This work presents a segmentation method based in a tuned version of watershed transform. It is based on a chromatic gradient that is made through Hyperspherical Coordinates. It avoids the natural oversegmentation induced by the standard watershed and it overcomes shadows and shines in order to elude false edges.

We present a color gradient with good color constancy preservation properties. The approach does not need a priori information or changes in color space. It is based on the angular distance between pixel color representations in the RGB space. It is naturally invariant to intensity magnitude, implying high robustness against bright spots produced be specular reflections and dark regions of low intensity.

In recent years, the concern with image authentication and ownership issues is growing in the remote sensing community. Watermarking techniques help to solve the problems raised by this issue. In this paper we elaborate on the proposition of an optimal placement of the watermark image in a hyperspectral image. The problem is posed as a multi-objective optimization problem. Two objective functions are formulated, one modeling the robustness of the watermark recovery and other modeling the distortion introduced in the watermarked image. The application of an evolutionary algorithm (MOGA) to the optimal watermarking hyperspectral images is presented.

Convergence of the self-organizing map (SOM) and neural gas (NG) is usually contemplated from the point of view of stochastic gradient descent (SGD) algorithms of an energy function. SGD algorithms are characterized by very restrictive conditions that produce a slow convergence rate. Also they are local minimization algorithms, very dependent on the initial conditions. However, some empirical results show that one-pass on-line training realizations of SOM and NG may perform comparable to more careful (slow) realizations. Moreover, other empirical works suggest that SOM is quite robust against initial conditions. In both cases the performance measure is the quantization distortion. That empirical evidence leads us to propose that the appropriate setting for the convergence analysis of SOM NG and similar competitive artificial neural network clustering algorithms is the theory of graduated non-convexity (GNC) algorithms.

Robust control of mobile robots needs to take into account the many noise sources in the sensors and actuators of the robots. In this paper, we propose the application of control strategies designed for deterministic chaotic systems to the design of robust control of mobile robots. The deterministic chaotic system hypothesis is proposed as an intermediate model between the noise free system and the pure random perturbations.

In this paper we consider the determination of the structure of the high-order Boltzmann machine (HOBM), a stochastic recurrent network for approximating probability distributions. We obtain the structure of the HOBM, the hypergraph of connections, from conditional independences of the probability distribution to model. We assume that an expert provides these conditional independences and from them we build independence maps, Markov and Bayesian networks, which represent conditional independences through undirected graphs and directed acyclic graphs respectively. From these independence maps we construct the HOBM hypergraph. The central aim of this paper is to obtain a minimal hypergraph. Given that different orderings of the variables provide in general different Bayesian networks, we define their intersection hypergraph. We prove that the intersection hypergraph of all the Bayesian networks (N!) of the distribution is contained by the hypergraph of the Markov network, it is more simple, and we give a procedure to determine a subset of the Bayesian networks that verifies this property. We also prove that the Markov network graph establishes a minimum connectivity for the hypergraphs from Bayesian networks.

In this paper we give a formal definition of the high-order Boltzmann machine (BM), and extend the well-known results on the convergence of the learning algorithm of the two-order BM. From the Bahadur-Lazarsfeld expansion we characterize the probability distribution learned by the high order BM. Likewise a criterion is given to establish the topology of the BM depending on the significant correlations of the particular probability distribution to be learned.

Generalized learning vector quantization (GLVQ) has been proposed in as a generalization of the simple competitive learning (SCL) algorithm. The main argument of GLVQ proposal is its superior insensitivity to the initial values of the weights (code vectors). In this paper we show that the distinctive characteristics of the definition of GLVQ disappear outside a small domain of applications. GLVQ becomes identical to SCL when either the number of code vectors grows or the size of the input space is large. Besides that, the behavior of GLVQ is inconsistent for problems defined on very small scale input spaces. The adaptation rules fluctuate between performing descent and ascent searches on the gradient of the distortion function.

Boltzmann machines (BMs) are proposed as a computational model for the solution of the satisfiability (SAT) problem in the propositional calculus setting. Conditions that guarantee consensus function maxima for configurations of the BM associated with solutions to the satisfaction problem are given. Experimental results that show a linear behavior of BMs solving the satisfiability problem are presented and discussed.>