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Author Luis Chuquimarca, Boris Vintimilla & Sergio Velastin openurl 
  Title Banana Ripeness Level Classification using a Simple CNN Model Trained with Real and Synthetic Datasets. Type Conference Article
  Year 2023 Publication accepted in 18th International Conference on Computer Vision Theory and Applications VISAPP 2023 Abbreviated Journal  
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  Notes Approved no  
  Call Number cidis @ cidis @ Serial 202  
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Author Luis Chuquimarca, Renzo Pacheco, Paula Gonzalez, Boris Vintimilla & Sergio Velastin openurl 
  Title Fruit defect detection using CNN models with real and virtual data. Type Conference Article
  Year 2023 Publication accepted in 18th International Conference on Computer Vision Theory and Applications VISAPP 2023 Abbreviated Journal  
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  Notes Approved no  
  Call Number cidis @ cidis @ Serial 203  
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Author Emmanuel Moran, Boris Vintimilla & Miguel Realpe openurl 
  Title Towards a Robust Solution for the Supermarket Shelf Audit Problem. Type Conference Article
  Year 2023 Publication accepted in 18th International Conference on Computer Vision Theory and Applications VISAPP 2023 Abbreviated Journal  
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  Notes Approved no  
  Call Number cidis @ cidis @ Serial 204  
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Author Patricia Suarez & Angel Sappa openurl 
  Title Toward a thermal image-like representation Type Conference Article
  Year 2023 Publication accepted in 18th International Conference on Computer Vision Theory and Applications VISAPP 2023 Abbreviated Journal  
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  Notes Approved no  
  Call Number cidis @ cidis @ Serial 205  
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Author Steven Silva, Nervo Verdezoto, Dennys Paillacho, Samuel Millan-Norman & Juan David Hernandez openurl 
  Title Online Social Robot Navigation in Indoor, Large and Crowded Environments. Type Conference Article
  Year 2023 Publication accepted in IEEE International Conference on Robotics and Automation (ICRA 2023) Abbreviated Journal  
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  Call Number cidis @ cidis @ Serial 206  
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Author Armin Mehri; Parichehr Behjati; Angel Domingo Sappa url  openurl
  Title TnTViT-G: Transformer in Transformer Network for Guidance Super Resolution. Type Journal Article
  Year 2023 Publication IEEE Access Abbreviated Journal  
  Volume Vol. 11 Issue Pages pp. 11529 - 11540  
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  Call Number cidis @ cidis @ Serial 207  
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Author Marjorie Chalen; Boris X. Vintimilla pdf  openurl
  Title Towards Action Prediction Applying Deep Learning Type Journal Article
  Year 2019 Publication Latin American Conference on Computational Intelligence (LA-CCI); Guayaquil, Ecuador; 11-15 Noviembre 2019 Abbreviated Journal  
  Volume Issue Pages  
  Keywords (up) action prediction, early recognition, early detec- tion, action anticipation, cnn, deep learning, rnn, lstm.  
  Abstract Considering the incremental development future action prediction by video analysis task of computer vision where it is done based upon incomplete action executions. Deep learning is playing an important role in this task framework. Thus, this paper describes recently techniques and pertinent datasets utilized in human action prediction task.  
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  Call Number cidis @ cidis @ Serial 129  
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Author Jacome-Galarza L.-R pdf  openurl
  Title Crop yield prediction utilizing multimodal deep learning Type Conference Article
  Year 2021 Publication 16th Iberian Conference on Information Systems and Technologies, CISTI 2021, junio 23 – 26, 2021 Abbreviated Journal  
  Volume Issue Pages  
  Keywords (up) Agricultura de precisión; sensores remotos; aprendizaje profundo multimodal; IoT; agentes inteligentes; computación aplicada.  
  Abstract La agricultura de precisión es una práctica vital para

