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Author Wassim El Ahmar, Angel D. Sappa and Riad Hammoud openurl 
  Title Thermal Pedestrian MultipleObject Tracking Challenge (TPMOT) Type Conference Article
  Year 2025 Publication IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops CVPRW 2025 Abbreviated Journal  
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  Notes Approved no  
  Call Number cidis @ cidis @ Serial 275  
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Author Nathan Inkawhich, Claire Thorp, Justice Wheelwright, Oliver Nina, Dylan Bowald, Angel Sappa, Erik Blasch openurl 
  Title 4th Multi-modal Aerial View Image Challenge: SAR CLASSIFICATION – PBVS 2025 Type Journal Article
  Year 2025 Publication IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops CVPRW 2025 Abbreviated Journal  
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  Notes Approved no  
  Call Number cidis @ cidis @ Serial 276  
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Author Dylan Bowald, Justice Wheelwright, Oliver Nina, Angel Sappa, Riad Hammoud, Erik Blasch, Nathan Inkawhich openurl 
  Title 3th Multi-modal Aerial View Image Challenge: Sensor Domain Translation – PBVS 2025 Type Conference Article
  Year 2025 Publication IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops CVPRW 2025 Abbreviated Journal  
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  Notes Approved no  
  Call Number cidis @ cidis @ Serial 277  
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Author Patricia Suarez & Angel D. Sappa openurl 
  Title Lightweight Architecture for Fruit Quality Estimation in the Infrared Domain Type Conference Article
  Year 2025 Publication 5th International Conference on Innovations in Computational Intelligence and Computer Vision ICICV 2025 Abbreviated Journal  
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  Notes Approved no  
  Call Number cidis @ cidis @ Serial 278  
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Author Henry O. Velesaca Hector Villegas, and Angel D. Sappa openurl 
  Title Exploring Camouflaged Object Detection Techniques for Invasive Vegetation Monitoring Type Conference Article
  Year 2025 Publication 14th International Conference on Data Science, Technology and Applications DATA 2025 Abbreviated Journal  
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  Call Number cidis @ cidis @ Serial 279  
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Author Kevin E. Munoz, Loberlly N. Salazar, Steven S. Araujo, and Boris X. Vintimilla openurl 
  Title Stereo Vision Techniques: A Comparative Study of Traditional and Machine Learning-Based Approaches Type Conference Article
  Year 2025 Publication 5th International Conference on Computer Vision and Robotics CVR 2025 Abbreviated Journal  
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  Call Number cidis @ cidis @ Serial 280  
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Author Kevin E. Muñoz Loberlly N. Salazar Steven S. Araujo Boris X. Vintimilla openurl 
  Title Detecting and Characterizing Human Interactions to EnhanceHuman-Robot Engagement Type Conference Article
  Year 2025 Publication 3rd International Conference on Robotics, Control and Vision Engineering RCVE 2025 Abbreviated Journal  
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  Call Number cidis @ cidis @ Serial 281  
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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 pp. 1-3  
  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.
 
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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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