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This document describes the development of a video game prototype designed to encourage physical activity among children and older adults. The prototype consists of a laptop, a camera with 3D sensors, and optionally requires an LCD screen or a projector. The programming component of this prototype was developed in Scratch, a programming language geared towards children, which greatly facilitates the creation of a game tailored to the users' preferences. The idea to create such a prototype originated from the desire to offer an option that promotes physical activity among children and adults, given that a lack of physical exercise is a predominant factor in the development of chronic degenerative diseases such as diabetes and hypertension, to name the most common. As a result of this initiative, an active video game prototype was successfully developed, based on a ping-pong game, which allows both children and adults to interact in a fun way while encouraging the performance of physical activities that can positively impact the users' health., Comment: 13 pages, in Spanish language, 11 figures

3D sensors, also known as RGB-D sensors, utilize depth images where each pixel measures the distance from the camera to objects, using principles like structured light or time-of-flight. Advances in artificial vision have led to affordable 3D cameras capable of real-time object detection without object movement, surpassing 2D cameras in information depth. These cameras can identify objects of varying colors and reflectivities and are less affected by lighting changes. The described prototype uses RGB-D sensors for bidirectional people counting in venues, aiding security and surveillance in spaces like stadiums or airports. It determines real-time occupancy and checks against maximum capacity, crucial during emergencies. The system includes a RealSense D415 depth camera and a mini-computer running object detection algorithms to count people and a 2D camera for identity verification. The system supports statistical analysis and uses C++, Python, and PHP with OpenCV for image processing, demonstrating a comprehensive approach to monitoring venue occupancy., Comment: 8 pages, in Spanish language, 8 figures

The identification and characterization of various rock types is one of the fundamental activities for geology and related areas such as mining, petroleum, environment, industry and construction. Traditionally, a human specialist is responsible for analyzing and explaining details about the type, composition, texture, shape and other properties using rock samples collected in-situ or prepared in a laboratory. The results become subjective based on experience, in addition to consuming a large investment of time and effort. The present proposal uses artificial intelligence techniques combining computer vision and natural language processing to generate a textual and verbal description from a thin section image of rock. We build a dataset of images and their respective textual descriptions for the training of a model that associates the relevant features of the image extracted by EfficientNetB7 with the textual description generated by a Transformer network, reaching an accuracy value of 0.892 and a BLEU value of 0.71. This model can be a useful resource for research, professional and academic work, so it has been deployed through a Web application for public use., Comment: 21 pages, in Spanish language, 7 figures

This paper presents the design of an artificial vision system prototype for automatic inspection and monitoring of objects over a conveyor belt and using a Smart camera 2D BOA-INS. The prototype consists of a conveyor belt and an embedded system based on an Arduino Mega card for system control, and it has as main peripherals the smart camera, a direct current motor, a photoelectric sensor, LED illumination and LEDs indicating the status (good or defect) of each evaluated object. The application of the prototype is for educational purposes, so that undergraduate, master and diploma students can simulate a continuous production line, controlled by an embedded system, and perform quality control by monitoring through a visual system and a personal computer. This allows implementing the topics of embedded systems, artificial vision, artificial intelligence, pattern recognition, automatic control, as well as automation of real processes., Comment: 8 pages, 16 figures, in Spanish language

Volcanic eruptions emit ash that can be harmful to human health and cause damage to infrastructure, economic activities and the environment. The delimitation of ash clouds allows to know their behavior and dispersion, which helps in the prevention and mitigation of this phenomenon. Traditional methods take advantage of specialized software programs to process the bands or channels that compose the satellite images. However, their use is limited to experts and demands a lot of time and significant computational resources. In recent years, Artificial Intelligence has been a milestone in the computational treatment of complex problems in different areas. In particular, Deep Learning techniques allow automatic, fast and accurate processing of digital images. The present work proposes the use of the Pix2Pix model, a type of generative adversarial network that, once trained, learns the mapping of input images to output images. The architecture of such a network consisting of a generator and a discriminator provides the versatility needed to produce black and white ash cloud images from multispectral satellite images. The evaluation of the model, based on loss and accuracy plots, a confusion matrix, and visual inspection, indicates a satisfactory solution for accurate ash cloud delineation, applicable in any area of the world and becomes a useful tool in risk management., Comment: 18 pages, in Spanish language, 15 figures

Currently, the diagnosis of Alzheimer's disease is a complex and error-prone process. Improving this diagnosis could allow earlier detection of the disease and improve the quality of life of patients and their families. For this work, we will use 249 brain images from two modalities: PET and MRI, taken from the ADNI database, and labelled into three classes according to the degree of development of Alzheimer's disease. We propose the development of a convolutional neural network to perform the classification of these images, during which, we will study the appropriate depth of the networks for this problem, the importance of pre-processing medical images, the use of transfer learning and data augmentation techniques as tools to reduce the effects of the problem of having too little data, and the simultaneous use of multiple medical imaging modalities. We also propose the application of an evaluation method that guarantees a good degree of repeatability of the results even when using a small dataset. Following this evaluation method, our best final model, which makes use of transfer learning with COVID-19 data, achieves an accuracy d 68\%. In addition, in an independent test set, this same model achieves 70\% accuracy, a promising result given the small size of our dataset. We further conclude that augmenting the depth of the networks helps with this problem, that image pre-processing is a fundamental process to address this type of medical problem, and that the use of data augmentation and the use of pre-trained networks with images of other diseases can provide significant improvements., Comment: in Spanish language

Artificial Intelligence has enabled the implementation of more accurate and efficient solutions to problems in various areas. In the agricultural sector, one of the main needs is to know at all times the extent of land occupied or not by crops in order to improve production and profitability. The traditional methods of calculation demand the collection of data manually and in person in the field, causing high labor costs, execution times, and inaccuracy in the results. The present work proposes a new method based on Deep Learning techniques complemented with conventional programming for the determination of the area of populated and unpopulated crop areas. We have considered as a case study one of the most recognized companies in the planting and harvesting of sugar cane in Ecuador. The strategy combines a Generative Adversarial Neural Network (GAN) that is trained on a dataset of aerial photographs of natural and urban landscapes to improve image resolution; a Convolutional Neural Network (CNN) trained on a dataset of aerial photographs of sugar cane plots to distinguish populated or unpopulated crop areas; and a standard image processing module for the calculation of areas in a percentage manner. The experiments performed demonstrate a significant improvement in the quality of the aerial photographs as well as a remarkable differentiation between populated and unpopulated crop areas, consequently, a more accurate result of cultivated and uncultivated areas. The proposed method can be extended to the detection of possible pests, areas of weed vegetation, dynamic crop development, and both qualitative and quantitative quality control., Comment: 21 pages, in Spanish, 17 figures, 3 tables

The following work shows an algorithm that can process images digitally with the goal of control the movement of a mobile robotic platform in a certain environment. The platform is identified with a specific color, and displacement environment of the platform shift has identified obstacles with different colors, for both cases it worked with the RGB color scale. To obtain the control's movement of the robotic platform, the algorithm was developed in C programming language, and used the Open CV libraries for processing images captured by a video camera on the Dev-platform C + +. The video camera was previously calibrated using ZHANG technique where parameters were obtained focal length and tilt focal pixel. In the algorithm histogram analysis and segmentation of the image were developed, allowing to determine exactly the relative position of the platform with respect to the obstacles and movement strategy to follow., Comment: 6 figures,5 pages, in Spanish language

The problem that we want to solve in this project of the subject of Data Structures and Algorithms, is to decipher some images, which have in them animals, being more specific, bovine animals; in which it is necessary to identify if the animal is healthy, that is to say, if it is in good conditions to be taken into account in the process of selection of the cattle, or if it is sick, to know if it is discarded. All this by means of an algorithm of compression, which allows to take the images and to take them to an examination of these in the code, where not always the results are going to be one hundred percent exact, but what allows this code to be efficient, is that it works with machine learning, which means that the more information it takes, the more precise the results are going to be without bringing with it general affectations. The proposed algorithms are NN and bilinear interpolation, where significant results were obtained on the execution speed. It is concluded that a better job could have been done, but with what was delivered, it is believed that it is a good result of the work., Comment: in Spanish

Introduction: Mobile apps, through artificial vision, are capable of recognizing vegetable species in real time. However, the existing species recognition apps do not take in consideration the wide variety of endemic and native (Chilean) species, which leads to wrong species predictions. This study introduces the development of a chilean species dataset and an optimized classification model implemented to a mobile app. Method: the data set was built by putting together pictures of several species captured on the field and by selecting some pictures available from other datasets available online. Convolutional neural networks were used in order to develop the images prediction models. The networks were trained by performing a sensitivity analysis, validating with k-fold cross validation and performing tests with different hyper-parameters, optimizers, convolutional layers, and learning rates in order to identify and choose the best models and then put them together in one classification model. Results: The final data set was compounded by 46 species, including native species, endemic and exotic from Chile, with 6120 training pictures and 655 testing pictures. The best models were implemented on a mobile app, obtaining a 95% correct prediction rate with respect to the set of tests. Conclusion: The app developed in this study is capable of classifying species with a high level of accuracy, depending on the state of the art of the artificial vision and it can also show relevant information related to the classified species., Comment: in Spanish

