Examinando por browse.metadata.type "info:eu-repo/semantics/conferenceObject"
  • Publicación
    3D Pose Estimation Oriented to the Initialization of an Augmented Reality System Applied to Cultural Heritage
    (Digital Cultural Heritage, 2018)
    Rodriguez, RM
    ;
    Aguilar, R
    ;
    Uceda, S
    ;
    Castaneda, B
    La realidad aumentada (AR) aplicada al patrimonio cultural pretende mejorar la experiencia de aprendizaje en sitios arqueológicos, no solo para los visitantes sino también para los investigadores. La estimación de Pose 3D es un problema común en aplicaciones para AR, reconocimiento de objetos, modelado 3D, entre otros. Los sistemas AR utilizan diferentes métodos para estimar la pose de la cámara: detección de bordes y detección de puntos clave, entre otros. La elección del método a utilizar depende de las características del escenario a detectar. En este trabajo se realiza un estudio comparativo de los principales métodos de estimación de pose basados ​​en modelos 3D. Además, presentamos la implementación y validación de un algoritmo de estimación de pose, orientado a la inicialización de un sistema AR aplicado a la “Huaca de la Luna”, una pirámide de ladrillos de adobe construida por la civilización Moche en el norte del Perú. El algoritmo propuesto presenta dos fases, una fase de entrenamiento, donde se extraen puntos clave 3D de una imagen de referencia, y una fase de detección, donde el proceso de inicialización se realiza comparando la correspondencia de puntos 2D/3D utilizando un algoritmo PnP. Hemos comparado cuatro variaciones del algoritmo de estimación de poses 3D utilizando diferentes métodos: descriptores SIFT y SURF para la descripción de puntos clave y algoritmos EPnP y REPPnP para la estimación de poses PnP. Los resultados muestran un error de traducción de 1,54 cm, con un tiempo medio de procesamiento de 2,78 s, un error máximo de reproyección de 1,5 píxeles y una tasa de estimación exitosa del 100 % en escenarios con condiciones de luz normales y altas.
  • Publicación
    3M: una historia de innovación
    (Consejo Nacional de Ciencia, Tecnología e Innovación Tecnológica - Concytec, 2014-11)
    Palenque Velasco, Luis Fernando
    Ponencia presentada en el Foro "Políticas para la gestión de CTI y casos exitosos", realizado el día 14 de noviembre de 2014, en la ciudad de Lima.
  • Publicación
    5ta Feria Internacional de Flores, Plantas y Paisaje PERUFLORA 2014 "Gestión, Innovación y Biodiversidad"
    (Asociación Peruana de Arquitectura del Paisaje, 2014-09-25)
    Asociación Peruana de Arquitectura de Paisaje
    Los asistentes pudieron disfrutar y ampliar sus conocimientos sobre la floricultura mediante las conferencias.
  • Publicación
    A Comparison of Digital Modelling Techniques Analyzing a Section of Qhapaq Nan
    (IEEE Xplore, 2015)
    Retamozo, S
    ;
    Arce, D
    ;
    Aguilar, R
    ;
    Zvietcovich, F
    ;
    Quintana, M
    ;
    Castaneda, B
    ;
    Angeles, S
    Total Station has been one of the most common acquisition devices for achieving maps through topographic survey. Nowadays, Terrestrial Laser Scanner (TLS) and Photogrammetry are commonly used to generate accurate meshes. In addition, commercial products such as Kinect offer low cost technology to acquire point-cloud information. The present paper aims to measure the accuracy of these digital modelling techniques by employing elevation contour maps, surface deviations and distance measurements. For this purpose, a 450 m sector of the Qhapaq Nan located in Lima-Peru, was selected as a case of study. A camera-enabled drone was used for acquiring pictures to obtain a high-resolution photogrammetric model. Subsequently, a 3D survey of the monument was conducted with a time-of-flight laser scanner. Contour elevation lines where extracted from TLS, Photogrammetry and Total Station models at the same depths in order to determine the precision of photogrammetry and laser scanner reconstructions. In addition, geometrical comparisons were performed among the 3D models above mentioned and the Kinect sensor. The comparison showed that TLS is the most accurate tool for 3D reconstruction. However, Photogrammetry and Kinect provided errors of less than one centimeter in accuracy.
  • Publicación
    A computer algorithm for detection of tuberculosis bacilli in Ziehl Nellsen sputum smear images based on the adjustment of RGB primary component tones and geometric eccentricity
    (International Institute of Informatics and Systemics, IIIS, 2017)
    Del Carpio C.
    ;
    Dianderas E.
    ;
    Zimick M.
    ;
    Sheen P.
    ;
    Coronel J.
    ;
    Fuentes P.
    ;
    Kemper G.
    The present study proposes a method of automatic detection of tuberculosis (TB) bacilli from digital images of Ziehl Neelsen sputum smear baciloscopy. The method is based on an algorithm that aims to automate the interpretation of optical microscopic images of sputum smears. According to the World Health Organization (WHO), a specialist can not analyze and process more than 20 samples per day (in order to not affect the analysis sensitivity and commit errors in diagnosis). Therefore, an automated tool as the proposed here, is an important contribution to the current efforts to fight tuberculosis. The algorithm is based on geometric eccentricity of ellipses and improvement of RGB component tones. Correspondence functions adjusted to sample preparation conditions were applied in order to improve the RGB primary component tones of the image. This allows to obtain an adequate segmentation of interest objects. For the recognition of each object as bacillus, the geometric descriptor of eccentricity of the ellipse was applied. The algorithm was validated with 66 independent sputum samples from TB patients. A sensitivity of 88.75% and a specificity of 95.5% was obtained for the diluted pellet method for sample preparation. © by the International Institute of Informatics and Systemics.
  • Publicación
    A deep learning approach for sentiment analysis in Spanish Tweets
    (Springer Verlag, 2018)
    Vizcarra G.
    ;
    Mauricio A.
    ;
    Mauricio L.
    Sentiment Analysis at Document Level is a well-known problem in Natural Language Processing (NLP), being considered as a reference in NLP, over which new architectures and models are tested in order to compare metrics that are also referents in other issues. This problem has been solved in good enough terms for English language, but its metrics are still quite low in other languages. In addition, architectures which are successful in a language do not necessarily works in another. In the case of Spanish, data quantity and quality become a problem during data preparation and architecture design, due to the few labeled data available including not-textual elements (like emoticons or expressions). This work presents an approach to solve the sentiment analysis problem in Spanish tweets and compares it with the state of art. To do so, a preprocessing algorithm is performed based on interpretation of colloquial expressions and emoticons, and trivial words elimination. Processed sentences turn into matrices using the 3 most successful methods of word embeddings (GloVe, FastText and Word2Vec), then the 3 matrices merge into a 3-channels matrix which is used to feed our CNN-based model. The proposed architecture uses parallel convolution layers as k-grams, by this way the value of each word and their contexts are weighted, to predict the sentiment polarity among 4 possible classes. After several tests, the optimal tuple which improves the accuracy were <1, 2>. Finally, our model presents %61.58 and %71.14 of accuracy in InterTASS and General Corpus respectively.
  • Publicación
    A Low-Cost IoT Platform for Heat Stress Monitoring in Dairy Cattle
    (Institute of Electrical and Electronics Engineers Inc., 2021)
    Choquehuanca-Zevallos J.J.
    ;
    Mayhua-Lopez E.
    This paper presents a compact and modular system based on Internet-of- Things for monitoring cattle behavior and stress in real-time. It will help to model certain parameters such as temperature and certain weather variables such as relative humidity, solar radiation, among others thanks to Internet-of- Things (IoT) sensors localized in different points of barns and the fields for cattle farming. A main benefit of the system is that it is built with low-cost hardware and low battery consumption. The wireless system also allows the collection of data in real-time and obtains the temperature-humidity index. This index will give an approach to the heat stress in cattle not only on the farm but in the vicinity of the farm. Finally, the high amount of collected data will allow employing Big Data solutions for estimating the impact on milk productivity. In the future, more sensors will be deployed for a more detailed reading of weather variables and their impact on dairy cattle. © 2021 IEEE.
