Cristian Munteanu

• University of Las Palmas de Gran Canaria

We present an interactive easy-to-use software package, based on an evolutionary algorithm, to perform adaptive anisotropic diffusion speckle filtering for synthetic aperture radar (SAR) images. As a main difference from other methodologies, there is an integration of a SAR-image human expert who provides a subjective validation to complete the dif...

Objective: So far there is no ideal speckle reduction filtering technique that is capable of enhancing and reducing the level of noise in medical ultrasound (US) images, while efficiently responding to medical experts' validation criteria which quite often include a subjective component. This paper presents an interactive tool called evolutionary...

This communication presents an interactive tool performing adaptive speckle filtering so that the medical expert who runs the algorithm has permanent control over the output and guides the process towards obtaining enhanced images that agree to his/her subjective quality criteria. The core of the filtering tool is an Interactive Genetic Algorithm t...

Classical Genetic Algorithm theory was built on four operators: proportional selection, one-point crossover, mutation and inversion. While the role of inversion was questioned, the use of the other remaining operators has thrived, some of these newly designed operators being motivated by good empirical results, some having a solid theory to support...

The electroencephalogram (EEG) is a non-invasive and affordable technique to study the brain activity during wakefulness and especially during sleep. The current trend on sleep analysis focuses on its dynamic organization described in the cyclic alternating pattern (CAP) paradigm. This paradigm looks to the EEG microstructure, gives attention to th...

The EEG is an affordable and non-invasive technique to study the brain and very useful in sleep analysis. The organization of the sleep in stages (global view) was widely used. The sleep EEG microstructure (local view) gives attention to the short duration EEG events. The CAPS (Cyclic Alternating Pattern Sequences) is a periodic EEG activity of sle...

In recent years, extensive work has been done to design algorithms that strive to mimic the robust human vision system which is able to perceive the true colors and discount the illuminant from a scene viewed under light having different spectral compositions (the feature is called “color constancy”). We propose a straightforward approach to the co...

This paper introduces a novel global optimization heuristic algorithm based on the basic paradigms of Evolutionary Algorithms (EA). The algorithm greatly extends a previous strategy proposed by the authors in Munteanu and Lazarescu (1998). In the newly designed algorithm the exploration/exploitation of the search space is adapted on-line based on t...

Image enhancement is the task of applying certain transformations to an input image such as to obtain a visually more pleasant, more detailed, or less noisy output image. The transformation usually requires interpretation and feedback from a human evaluator of the output result image. Therefore, image enhancement is considered a difficult task when...

It is now common knowledge that blind search algorithms cannot perform with equal efficiency on all possible optimization problems defined on a domain. This knowledge applies also to Genetic Algorithms when viewed as global and blind optimizers. From this point of view it is necessary to design algorithms capable of adapting their search behavior b...

This paper extends the theoretical analysis of the Adaptive Reservoir Genetic Algorithm (ARGA), a variant of a Genetic Algorithm (GA) proposed by the authors in [4]. We show that ARGA visits the global optimum after a finite number of iterations with probability one, regardless of the initialization of the population.

Classification of the electroencephalogram (EEG) during motor imagery of the left or right hand can be performed using a classifier comprising two hidden Markov models (HMMs) describing the spatio-temporal patterns related to the imagination. Due to the known asymmetries during motor imagery of rightand left-hand movement, an HMM-based classifier a...

The paper investigates the use of Evolutionary Computation (EC) to automate the process of color image enhancement, for a large category of color images taken with different illuminants. Our approach employs the model of Multi-Scale Retinex with Color Restoration (MSRCR) for which the parameters of the model are adapted to each given image with a R...

In nature, some species mate according to their phenotype similarity. The Assortative Mating Genetic Algorithm (AMGA) mimics some mechanisms of reproduction in natural environments. The main difference between AMGA and the Standard GA (SGA) is the selection of the parents in the crossover operators. We develop a similarity measure for the Vector Qu...

In this paper we present a novel method for image enhancement of gray-scale images based on the simulation of evolution. Our method employs Genetic Algorithms to evolve the shape of the contrast curve in the image, while attempting to partially automate the subjective process of image evaluation (e.g. user behaviour) by performing multiple regressi...

Classical Genetic Algorithm theory was built on four operators: proportional selection, one-point crossover, mutation and inversion. While the role of inversion was questioned, the use of the other remaining operators has thrived, some of these newly designed operators being motivated by good empirical results, some having a solid theory to support...

This paper introduces a new automatic image enhancement technique based on real-coded genetic algorithms (GAs). The task of the GA is to adapt the parameters of a novel extension to a local enhancement technique similar to statistical scaling, as to enhance the contrast and detail in the image according to an objective fitness criterion. We compare...

In this paper we present a method of evolving the contrast characteristics of a satellite image by employing a real-coded genetic algorithm with subjective fitness. The contrast properties of an image are given by its contrast-stretching curve, and the task of the Genetic Algorithm is to evolve this curve in order to find the best shape according t...

Effective optimization and search methods have to explore the whole search space, in order to find promising regions in which the optimum may lie, and to exploit those regions, so as to find the optimum solution. Genetic algorithms (GA) are global searching methods that may be capable of exploration as well as providing a good exploitation of the s...

Feedforward neural network performances depend on finding optimal learning parameters. Among them, the learning rate is critical for both the convergence speed and the value of the learning error. This paper introduces a new method for learning rate adaptation, namely the "Anti–Oscillatory Dynamic Method". Its performances are evaluated on a vowel...

In this paper we introduce a new search heuristic based on a Genetic Algorithm structure, called Adaptive Reservoir Genetic Algorithm (ARGA). This search mechanism reduces the loss of genetic diversity, present in most of Genetic Algorithms, by employing an adaptive mutant subpopulation called reservoir. We present preliminary theoretical considera...

This paper introduces a new method of performing mutation in a real-coded Genetic Algorithm (GA), a method driven by Principal Component Analysis (PCA). We present both theoretical and empirical results which show that our mutation operator attains higher levels of diversity in the search space, as compared to other mutation operators, meaning that...

It is now common knowledge that blind search algorithms cannot perform with equal efficiency on all possible optimization prob-lems defined on a domain. This knowledge applies also to Genetic Algo-rithms when viewed as global and blind optimizers. From this point of view it is necessary to design algorithms capable of adapting their search behaviou...

Feedforward neural network performances depend on finding optimal learning parameters. Among them, the learning rate is critical for both the convergence speed and the value of the learning error. This paper introduces a new method for learning rate adaptation, namely the "Anti-Oscillatory Dynamic Method". Its performances are evaluated on a vowel...

This paper extends the theoretical analysis of the Adaptive Reservoir Genetic Algorithm (ARGA), a variant of a Genetic Algorithm (GA) proposed by the authors in (4). We show that ARGA visits the global optimum after a fin ite number of iterations with probability one, regardless of the initialization of the population.