A new self-organizing neural gas model based on Bregman divergences

In this paper, a new self-organizing neural gas model that we call Growing Hierarchical Bregman Neural Gas (GHBNG) has been proposed. Our proposal is based on the Growing Hierarchical Neural Gas (GHNG) in which Bregman divergences are incorporated in order to compute the winning neuron. This model has been applied to anomaly detection in video sequences together with a Faster R-CNN as an object detector module. Experimental results not only confirm the effectiveness of the GHBNG for the detection of anomalous object in video sequences but also its selforganization capabilities.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tec

Motion Detection by Microcontroller for Panning Cameras

Motion detection is the first essential process in the extraction of information regarding moving objects. The approaches based on background difference are the most used with fixed cameras to perform motion detection, because of the high quality of the achieved segmentation. However, real time requirements and high costs prevent most of the algorithms proposed in literature to exploit the background difference with panning cameras in real world applications. This paper presents a new algorithm to detect moving objects within a scene acquired by panning cameras. The algorithm for motion detection is implemented on a Raspberry Pi microcontroller, which enables the design and implementation of a low-cost monitoring system.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Deep learning-based anomalous object detection system powered by microcontroller for PTZ cameras

Automatic video surveillance systems are usually designed to detect anomalous objects being present in a scene or behaving dangerously. In order to perform adequately, they must incorporate models able to achieve accurate pattern recognition in an image, and deep learning neural networks excel at this task. However, exhaustive scan of the full image results in multiple image blocks or windows to analyze, which could make the time performance of the system very poor when implemented on low cost devices. This paper presents a system which attempts to detect abnormal moving objects within an area covered by a PTZ camera while it is panning. The decision about the block of the image to analyze is based on a mixture distribution composed of two components: a uniform probability distribution, which represents a blind random selection, and a mixture of Gaussian probability distributions. Gaussian distributions represent windows in the image where anomalous objects were detected previously and contribute to generate the next window to analyze close to those windows of interest. The system is implemented on a Raspberry Pi microcontroller-based board, which enables the design and implementation of a low-cost monitoring system that is able to perform image processing.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Pneumonia Detection in Chest X-ray Images using Convolutional Neural Networks

Pneumonia is an infectious and deadly disease which strikes over millions of people. Usually, chest X-rays are used by radiotherapist to diagnose pneumonia. In this paper, a Computer- Aided Diagnosis (CAD) system for pneumonia detection in chest X-ray images is proposed. This system is based on Convolutional Neural Networks (CNNs) which are able to classify the image into two classes (pneumonia or normal). Experimental results show that the proposed system obtained an accuracy rate of 98.59%.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Stenosis detection in coronary angiography images using deep learning models

The emergence of deep learning has caused its massive application to different fields in industry and research, among which is the clinical field, especially in those where the data is structured in the form of images or video. The present proposal intends to develop a coronary angiography image analysis system based on artificial intelligence. These images are radiocontrast X-ray images of the coronary arteries. The proposed system will be able to analyze these coronary angiography images of patients with no obstructive coronary lesions to detect and characterize smooth and irregular coronary arteries and predict the presence of cardiovascular events during follow-up. Deep learning convolutional artificial neural networks will support the algorithmic basis of the proposed system.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Are learning styles useful? A new software to analyze correlations with grades and a case study in engineering

Knowing student learning styles represents an effective way to design the most suitable methodology for our students so that performance can improve with less effort for both students and teachers. However, a methodology is usually set in teaching guides according to the previous academic year's information without any knowledge of our current audience. In this work, a new software for learning styles and grade analysis based on the Honey-Alonso Learning Styles Questionnaire has been proposed. This tool proposes the average learning style profiles of a given course by clustering student learning styles and analyzes the possible relation between grades and learning style profiles. By using that program, three different courses from Computer Sciences Engineering degrees during an academic year have been analyzed. The obtained results in our specific context exhibit that possible relation. This information could be useful to understand how students approach learning materials

Peer assessments in Engineering: A pilot project

The evaluation methods employed in a course are the most important point for the students, above any other learning aspect. For teachers, this task is arduous when the number of students is high. Traditional evaluation requires the teacher to grade all the assignments and exams, while peer assessments have become a valuable tool to involve students effectively in the correction of exercises. This paper applied and analyzes the evaluation through peer review in a course of Computer Sciences Engineering. A total of six assignments and a mid-term exam were evaluated by both teachers (individually) and students (cooperatively), and the differences were discussed to extract conclusions about the viability of this evaluation model.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Longitudinal study of the learning styles evolution in Engineering degrees

A learning style describes what are the predominant skills for learning tasks. In the context of university education, knowing the learning styles of the students constitutes a great opportunity to improve both teaching and evaluation. By using the Honey-Alonso Learning Styles Questionnaire (CHAEA), in this work, we carried out a longitudinal study of the correlation between marks and the results of the survey. The 2018/2019, 2019/2020, and 2020/2021 academic years in two different courses from Computer Sciences engineering degrees were studied. The results were analyzed in order to evaluate the impact of the adaptation to digital lessons during the last year.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Hierarchical Color Quantization with a Neural Gas Model Based on Bregman Divergences

In this paper, a new color quantization method based on a self-organized artificial neural network called the Growing Hierarchical Bregman Neural Gas (GHBNG) is proposed. This neural network is based on Bregman divergences, from which the squared Euclidean distance is a particular case. Thus, the best suitable Bregman divergence for color quantization can be selected according to the input data. Moreover, the GHBNG yields a tree-structured model that represents the input data so that a hierarchical color quantization can be obtained, where each layer of the hierarchy contains a different color quantization of the original image. Experimental results confirm the color quantization capabilities of this approach.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Color Space Selection for Self-Organizing Map Based Foreground Detection in Video Sequences

The selection of the best color space is a fundamental task in detecting foreground objects on scenes. In many situations, especially on dynamic backgrounds, neither grayscale nor RGB color spaces represent the best solution to detect foreground objects. Other standard color spaces, such as YCbCr or HSV, have been proposed for background modeling in the literature; although the best results have been achieved using diverse color spaces according to the application, scene, algorithm, etc. In this work, a color space and color component weighting selection process is proposed to detect foreground objects in video sequences using self-organizing maps. Experimental results are also provided using well known benchmark videos.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech