D. González-Ortega

A Kinect-based system for cognitive rehabilitation exercises monitoring

In this paper, a 3D computer vision system for cognitive assessment and rehabilitation based on the Kinect device is presented. It is intended for individuals with body scheme dysfunctions and left-right confusion. The system processes depth information to overcome the shortcomings of a previously presented 2D vision system for the same application. It achieves left and right-hand tracking, and face and facial feature detection (eye, nose, and ears) detection. The system is easily implemented with a consumer-grade computer and an affordable Kinect device and is robust to drastic background and illumination changes. The system was tested and achieved a successful monitoring percentage of 96.28%. The automation of the human body parts motion monitoring, its analysis in relation to the psychomotor exercise indicated to the patient, and the storage of the result of the realization of a set of exercises free the rehabilitation experts of doing such demanding tasks. The vision-based system is potentially applicable to other tasks with minor changes.

Wavelet‐Based Denoising for Traffic Volume Time Series Forecasting with Self‐Organizing Neural Networks

In their goal to effectively manage the use of existing infrastructures, intelligent transportation systems require precise forecasting of near-term traffic volumes to feed real-time analytical models and traffic surveillance tools that alert of network links reaching their capacity. This article proposes a new methodological approach for short-term predictions of time series of volume data at isolated cross sections. The originality in the computational modeling stems from the fit of threshold values used in the stationary wavelet-based denoising process applied on the time series, and from the determination of patterns that characterize the evolution of its samples over a fixed prediction horizon. A self-organizing fuzzy neural network is optimized in its configuration parameters for learning and recognition of these patterns. Four real-world data sets from 3 interstate roads are considered for evaluating the performance of the proposed model. A quantitative comparison made with the results obtained by 4 other relevant prediction models shows a favorable outcome.

Recognition of coloured and textured images through a multi-scale neural architecture with orientational filtering and chromatic diffusion

The aim of this paper is to outline a multiple scale neural model to recognise colour images of textured scenes. This model combines colour and textural information in order to recognise colour texture images through the operation of two main components: a segmentation component composed of the colour opponent system (COS) and the chromatic segmentation system (CSS); and a recognition component formed by an ARTMAP-based neural network with scale and orientation-invariance properties. Segmentation is achieved by perceptual contour extraction and diffusion processes on the colour opponent channels based on the human psychophysical theory of colour perception. This colour regions enhancement along with their local textural features constitutes the recognition pattern to be sent to the supervised neural classifier. The CSS accomplishes the colour region enhancement through a multiple scale loop of oriented filters and competition-cooperation mechanisms. Afterwards, the neural architecture performs an attentive recognition of the scene using those oriented filters responses and the chromatic diffusions. Some comparative tests with other models are included in order to prove the recognition capabilities of this neural architecture and how the use of colour information encourages the texture classification and the accuracy of the boundary detection.

Real-time hands, face and facial features detection and tracking: Application to cognitive rehabilitation tests monitoring

In this paper, a marker-free computer vision system for cognitive rehabilitation tests monitoring is presented. The system monitors and analyzes the correct and incorrect realization of a set of psicomotricity exercises in which a hand has to touch a facial feature. The monitoring requires different human body parts detection and tracking. Detection of face, eyes, nose, and hands is achieved with a set of classifiers built independently based on the AdaBoost algorithm. Comparisons with other detection approaches, regarding performance and applicability to the monitoring system, are presented. Face and hands tracking is accomplished through the CAMShift algorithm with independent and adaptive two-dimensional histograms of the chromaticity components of the TSL color space for the pixels inside these three regions. The TSL color space was selected after a study of five color spaces regarding skin color characterization. The system is easily implemented with a consumer-grade computer and a camera, unconstrained background and illumination and runs at more than 23 frames per second. The system was tested and achieved a successful monitoring percentage of 97.62%. The automation of the human body parts motion monitoring, its analysis in relation to the psicomotricity exercise indicated to the patient and the storage of the result of the realization of a set of exercises free the rehabilitation experts of doing such demanding tasks. The vision-based system is potentially applicable to other human-computer interface tasks with minor changes.