Baggage inspection systems using X-ray screening are crucial for security. Only 90% of threat objects are recognized from the X-ray system based in human inspection. Manual detection requires high concentration due to the images complexity and the challenges objects points of view. An algorithm based on Bag of Visual Word (BoVW) with Selective Search is proposed in this paper for handguns detection in single energy X-ray images from the public GDXray database. This approach is an adaptation of BoVW for X-ray baggage images context. In order to evaluate the proposed method the algorithm effectiveness recognition was tested on all bounding boxes returned by selective search algorithm in 200 images. The most relevant result is the precision and true positive rate (PPV = 80%, TPR= 92%). This approach achieves good performance for handgun recognition. In addition, it is the first time the Selective Search localization algorithm was tested in baggage X-ray images and showed possibilities with Bag of Visual Words., Comment: in Spanish

Due to the growth of the population with hearing problems, devices have been developed that facilitate the inclusion of deaf people in society, using technology as a communication tool, such as vision systems. Then, a solution to this problem is presented using neural networks and autoencoders for the classification of American Sign Language images. As a result, 99.5% accuracy and an error of 0.01684 were obtained for image classification, Comment: 5 pages, in Spanish, 10 figures, 1 table

The segmentation of satellite images is a necessary step to perform object-oriented image classification, which has become relevant due to its applicability on images with a high spatial resolution. To perform object-oriented image classification, the studied image must first be segmented in uniform regions. This segmentation requires manual work by an expert user, who must exhaustively explore the image to establish thresholds that generate useful and representative segments without oversegmenting and without discarding representative segments. We propose a technique that automatically segments the multispectral image while facing these issues. We identify in the image homogenous zones according to their spectral signatures through the use of morphological filters. These homogenous zones are representatives of different types of land coverings in the image and are used as seeds for the GrowCut multispectral segmentation algorithm. GrowCut is a cellular automaton with competitive region growth, its cells are linked to every pixel in the image through three parameters: the pixel's spectral signature, a label, and a strength factor that represents the strength with which a cell defends its label. The seed cells possess maximum strength and maintain their state throughout the automaton's evolution. Starting from seed cells, each cell in the image is iteratively attacked by its neighboring cells. When the automaton stops updating its states, we obtain a segmented image where each pixel has taken the label of one of its cells. In this paper the algorithm was applied in an image acquired by Landsat8 on agricultural land of Calabozo, Guarico, Venezuela where there are different types of land coverings: agriculture, urban regions, water bodies, and savannas with different degrees of human intervention. The segmentation obtained is presented as irregular polygons enclosing geographical objects., Comment: 10 pages, in Spanish, originally presented at CIMENICS 2016, accepted to the Journal of the Faculty of Engineering UCV

In this work, we propose a new similarity index for images considering the entropy function and group theory. This index considers an algebraic group of images, it is defined by an inner law that provides a novel approach for the subtraction of images. Through an equivalence relationship in the field of images, we prove the existence of the quotient group, on which the new similarity index is defined. We also present the main properties of the new index, and the immediate application thereof as a stopping criterion of the "Mean Shift Iterative Algorithm"., Comment: in Spanish

The effects of global climate change on Peruvian glaciers have brought about several processes of deglaciation during the last few years. The immediate effect is the change of size of lakes and rivers. Public institutions that monitor water resources currently have only recent studies which make up less than 10% of the total. The effects of climate change and the lack of updated information intensify social-economic problems related to water resources in Peru. The objective of this research is to develop a software application to automate the Cadastral Registry of Water Bodies in Peru, using techniques of digital image processing, which would provide tools for detection, record, temporal analysis and visualization of water bodies. The images used are from the satellite Landsat5, which undergo a pre-processing of calibration and correction of the satellite. Detection results are archived into a file that contains location vectors and images of the segmentated bodies of water., Comment: in Spanish

A system for semi-automatic vectorization of linear networks (roads, rivers, etc.) on rasterized cartographic maps is presented. In this system, human intervention is limited to a graphic, interactive selection of the color attributes of the information to be obtained. Using this data, the system performs a preliminary extraction of the linear network, which is subsequently completed, refined and vectorized by means of an automatic procedure. Results on maps of different sources and scales are included. ----- Se presenta un sistema semi-automatico de vectorizacion de redes de objetos lineales (carreteras, rios, etc.) en mapas cartograficos digitalizados. En este sistema, la intervencion humana queda reducida a la seleccion grafica interactiva de los atributos de color de la informacion a obtener. Con estos datos, el sistema realiza una extraccion preliminar de la red lineal, que se completa, refina y vectoriza mediante un procedimiento automatico. Se presentan resultados de la aplicacion del sistema sobre imagenes digitalizadas de mapas de distinta procedencia y escala., Comment: 12 pages with 13 figures, in Spanish