a nonlinear free trajectory. The algorithm needs solving on-line only a quadratic programming problem. It gives closed-loop control performance similar to that obtained in the fully-fledged nonlinear MPC, which hinges on non-convex optimisation.

A primary goal for an autonomous mobile robot is to explore and perfrom simultaneous localization and mapping (SLAM). During SLAM, the robot must balance the opposing desires of pose certainty maintenance and information gain. Much of previous research has ignored the need of pose maintenance. This paper provides the first step in developing a neural dynamics based algorithm which considers both information gain and pose maintenance when determining the robot’s next pose. Simulation results show that the algorithm is able to provide the robot with an exploration plan to fully explore the tested environments. The next step is to apply the algorithm in a full SLAM environment.

This paper discusses the problem of cooperation of economic optimisation with Model Predictive Control (MPC) algorithms when the dynamics of disturbances is comparable with the dynamics of the process. A dynamic neural model is used in the suboptimal nonlinear MPC algorithm with Nonlinear Prediction and Linearisation (MPC-NPL), a steady-state neural model is used in approximate economic optimisation which is executed as frequently as the MPC algorithm. The MPC-NPL algorithm requires solving on-line only a quadratic programming problem whereas approximate economic optimisation needs solving a linear programming problem. As a result, the necessity of repeating two nonlinear optimisation problems at each sampling instant is avoided.

Reservoir Computing (RC) uses a randomly created recurrent neural network where only a linear readout layer is trained. In this work, RC is used for detecting complex events in autonomous robot navigation. This can be extended to robot localization based solely on sensory information. The robot thus builds an implicit map of the environment without the use of odometry data. These techniques are demonstrated in simulation on several complex and even dynamic environments.

This paper demonstrates the use of CMAC neural networks in real world applications for the system identification and control of nonlinear systems. As a testbed application, the problem of regulating fluid height in a column is considered. A dynamic nonlinear model of the process is obtained using fundamental physical laws and by training a CMAC neural network using experimental input-output data. The CMAC model is used to implement a model reference control system. Successful experimental results are obtained in the presence of disturbances.

This work presents a methodology for the land-cover classification of satellite images based on clustering of the Kohonen’s self-organizing map (SOM). The classification task is carried out using a three-stage approach. At the first stage, the SOM is used to quantize and to represent the original patterns of the image in a space of smaller dimension. At the second stage of the method, a filtering process is applied on the SOM prototypes, wherein prototypes associated to input patterns that incorporate more than one land cover class and prototypes that have null activity are excluded in the next stage or simply eliminated of the analysis. At the third and last stage, the SOM prototypes are segmented through a hierarchical clustering method which uses the neighborhood relation of the neurons and incorporates spatial information in its merging criterion. The experimental results show an application example of the proposed methodology on an IKONOS image.