Juan Villegas-Cortez

Self organizing natural scene image retrieval

In this work we describe a new statistically-based methodology to organize and retrieve images of natural scenes by combining feature extraction, automatic clustering, automatic indexing and classification techniques. Our proposal belongs to the content-based image retrieval (CBIR) category. Our goal is to retrieve images from an image database by their content. The methodology combines randomly extracted points for feature extraction. The describing features are the mean, the standard deviation and the homogeneity (from the co-occurrence matrix) of a sub-image extracted from the three color channels (HSI). A K-means algorithm and a 1-NN classifier are used to build an indexed database. Three databases of images of natural scenes are used during the training and testing processes. One of the advantages of our proposal is that the images are not labeled manually for their retrieval. The performance of our framework is shown through several experimental results, including a comparison with several classifiers and comparison with related works, achieving up to 100% good recognition. Additionally, our proposal includes scene retrieval.

EEG Pattern Recognition: An Efficient Improvement Combination of ERD/ERS/Laterality Features to Create a Self-paced BCI System

In this paper, a new method based on an efficient improvement combination of Event-Related Desynchronization (ERD), Event-Related Synchronization (ERS) and lateral activity of sensorimotor cortex features is presented to analyze both left and right hand motor imagery tasks. Our proposal uses delta, theta, alfa and beta rhythms to BCI system. From the spectral power, an efficient combination of ERD/ERS/laterality features was used. Because electroencephalogram signals are non-stationary type and highly vary over time and frequency, a detailed time-frequency analysis is applied. Features coming from time-frequency analysis, where eight frequency bands ranging from 0 to 32 Hz were chosen. Features vectors are classified by Gaussian classifier and the final performance is evaluated in cross-validation scheme. This novel approach was tested using the BCI competition IV data set 1. The detection of the left and right hand motor imagery task was very good, with a result of \(96.4\,\%\) using BCI-Competition -IV. When comparing results from others competing methods reported in the literature, our approach resulted the best and useful to create a self-paced BCI-system.

Monocular Visual Odometry Based Navigation for a Differential Mobile Robot with Android OS

In this work, a real time Monocular Visual Odometry system to estimate camera position and orientation based solely on image measurements is proposed. The system is built on the basis of the fundamentals of Structure from Motion theory, and requires only a single camera to estimate positional information. Experiments were conducted on flat ground, under controlled light conditions environment, in which and an Android mobile device camera was employed as the processor and the system sensor due to ease of acquisition and low price. The proposed system resulted in absolute navigation error rates ranging from 0.14% to 0.4% of the travelled distance at processing rates of up to 5Hz.

A Genetic Algorithm Applied to Content-Based Image Retrieval for Natural Scenes Classification

The Content-Based Image Retrieval (CBIR) techniques comprise methodologies intended to retrieve self-content descriptors over the image data set being studied according to the type of the image. The main purpose of CBIR consists in classifying images avoiding the use of manual labels related to understanding of the image by the human being vision. In this work we provide a new CBIR procedure which works with local texture analysis, and which is developed in a non supervised fashion, clustering the local achieved descriptors and classifying them with the use of a K-means algorithm supported by the genetic algorithm. This method has been deployed in LabVIEW software, programming each part of the procedure in order to implement it in hardware. The results are very promising, reaching up to 90% of recall for natural scene classification.

EEG PATTERN RECOGNITION: Application to a Real Time Control System for Android-Based Mobile Devices

This paper describes a new EEG pattern recognition methodology in Brain Computer Interface (BCI) field. The EEG signal is analyzed in real time looking for detection of “intents of movement”. The signal is processed at specific segments in order to classify mental tasks then a message is formulated and sent to a mobile device to execute a command. The signal analysis is carried out through eight frequency bands within the range of 0 to 32 Hz. A feature vector is conformed using histograms of gradients according to 4 orientations, subsequently the features feed a Gaussian classifier. Our methodology was tested using BCI Competition IV data sets I. For “intents of movements” we detect up to 95% with 0.2 associated noise, with mental task differentiation around 99%. This methodology has been tested building a prototype using an Android based mobile telephone and data gathered with an EPOC Emotive headset, showing very promising results.

Automatic synthesis of associative memories through genetic programming: a first co-evolutionary approach

Associative Memories (AMs) are mathematical structures specially designed to associate input patterns with output patterns within a single stage. Since the last fifty years all reported AMs have been manually designed. The paper describes a Genetic Programming based methodology able to create a process for the automatic synthesis of AMs. It paves a new area of research that permits for the first time to propose new AMs for solving specific problems. In order to test our methodology we study the application of AMs for real value patterns. The results illustrate that it is possible to automatically generate AMs that achieve good recall performance for problems commonly used in pattern recognition research.

EEG Signal Implementation of Movement Intention for the Teleoperation of the Mobile Differential Robot

In the year 1929 a German psychiatrist, named Hans Berger, demonstrated for the first time that the electric activity of the human brain was related to the person’s mental state. He also announced the possibility of registering such type of electric activities without opening the human head, i.e. non invasive procedure , and that such electric activities could be plotted on a graph. Berger called such type of registration as electroencephalogram (EEG). EEG signals research has been growing over the years due to the their increasing use to control electronic devices in all sorts of contexts. The present work developed a prototype to control a differential robot by means of EEG signals using the detection of movement intention of the right and left hand. The study covered on one hand, the analysis and design of the teleoperation system, and on the other hand, the robot tele operational tests. It is important to point out that the robot was designed and built to meet the technical research purposes. The programming of the EEG signal processing was made using the API provided by MATLAB. In turn, the programming for controlling the mobile differential robot was made with Wiring and Python. Lastly, several tests and experiments were carried out, and they showed that the objective in view was met.

Evolutionary Associative Memories through Genetic Programming

Associative Memories (AMs) are useful devices designed to recall output patterns from input patterns. Each input-output pair forms an association. Thus, AMs store associations among pairs of patterns. An important feature is that since its origins AMs have been manually designed. This way, during the last 50 years about 26 different models and variations have been reported. In this paper, we illustrate how new models of AMs can be automatically generated through Genetic Programming (GP) based methodology. In particular, GP provides a way to successfully facilitate the search for an AM in the form of a computer program. The efficiency of the proposal was conducted by means of two tests based on binary and real-valued patterns. The experimental results show that it is possible to automatically generate AMs that achieve good results for the selected pattern recognition problems. This opens a new research area that allows, for the first time, synthesizing new AMs to solve specific problems.

Systematic Simulation of Disordered Cylindrical Nanoporous Materials

This paper describes a simple method, based on the Dual Site–Bond Model, for constructing disordered cylindrical porous substrata. Heterogeneity is accounted for by allowing the presence of variable cross-sectional areas along the length of the pore tubes forming the solid. The degree of heterogeneity is easily controlled with overlapping between the pore-size distributions of pore cavities and pore necks. Some simulated materials with different textural properties are presented. An analysis of the texture of the generated structures, together with their nitrogen adsorption characteristics, has been carried out and is discussed.