mejorar la producción de cosechas. El presente trabajo tiene

como objetivo desarrollar un modelo multimodal de aprendizaje

profundo que es capaz de producir un mapa de salud de

cosechas. El modelo recibe como entradas imágenes multiespectrales

y datos de sensores de campo (humedad,

temperatura, estado del suelo, etc.) y crea un mapa de

rendimiento de la cosecha. La utilización de datos multimodales

tiene como finalidad extraer patrones ocultos del estado de salud

de las cosechas y de esta manera obtener mejores resultados que

los obtenidos mediante los índices de vegetación.		  
  Address  
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  Language Español Summary Language Original Title  
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  Notes Approved no  
  Call Number cidis @ cidis @ Serial 150  
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Author M. Oliveira; L. Seabra Lopes; G. Hyun Lim; S. Hamidreza Kasaei; Angel D. Sappa; A. Tomé pdf  url
openurl 
  Title Concurrent Learning of Visual Codebooks and Object Categories in Open- ended Domains Type Conference Article
  Year 2015 Publication Intelligent Robots and Systems (IROS), 2015 IEEE/RSJ International Conference on, Hamburg, Germany, 2015 Abbreviated Journal  
  Volume Issue Pages 2488 - 2495  
  Keywords (up) Birds, Training, Legged locomotion, Visualization, Histograms, Object recognition, Gaussian mixture model  
  Abstract In open-ended domains, robots must continuously learn new object categories. When the training sets are created offline, it is not possible to ensure their representativeness with respect to the object categories and features the system will find when operating online. In the Bag of Words model, visual codebooks are usually constructed from training sets created offline. This might lead to non-discriminative visual words and, as a consequence, to poor recognition performance. This paper proposes a visual object recognition system which concurrently learns in an incremental and online fashion both the visual object category representations as well as the codebook words used to encode them. The codebook is defined using Gaussian Mixture Models which are updated using new object views. The approach contains similarities with the human visual object recognition system: evidence suggests that the development of recognition capabilities occurs on multiple levels and is sustained over large periods of time. Results show that the proposed system with concurrent learning of object categories and codebooks is capable of learning more categories, requiring less examples, and with similar accuracies, when compared to the classical Bag of Words approach using codebooks constructed offline.  
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  Publisher IEEE Place of Publication Hamburg, Germany Editor  
  Language English Summary Language English Original Title  
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  Area Expedition Conference 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)  
  Notes Approved no  
  Call Number cidis @ cidis @ Serial 41  
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Author Carlos Monsalve; Alain April; Alain Abran pdf  openurl
  Title Requirements Elicitation Using BPM Notations: Focusing on the Strategic Level Representation Type Conference Article
  Year 2011 Publication 10th WSEAS international conference on Applied computer and applied computational science Abbreviated Journal  
  Volume Issue Pages 235-241  
  Keywords (up) Business process modeling, levels of abstraction, requirements elicitation, case study, action research  
  Abstract Business process models (BPM) can be useful for requirements elicitation, among other uses. Since the active participation of all stakeholders is a key factor for successful requirements engineering, it is important that BPM be shared by all stakeholders. Unfortunately, organizations may end up with inconsistent BPM not covering all stakeholders’ needs and constraints. The use of multiple levels of abstraction (MLA), such as at the strategic, tactical and operational levels, is often used in various process-oriented initiatives to facilitate the consolidation of various stakeholders’ needs and constraints. This article surveys the use of MLA in recent BPM research publications and reports on a BPM action-research case study conducted in a Canadian organization, with the aim of exploring the usefulness of the strategic level.  
  Address CIDIS – Electrical and Computer Engineering Department Escuela Superior Politécnica del Litoral Km. 30.5 vía Perimetral  
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  Publisher Place of Publication 1100 rue Notre-Dame Ouest, Montréal, Québec H3C 1K3 CANADA Editor  
  Language English Summary Language English Original Title  
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  Notes Approved no  
  Call Number cidis @ cidis @ Serial 16  
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