  • Publicación
    A Low-Resourced Peruvian Language Identification Model
    (CEUR-WS, 2017)
    Linares A.E.
    ;
    Oncevay-Marcos A.
    Due to the linguistic revitalization in Peru´ through the last years, there is a growing interest to reinforce the bilingual education in the country and to increase the research focused in its native languages. From the computer science perspective, one of the first steps to support the languages study is the implementation of an automatic language identification tool using machine learning methods. Therefore, this work focuses in two steps: (1) the building of a digital and annotated corpus for 16 Peruvian native languages extracted from documents in web repositories, and (2) the fit of a supervised learning model for the language identification task using features identified from related studies in the state of the art, such as ngrams. The obtained results were promising (97% in average precision), and it is expected to take advantage of the corpus and the model for more complex tasks in the future.
  • Publicación
    A mapping approach for real time imitation of human movements by a 22 DOF humanoid
    (IEEE, 2018)
    Cornejo-Arismendi V.A.
    ;
    Barrios-Aranibar D.
    The main way of displacement of a humanoid robot is by walking, humanoid robots have a basic architecture of 22 DOF which are the minimum necessary to replicate human movements. A motion capture system stores the information of a human being from static points in a human body, the data used will be cycles of gait of a human being. The proposed technique transforms the data of a capture system and transforms them into angles in an architecture of a humanoid robot of 22 DOF. For this purpose it uses key points of a capture system and makes a mapping from the torso to then proceed with its upper and lower limbs. Tests were performed on an author's own simulator and also on the V-REP simulator using the architecture of the Poopy robot. The results show a visually imperceptibly mathematical error in the simulator, but numerically measurable, that lies in the elimination of an axial axis located at the waist. Tests were performed with the data of a woman, a man and a child, being the woman who has the greatest error for having a more pronounced hip movement in the gait. This proposed research opens the door for future research that requires a mapping of a capture system to be replicated in a humanoid robot of 22 DOF, being its use very versatile and expandable to dynamic solutions of balance and tightness.
  • Publicación
    A mixed methodology for detailed 3D modeling of architectural heritage
    (Tayler & Francis Group, 2016)
    Arce, D
    ;
    Retamozo, S
    ;
    Aguilar, R
    ;
    Castaneda, B
    Architectural and archaeological heritage is the most representative part of history of a country. For these reasons, over the years, the scientific community has focused its attention on three-dimensional (3D) modeling techniques, conservation, structural analysis and other related applications. Laser scanning, shape from structured light, shape from silhouette, shape from video and shape from photometry are among the techniques that have been used over the past years for 3D modeling of archeological heritage. The application of 3D modeling can be found in several disciplines. In Architecture, it is used to generate digital models from which drawings can be produced. Aerial and terrestrial photogrammetry Photogrammetry is commonly used for digitization of 3D models in different applications due to the fast acquisition process and low cost of equipment. Digitization of structures with terrestrial laser scanner requires the usage of a correct methodology in order to generate a complete 3D model.
  • Publicación
    A multi-modal visual emotion recognition method to instantiate an ontology
    (SciTePress, 2021)
    Heredia J.P.A.
    ;
    Cardinale Y.
    ;
    Dongo I.
    ;
    Díaz-Amado J.