Double recurrent interaction V1–V2–V4 based neural architecture for color natural scene boundary detection and surface perception

Abstract In this paper, a new neural model for bio-inspired processing of color images, called dPREEN (double recurrent Perceptual boundaRy dEtection Neural) model, is presented. The dPREEN model includes a double feedback among V1, V2 and V4 cortical areas, simple and double color opponent processes, orientation filtering using Gabor kernels, surround suppression in complex cells, top-down and bottom-up information fusion and chromatic diffusion, to generate contours of perceptual significance in color natural scenes. The outputs of the model are a boundary map of the scene and surface perception images. This paper incorporates a comparative analysis of the proposed model against two other contour extraction methods in the Berkeley Segmentation Dataset and Benchmark. The analysis shows favorable results to the dPREEN model. Additionally, this paper describes two parallel implementations of the model for its execution on Graphics Processing Units.

AdaBoost face detection on the gpu using Haar-like features

Face detection is a time consuming task in computer vision applications. In this article, an approach for AdaBoost face detection using Haar-like features on the GPU is proposed. The GPU adapted version of the algorithm manages to speed-up the detection process when compared with the detection performance of the CPU using a well-known computer vision library. An overall speed-up of × 3.3 is obtained on the GPU for video resolutions of 640×480 px when compared with the CPU implementation. Moreover, since the CPU is idle during face detection, it can be used simultaneously for other computer vision tasks.

Computer vision architecture for real-time face and hand detection and tracking

In this paper we present a computer vision architecture to detect and track the face and hands of a human being in real time from a video sequence captured by a webcam. The architecture has a first preprocessing stage, including a color filtering module, a motion filtering module, a color-based segmentation, a processing channels merge module and, finally, a contour search and discrimination module. The aim of the first stage is to discard the image regions which are highly unlikely to correspond with skin. Thus, the second stage of the architecture is a previously trained Fuzzy ARTMAP multiscale neural network module which only processes those image regions selected by the preprocessing stage, which are fully expected to be skin. The neural networks make the last decision about face and hand detection. After that, the architecture tracks the trajectories which face and hands follow.

Moodle-Based Software to Support the Learning of Web Programming

New educational trends demand learning processes that fulfill the requirements and interests of students. In this sense, developing new activities focused on specific themes for the open source e-learning platform Moodle, provides the added value of offering their integrated use in the learning environment. Basing on this assumption, Moodle applications for checking JavaScript and PHP codes have been developed, allowing improving the learning process in web programming University courses. These applications offer the students information about the committed errors and about the key terms of the programming language. Moreover, they also gather information about the type of errors committed by each student so that the teacher can graphically observe which concepts are more problematic and need to be clarified. The paper also describes the result of a qualitative analysis of its use in several courses offered in study programmes of the University of Valladolid.

Analysis of the benefits and constraints for the implementation of cloud computing over an EHRs system

The Cloud Computing paradigm means a radical change over the IT technologies. This transform offers us many benefits in terms of e-services. Cloud Computing offers us a new solution for the implementation of electronic management system in a huge variety of fields. So the e-health is included on these solutions. Despite the fact that Cloud Computing is under development there are a lot of opportunities of implementation of Cloud Computing over e-health services. So in this paper we are going to discuss the viability of the implementation of this new model over an Electronic Health Records (EHRs) system. To find an answer of this issue we are going to analyze the benefits and constraints that can be given in this kind of systems.

Texture Classification of the Entire Brodatz Database through an Orientational-Invariant Neural Architecture

This paper presents a supervised neural architecture, called SOON, for texture classification. Multi-scale Gabor filtering is used to extract the textural features which shape the input to a neural classifier with orientation invariance properties in order to accomplish the classification. Three increasing complexity tests over the well-known Brodatz database are performed to quantify its behavior. The test simulations, including the entire texture album classification, show the stability and robustness of the SOON response.