M. Pujol

Use of mathematical morphology in real‐time path planning

If an autonomous vehicle is working in an image‐based system which needs real‐time answers and whose response is critical, it will be very important to reduce computation times and, as we know, this could be performed by increasing the system parallelism. Since morphological filtering is the origin of several applications in computer vision, in this paper we are going to describe some new features to implement morphological operations by using digital signal processors. After that, an application to path planning is proposed. The standard shortest path planning problem determines a collision‐free path of shortest distance between two distinct locations in an environment scattered with obstacles. Consequently, a path planning algorithm which uses morphological operations and a DSP to process images is then described.

Face Detection Based on Skin Color Segmentation Using Fuzzy Entropy

Face detection is the first step of any automated face recognition system. One of the most popular approaches to detect faces in color images is using a skin color segmentation scheme, which in many cases needs a proper representation of color spaces to interpret image information. In this paper, we propose a fuzzy system for detecting skin in color images, so that each color tone is assumed to be a fuzzy set. The Red, Green, and Blue (RGB), the Hue, Saturation and Value (HSV), and the YCbCr (where Y is the luminance and Cb,Cr are the chroma components) color systems are used for the development of our fuzzy design. Thus, a fuzzy three-partition entropy approach is used to calculate all of the parameters needed for the fuzzy systems, and then, a face detection method is also developed to validate the segmentation results. The results of the experiments show a correct skin detection rate between 94% and 96% for our fuzzy segmentation methods, with a false positive rate of about 0.5% in all cases. Furthermore, the average correct face detection rate is above 93%, and even when working with heterogeneous backgrounds and different light conditions, it achieves almost 88% correct detections. Thus, our method leads to accurate face detection results with low false positive and false negative rates.

A non‐linear model of cerebral diffusion: stability of finite differences method and resolution using the Adomian method

This paper describes a non‐linear reaction‐diffusion equation, which models how a substance spreads in the surface of the cortex so as to avoid a massive destruction of neurones when cerebral tissue is not oxygenated correctly. For the explicit finite differences method, the necessary stability condition is provided by a reaction‐diffusion equation with non‐linearity given by a decreasing function. The solution to the non‐linear reaction‐diffusion equation of the model can be obtained via one of the two methods: the finite differences (explicit schema) method and the Adomian method.

Agents for Swarm Robotics: Architecture and Implementation

Swarm robotics is a type of robotic systems based on many simple robots interactions. Such systems enjoy many benefits such as high tolerance and the possibility of increasing the number of robots in a transparent way to the programmer; but they also have many difficulties when applied to complex problems. In this paper, we will present a hybrid architecture for swarm robotics based on a multi-agent system. The main contribution of this architecture is to make possible the use of cognitive agents to lead a robotic swarm of simple agents without losing the advantages of swarms. Moreover, the implementation of this architecture within Real Swarm platform and the discussion of how to apply this architecture in real systems will be presented.

Obtaining a Bayesian map for data fusion and failure detection under uncertainty

This paper presents a generic Bayesian map and shows how it is used for the development of a task done by an agent arranged in an environment with uncertainty. This agent interacts with the world and is able to detect, using only readings from its sensors, any failure of its sensorial system. It can even continue to function properly while discarding readings obtained by the erroneous sensor/s. A formal model based on Bayesian Maps is proposed. The Bayesian Maps brings up a formalism where implicitly, using probabilities, we work with uncertainly. Some experimental data is provided to validate the correctness of this approach.

On searching for an optimal threshold for morphological image segmentation

Segmentation of images represents the first step in many of the tasks that pattern recognition or computer vision has to deal with. Therefore, the main goal of our paper is to describe a new method for image segmentation, taking into account some Mathematical Morphology operations and an adaptively updated threshold, what we call Morphological Gradient Threshold, to obtain the optimal segmentation. The key factor in our work is the calculation of the distance between the segmented image and the ideal segmentation. Experimental results show that the optimal threshold is obtained when the Morphological Gradient Threshold is around the 70% of the maximum value of the gradient. This threshold could be computed, for any new image captured by the vision system, using a properly designed binary metrics.

Robot security and failure detection using bayesian fusion

This paper shows a Bayesian framework for fuse information. Using this framework we present a robotic system, based on two processing units. The system is used for the development of a task, done by an autonomous agent, arranged in an environment with uncertainty. This agent interacts with the world and is able to detect, only using its sensor readings, any failure of its sensorial system. Even it can continue working properly while discarding the readings obtained by the erroneous sensor/s. A security unit is also provided to make the system even more robust. The Bayesian Units brings up a formalism where implicitly, using probabilities, we work with uncertainly. Some experimental data are provided to validate the correctness of this approach.

A cognitive model for autonomous agents based on bayesian programming

This paper presents a cognitive model for an autonomous agent based on emotional psychology and Bayesian programming. A robot with emotional responses allows us to plan behaviour in a different way than present robotic architectures and provides us with a method of generating a new interface for human/robot interaction. The use of emotional modules means that the emotional state of the robot can be obtained directly and, therefore, it is relatively simple to obtain a virtual face that represents these emotions. An autonomous agent could have a model of the environment to be able to interact with the real universe where it is working. It is necessary to consider that any model of a real phenomenon will be incomplete due to the existence of uncertain, unknown variables that influence the phenomenon. Two example arquitectures are proposed here. Using these architectures some experimental data, to verify the correctness of this approach, is provided.

Selection of an automated morphological gradient threshold for image segmentation

Segmentation is an essential part of practically any automated image recognition system, since it is necessary for further processing such as feature extraction or object recognition. There exist a variety of techniques for threshold selection, as it is a fast, simple and robust method. Threshold value will have considerable effects on the boundary position and overall size of the extracted objects. In this work, we propose an automated thresholding selection, which takes into account the local properties of a pixel. To do this, the algorithm calculates the morphological gradient and Laplacian and, afterwards, chooses a suitable threshold after estimating the lowest distance between the ideal segmentation and the morphological gradient thresholding segmentation.

On intelligent interface agents for human based computation

In this paper a new type of interface agent will be presented. This agent is oriented to model systems for human based computation. This kind of computation, that we consider a logical extension of intelligent agent paradigm, emerges as valid approach for the resolution of complex problems. Firstly an study of the state of the art of interface agents will be review. Next, human based computation will be defined and we will see how is necessary to extend the current typology of interface agents to model this new kind of computation. In addition, a new type of interface agent, oriented to model this type of computational system, will be presented. Finally, two of the most representative applications of human based computation will be specified using this new typology.