    Human emotion recognition from visual expressions is an important research area in computer vision and machine learning owing to its significant scientific and commercial potential. Since visual expressions can be captured from different modalities (e.g., face expressions, body posture, hands pose), multi-modal methods are becoming popular for analyzing human reactions. In contexts in which human emotion detection is performed to associate emotions to certain events or objects to support decision making or for further analysis, it is useful to keep this information in semantic repositories, which offers a wide range of possibilities for implementing smart applications. We propose a multi-modal method for human emotion recognition and an ontology-based approach to store the classification results in EMONTO, an extensible ontology to model emotions. The multi-modal method analyzes facial expressions, body gestures, and features from the body and the environment to determine an emotional state; this processes each modality with a specialized deep learning model and applying a fusion method. Our fusion method, called EmbraceNet+, consists of a branched architecture that integrates the EmbraceNet fusion method with other ones. We experimentally evaluate our multi-modal method on an adaptation of the EMOTIC dataset. Results show that our method outperforms the single-modal methods. Copyright © 2021 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
  • Publicación
    A nonlinear model to estimate Nitrogen level in agricultural soil using Gaussian Kernels
    (IEEE, 2016)
    Sanchez-Mora, K
    ;
    Zuniga-Gutierrez, MA
    ;
    Mayhua-Lopez, E
    Nitrogen fertilizers are commonly used to improve agricultural productivity. However, its excessive use may cause or lead to environmental problems. Therefore, technologies capable of monitoring and measure levels of nitrogen in agricultural soil in-situ and in real time are required in order to make efficient the use of fertilizers. Nitrogen levels are usually measured by direct and indirect methods. Direct methods can be conducted in-situ or in laboratory, but they are really expensive and/or little resistant to soil conditions.
  • Publicación
    A Novel Fuzzy Probabilistic Clustering Algorithm for Satellite Image Segmentation
    (2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2018)
    Mantilla, L
    ;
    Yari, Y
    ;
    Meza-Lovon, G
    Satellite Image Segmentation is a task widely investigate since we can extract and analyze information of an image. In satellite image, the information of each one of the bands must be considered. We propose a new method based on the New Fuzzy Centroid Model and includes spatial information. Furthermore, we use the occurrence of each intensity value in a particular band and the Gaussian function in order to compute the degree of contribution of pixels in the neighborhood. By incorporating spatial information (global and local), we improve the clustering process and consequently, a better segmentation is obtained. This paper reports preliminary results of experiments that show that the proposed algorithm performs accurately on a real data set. For the evaluation of the algorithm, different cluster validity indexes are employed.
  • Publicación
    A numerical algorithm for finding solutions of a generalized Nash equilibrium problem
    (Springer Nature, 2012)
    Matioli L.C.
    ;
    Sosa W.
    ;
    Yuan J.
    A family of nonempty closed convex sets is built by using the data of the Generalized Nash equilibrium problem (GNEP). The sets are selected iteratively such that the intersection of the selected sets contains solutions of the GNEP. The algorithm introduced by Iusem-Sosa (Optimization 52:301–316, 2003) is adapted to obtain solutions of the GNEP. Finally some numerical experiments are given to illustrate the numerical behavior of the algorithm.
  • Publicación
    A parametric 3D-printed body-powered hand prosthesis based on the four-bar linkage mechanism
    (Institute of Electrical and Electronics Engineers Inc., 2018)
    Bustamante M.
    ;
    Vega-Centeno R.
    ;
    Sánchez M.
    ;
    Mio R.
    The widespread of 3D-printing technology has resulted in the appearance of many open-source prosthetic hand models, especially for partial hand amputations. However, most of these designs are not editable and while some are parametric to some degree, customization for every user is limited to scaling the size of a base design. As consequence, most prostheses fail to closely match the user specific anthropometry and have poor aesthetics, which could result in abandonment of the device. Furthermore, achieving a high degree of customization could be a time-consuming task and requires previous knowledge of CAD design. This work presents a prosthetic hand easy to customize by changing parametric dimensions of the finger phalanges and palm on an Excel sheet. Additionally, the design tackles common issues from previous 3D-printed body-powered prosthetic hands by incorporating new features such as the use of linkages instead of cables as finger flexors and a new cable-adjusting system which requires no additional tools and makes the tensioning of finger tendons easier and quicker.
  • Publicación
    A study of observation scales based on felzenswalb-huttenlocher dissimilarity measure for hierarchical segmentation
    (Springer Verlag, 2019)
    Cayllahua-Cahuina E.
