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Author Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla pdf  openurl
  Title Infrared Image Colorization based on a Triplet DCGAN Architecture. Type Conference Article
  Year 2017 Publication (up) 13th IEEE Workshop on Perception Beyond the Visible Spectrum – In conjunction with CVPR 2017. (This paper has been selected as “Best Paper Award” ) Abbreviated Journal  
  Volume 2017-July Issue Pages 212-217  
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  Notes Approved no  
  Call Number cidis @ cidis @ Serial 62  
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Author Henry O. Velesaca, Juan Antonio Holgado-Terriza, Doménica Carrasco, José Miguel Gutiérrez Guerrero, Tonny Toscano, Darío Carpio & Angel Sappa pdf  openurl
  Title Anomaly Detection in Industrial Production Products using OPC-UA and Deep Learning Type Conference Article
  Year 2024 Publication (up) 13th International Conference on Data Science, Technology and Applications (DATA). Abbreviated Journal  
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  Call Number cidis @ cidis @ Serial 232  
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Author Xavier Soria; Angel D. Sappa openurl 
  Title Improving Edge Detection in RGB Images by Adding NIR Channel. Type Conference Article
  Year 2018 Publication (up) 14th IEEE International Conference on Signal Image Technology & Internet based Systems (SITIS 2018) Abbreviated Journal  
  Volume Issue Pages 266-273  
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  Call Number gtsi @ user @ Serial 95  
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Author Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla pdf  openurl
  Title Cross-spectral image dehaze through a dense stacked conditional GAN based approach. Type Conference Article
  Year 2018 Publication (up) 14th IEEE International Conference on Signal Image Technology & Internet based Systems (SITIS 2018) Abbreviated Journal  
  Volume Issue Pages 358-364  
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  Abstract This paper proposes a novel approach to remove haze from RGB images using a near infrared images based on a dense stacked conditional Generative Adversarial Network (CGAN). The architecture of the deep network implemented receives, besides the images with haze, its corresponding image in the near infrared spectrum, which serve to accelerate the learning process of the details of the characteristics of the images. The model uses a triplet layer that allows the independence learning of each channel of the visible spectrum image to remove the haze on each color channel separately. A multiple loss function scheme is proposed, which ensures balanced learning between the colors and the structure of the images. Experimental results have shown that the proposed method effectively removes the haze from the images. Additionally, the proposed approach is compared with a state of the art approach showing better results.  
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  Call Number gtsi @ user @ Serial 92  
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Author Jorge L. Charco; Boris X. Vintimilla; Angel D. Sappa pdf  openurl
  Title Deep learning based camera pose estimation in multi-view environment. Type Conference Article
  Year 2018 Publication (up) 14th IEEE International Conference on Signal Image Technology & Internet based Systems (SITIS 2018) Abbreviated Journal  
  Volume Issue Pages 224-228  
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  Abstract This paper proposes to use a deep learning network architecture for relative camera pose estimation on a multi-view environment. The proposed network is a variant architecture of AlexNet to use as regressor for prediction the relative translation and rotation as output. The proposed approach is trained from scratch on a large data set that takes as input a pair of images from the same scene. This new architecture is compared with a previous approach using standard metrics, obtaining better results on the relative camera pose.  
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  Call Number gtsi @ user @ Serial 93  
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Author Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla; Riad I. Hammoud pdf  openurl
  Title Deep Learning based Single Image Dehazing Type Conference Article
  Year 2018 Publication (up) 14th IEEE Workshop on Perception Beyond the Visible Spectrum – In conjunction with CVPR 2018. Salt Lake City, Utah. USA Abbreviated Journal  
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  Abstract This paper proposes a novel approach to remove haze

degradations in RGB images using a stacked conditional

Generative Adversarial Network (GAN). It employs a triplet

of GAN to remove the haze on each color channel independently.

A multiple loss functions scheme, applied over a

conditional probabilistic model, is proposed. The proposed

GAN architecture learns to remove the haze, using as conditioned

entrance, the images with haze from which the clear

images will be obtained. Such formulation ensures a fast

model training convergence and a homogeneous model generalization.

Experiments showed that the proposed method

generates high-quality clear images.
 
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  Call Number gtsi @ user @ Serial 83  
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Author Omar Coello, Moisés Coronel, Darío Carpio, Boris X. Vintimilla & Luis Chuquimarca openurl 
  Title Enhancing Apple’s Defect Classification: Insights from Visible Spectrum and Narrow Spectral Band Imaging Type Conference Article
  Year 2024 Publication (up) 14th International Conference on Pattern Recognition Systems (ICPRS) Londres 15 – 18 July 2024 Abbreviated Journal  
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  Call Number cidis @ cidis @ Serial 244  
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Author Luis Chuquimarca, Boris X. Vintimilla & Sergio Velastin openurl 
  Title Classifying Healthy and Defective Fruits with a Multi-Input Architecture and CNN Models Type Conference Article
  Year 2024 Publication (up) 14th International Conference on Pattern Recognition Systems (ICPRS) Londres 15 – 18 July 2024 Abbreviated Journal  
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  Call Number cidis @ cidis @ Serial 245  
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Author Patricia Suarez, Angel Sappa openurl 
  Title Depth-Conditioned Thermal-like Image Generation Type Conference Article
  Year 2024 Publication (up) 14th International Conference on Pattern Recognition Systems (ICPRS) Londres 15 – 18 July 2024 Abbreviated Journal  
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  Call Number cidis @ cidis @ Serial 243  
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Author Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla pdf  openurl
  Title Learning to Colorize Infrared Images Type Conference Article
  Year 2017 Publication (up) 15th International Conference on Practical Applications of Agents and Multi-Agent Systems Abbreviated Journal  
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  Notes Approved no  
  Call Number cidis @ cidis @ Serial 58  
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