José A. Piedra

An ontology-based modeling of an ocean satellite image retrieval system

The need to access information in large volumes of image data, e.g. big images, large images archives, distributed image repositories, etc, has stimulated the research in the field of Content-based Image Retrieval during last decade [DJLW08, LZLM07, Han08]. Many new concepts have been developed and prototyped. However, the dramatic increase in volume, details, diversity and complexity and the user demand for simultaneous access to multi-domain data urgently requires new approaches for image information mining, multi-domain information management, knowledge management and sharing.

ACA Multiagent System for Satellite Image Classification

In this paper, we present a multiagent system for satellite image classification. With this aim we will describe a new classification algorithm based on cellular automata called ACA (Algorithm based on Cellular Automata). This algorithm can be modeled by agents. Actually, there are different classification algorithms, such as minimum distance and parallelepiped classifiers, but none is fullreliable in terms of quality. One of the main advantages of ACA is to provide a mechanism which offers a hierarchical classification divided into levels of reliability with a final quality optimized through contextual techniques. Finally, we have developed a multiagent system which allows to classify satellite images in the SOLERES framework.

Ontology-Based Modelling of Ocean Satellite Images

In this paper we will define an ontology about the semantic content of ocean satellite images in which we are able to represent types of ocean structures, spatial and morphological concepts, and knowledge about measures of temperature, chrolophyll concentration, among others. Such ontology will provide the basis of a classification system based on the low-level features of images. We have tested our approach using the Protege semantic web tool.

Pattern recognition in AVHRR images by means of hibryd and neuro-fuzzy systems

The main goal of this work is to improve the automatic interpretation of ocean satellite images. We present a comparative study of different classifiers: Graphic Expert System (GES), ANN-based Symbolic Processing Element (SPE), Hybrid System (ANN – Radial Base Function & Fuzzy System), Neuro-Fuzzy System and Bayesian Network.. We wish to show the utility of hybrid and neuro-fuzzy system in recongnition of oceanic structures. On the other hand, other objective is the feature selection, which is considered a fundamental step for pattern recognition. This paper reports a study of learning Bayesian Network for feature selection [1] in the recognition of oceanic structures in satellite images.

Stigma-Stop: A Serious Game against the Stigma toward Mental Health in Educational Settings

This paper presents the results from the application of a serious game called Stigma-Stop among a group of high school students with the aim of reducing the stigma toward mental illnesses. The video game features characters with various mental disorders (schizophrenia, depression, bipolar disorder, and panic disorder with agoraphobia) and provides information about these problems. Additionally, the game asks players about whether they have ever felt the same as the characters, if they believe the characters are psychologically well, and if they think they could help these individuals. Similarly, a variety of reactions are provided for players to choose from when they encounter the characters with these problems. A total of 552 students between the ages of 14 and 18 participated in the study, and they were randomly assigned to either the experimental group, which used Stigma-Stop, or the control group, which utilized a video game completely unrelated to mental health. Both video games were used for similar lengths of time. Following the application of Stigma-Stop, a statistically significant decrease was obtained in levels of stigma toward schizophrenia, both in terms of stereotypes and, to a greater extent, its potential dangerousness. However, this was not the case in the control group. Results thus demonstrate the video game’s usefulness toward eradicating erroneous notions about serious mental disorders like schizophrenia.

Assessment of the usefulness and appeal of stigma-stop by psychology students: a serious game designed to reduce the stigma of mental illness

Article history: Received: 05 March, 2017 Accepted: 11 April, 2017 Online: 24 April, 2017 The present work describes the first serious game designed to reduce the stigma among students towards mental health problems. The game is called Stigma-Stop, and it features characters who suffer from various mental disorders. Players are provided with information about different mental illnesses, and they are presented with several options on how to act when they encounter characters with these problems. In addition, the game questions the participants as to whether they have ever felt like the individuals portrayed in the game, with the goal of fostering empathy for those that suffer from these types of disorders. Stigma-Stop was applied to a sample of second-year university Psychology students to evaluate the game’s usefulness and appeal. The results demonstrate the importance of this game and that these students consider it to be highly useful. The most notable characteristics are described in depth in the present paper.

Extraction of Knowledge Rules for the Retrieval of Mesoscale Oceanic Structures in Ocean Satellite Images

The processing of ocean satellite images has as goal the detection of phenomena related with ocean dynamics. In this context, Mesoscale Oceanic Structures (MOS) play an essential role. In this chapter we will present the tool developed in our group in order to extract knowledge rules for the retrieval of MOS in ocean satellite images. We will describe the implementation of the tool: the workflow associated with the tool, the user interface, the class structure, and the database of the tool. Additionally, the experimental results obtained with the tool in terms of fuzzy knowledge rules as well as labeled structures with these rules are shown. These results have been obtained with the tool analyzing chlorophyll and temperature images of the Canary Islands and North West African coast captured by the SeaWiFS and MODIS-Aqua sensors.

Mobile System to Guide Blind People in a Well-Known Environment

The system aids to low-vision disabled people to move in a well-known environment, at the University of Almeria. So, a 3G mobile phone has been used in order to help blind people to have a better standard of life because they could go from a building to other using his mobile phone. Therefore, audible instructions are used to inform the user, which will have been decided in advance by the computer vision module (the decision will be taken according to the environment). That module captures images in real time (using the camera of the mobile phone, which has to be over the chest of the user, fixed with a string) and recognizes static objects in the middle of the way. Moreover, it tries to avoid the situation in which the user is going out of the way. On the other hand, an application has been developed so as to supervise all the users of the system and even visualize their position.