    ;
    Cousty J.
    ;
    Guimarães S.
    ;
    Kenmochi Y.
    ;
    Cámara-Chávez G.
    ;
    de Albuquerque Araújo A.
    Hierarchical image segmentation provides a region-oriented scale-space, i.e., a set of image segmentations at different detail levels in which the segmentations at finer levels are nested with respect to those at coarser levels. Guimarães et al. proposed a hierarchical graph based image segmentation (HGB) method based on the Felzenszwalb-Huttenlocher dissimilarity. This HGB method computes, for each edge of a graph, the minimum scale in a hierarchy at which two regions linked by this edge should merge according to the dissimilarity. In order to generalize this method, we first propose an algorithm to compute the intervals which contain all the observation scales at which the associated regions should merge. Then, following the current trend in mathematical morphology to study criteria which are not increasing on a hierarchy, we present various strategies to select a significant observation scale in these intervals. We use the BSDS dataset to assess our observation scale selection methods. The experiments show that some of these strategies lead to better segmentation results than the ones obtained with the original HGB method.
  • Publicación
    A virtual reality and brain computer interface system for upper limb rehabilitation of post stroke patients
    (Institute of Electrical and Electronics Engineers Inc., 2017)
    Achanccaray D.
    ;
    Acuña K.
    ;
    Carranza E.
    ;
    Andreu-Perez J.
    This work presents a brain computer interface (BCI) framework for upper limb rehabilitation of post stroke patients, combining BCI and virtual reality (VR) technology; a VR feedback is shown to the participants to achieve a greater activation of certain brain regions involved with the performing of upper limb motor task. This system uses an adaptive neuro-fuzzy inference system (ANFIS) classifier to discriminate between a motor task and rest condition, the first one classifies between extension and rest conditions; and the second one classifies between flexion and rest conditions. In the training stage, eight healthy subjects participated in the sessions, the best accuracies are 99.3% and 88.9%, as a result of cross-validation. Meanwhile, the best accuracy in online test is 89%. The methodology here presented can be straightforwardly employed as a rehabilitation system for brain repair in individuals with neurological diseases or brain injury.
  • Publicación
    A visual analytics approach for exploration of high-dimensional time series based on Neighbor-Joining Tree
    (Association for Computing Machinery, 2018)
    Rodríguez R.
    ;
    Alfonte R.
    ;
    Cuadros A.M.
    High-dimensional time series analysis through visual techniques poses many challenges due to the visualization solutions proposed until now for exploratory tasks are not well-oriented to high volume of data. When the data sets grow large, the visual alternatives do not allow for a good association between similar time series. With the aim to increase more alternatives, we introduce a visual analytic approach based on Neighbor-Joining similarity tree. The proposed approach internally consists of five time series dimension reduction techniques widely used, two well-known similarity measures and interaction mechanisms to do exploratory analysis of high-dimensional time series data interactively.
  • Publicación
    Abierta por defecto: el plan de conocimiento abierto en la UOC
    (Consejo Nacional de Ciencia, Tecnología e Innovación Tecnológica - Concytec, 2019-09-12)
    Llueca, Ciro
    Presenta las características y cifras más resaltantes de la Universitat Oberta de Catalunya respecto a docencia, investigación, biblioteca y recursos de aprendizaje y el plan de acción "abierta por defecto".
  • Publicación
    Abnormal event detection in video using motion and appearance information
    (Springer Verlag, 2018)
    Menejes Palomino N.
    ;
    Cámara Chávez G.
    This paper presents an approach for the detection and localization of abnormal events in pedestrian areas. The goal is to design a model to detect abnormal events in video sequences using motion and appearance information. Motion information is represented through the use of the velocity and acceleration of optical flow and the appearance information is represented by texture and optical flow gradient. Unlike literature methods, our proposed approach provides a general solution to detect both global and local abnormal events. Furthermore, in the detection stage, we propose a classification by local regions. Experimental results on UMN and UCSD datasets confirm that the detection accuracy of our method is comparable to state-of-the-art methods.