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Author Rivadeneira R.E., Sappa A.D., Vintimilla B.X., Nathan S., Kansal P., Mehri A et al. pdf  openurl
  Title Thermal Image Super-Resolution Challenge – PBVS 2021. Type Conference Article
  Year 2021 Publication (down) In IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2021., junio 19 – 25, 2021 Abbreviated Journal  
  Volume Issue Pages 4354-4362  
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  Notes Approved no  
  Call Number cidis @ cidis @ Serial 151  
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Author Mehri, A, Ardakani, P.B., Sappa, A.D. pdf  openurl
  Title MPRNet: Multi-Path Residual Network for Lightweight Image Super Resolution. Type Conference Article
  Year 2021 Publication (down) In IEEE Winter Conference on Applications of Computer Vision WACV 2021, enero 5-9, 2021 Abbreviated Journal  
  Volume Issue Pages 2703-2712  
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  Notes Approved no  
  Call Number cidis @ cidis @ Serial 148  
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Author Suárez P. pdf  openurl
  Title Processing and Representation of Multispectral Images Using Deep Learning Techniques Type Magazine Article
  Year 2021 Publication (down) In Electronic Letters on Computer Vision and Image Analysis Abbreviated Journal  
  Volume Vol. 19 Issue Issue 2 Pages pp. 5-8  
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  Corporate Author Ph.D. Angel Sappa, Director & Ph.D. Boris Vintimilla, Codirector Thesis Master's thesis  
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  Language Español Summary Language Original Title  
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  Notes Approved yes  
  Call Number cidis @ cidis @ Serial 122  
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Author Velesaca, H.O., Suárez, P. L., Mira, R., & Sappa, A.D. pdf  openurl
  Title Computer Vision based Food Grain Classification: a Comprehensive Survey Type Journal Article
  Year 2021 Publication (down) In Computers and Electronics in Agriculture Journal. (Article number 106287) Abbreviated Journal  
  Volume Vol. 187 Issue Pages  
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  Notes Approved no  
  Call Number cidis @ cidis @ Serial 159  
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Author Dennys Paillacho; Cecilio Angulo; Marta Díaz. pdf  openurl
  Title An Exploratory Study of Group-Robot Social Interactions in a Cultural Center Type Conference Article
  Year 2015 Publication (down) IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2015, International Conference on, Hamburg, Germany, 2015 Abbreviated Journal  
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  Abstract This article describes an exploratory study of social human-robot interaction with the experimental robotic platform MASHI. The experiences were carried out in La B`obila Cultural Center in Barcelona, Spain to study the visitor preferences, characterize the groups and their spatial relationships in this open and unstructured environment. Results showed that visitors prefers to play and dialogue with the robot. Children have the highest interest in interacting with the robot, more than young and adult visitors. Most of the groups consisted of more than 3 visitors, however the size of the groups during interactions was continuously changed. In static situations, the observed spatial relationships denotes a social cohesion in the human-robot interactions.  
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  Notes Approved no  
  Call Number gtsi @ user @ Serial 67  
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Author Cristhian A. Aguilera; Angel D. Sappa; R. Toledo pdf  url
openurl 
  Title LGHD: A feature descriptor for matching across non-linear intensity variations Type Conference Article
  Year 2015 Publication (down) IEEE International Conference on, Quebec City, QC, 2015 Abbreviated Journal  
  Volume Issue Pages 178 - 181  
  Keywords Feature descriptor, multi-modal, multispectral, NIR, LWIR  
  Abstract This paper presents a new feature descriptor suitable to the task of matching features points between images with nonlinear intensity variations. This includes image pairs with significant illuminations changes, multi-modal image pairs and multi-spectral image pairs. The proposed method describes the neighbourhood of feature points combining frequency and spatial information using multi-scale and multi-oriented Log- Gabor filters. Experimental results show the validity of the proposed approach and also the improvements with respect to the state of the art.  
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  Publisher IEEE Place of Publication Quebec City, QC, Canada Editor  
  Language English Summary Language English Original Title  
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  Area Expedition Conference 2015 IEEE International Conference on Image Processing (ICIP)  
  Notes Approved no  
  Call Number cidis @ cidis @ Serial 40  
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Author Steven Silva, Nervo Verdezoto, Dennys Paillacho, Samuel Millan-Norman & Juan David Hernandez doi  isbn
openurl 
  Title Online Social Robot Navigation in Indoor, Large and Crowded Environments. Type Conference Article
  Year 2023 Publication (down) IEEE International Conference on Robotics and Automation (ICRA 2023) Abbreviated Journal  
  Volume 2023-May Issue Pages 9749 - 9756  
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  ISSN 10504729 ISBN 979-835032365-8 Medium  
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  Notes Approved no  
  Call Number cidis @ cidis @ Serial 206  
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Author Patricia L. Suárez, Angel D. Sappa, Boris X. Vintimilla pdf  openurl
  Title Cycle generative adversarial network: towards a low-cost vegetation index estimation Type Conference Article
  Year 2021 Publication (down) IEEE International Conference on Image Processing (ICIP 2021) Abbreviated Journal  
  Volume 2021-September Issue Pages 2783-2787  
  Keywords CyclicGAN, NDVI, near infrared spectra, instance normalization.  
  Abstract This paper presents a novel unsupervised approach to estimate the Normalized Difference Vegetation Index (NDVI).The NDVI is obtained as the ratio between information from the visible and near infrared spectral bands; in the current work, the NDVI is estimated just from an image of the visible spectrum through a Cyclic Generative Adversarial Network (CyclicGAN). This unsupervised architecture learns to estimate the NDVI index by means of an image translation between the red channel of a given RGB image and the NDVI unpaired index’s image. The translation is obtained by means of a ResNET architecture and a multiple loss function. Experimental results obtained with this unsupervised scheme show the validity of the implemented model. Additionally, comparisons with the state of the art approaches are provided showing improvements with the proposed approach.  
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  Notes Approved no  
  Call Number cidis @ cidis @ Serial 164  
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Author Cristhian A. Aguilera; Francisco J. Aguilera; Angel D. Sappa; Ricardo Toledo pdf  openurl
  Title Learning crossspectral similarity measures with deep convolutional neural networks Type Conference Article
  Year 2016 Publication (down) IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) Workshops Abbreviated Journal  
  Volume Issue Pages 267-275  
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  Abstract The simultaneous use of images from different spectra can be helpful to improve the performance of many com- puter vision tasks. The core idea behind the usage of cross- spectral approaches is to take advantage of the strengths of each spectral band providing a richer representation of a scene, which cannot be obtained with just images from one spectral band. In this work we tackle the cross-spectral image similarity problem by using Convolutional Neural Networks (CNNs). We explore three different CNN archi- tectures to compare the similarity of cross-spectral image patches. Specifically, we train each network with images from the visible and the near-infrared spectrum, and then test the result with two public cross-spectral datasets. Ex- perimental results show that CNN approaches outperform the current state-of-art on both cross-spectral datasets. Ad- ditionally, our experiments show that some CNN architec- tures are capable of generalizing between different cross- spectral domains.  
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  Notes Approved no  
  Call Number cidis @ cidis @ Serial 48  
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Author Cristina L. Abad; Yi Lu; Roy H. Campbell pdf  url
openurl 
  Title DARE: Adaptive Data Replication for Efficient Cluster Scheduling Type Conference Article
  Year 2011 Publication (down) IEEE International Conference on Cluster Computing, 2011 Abbreviated Journal  
  Volume Issue Pages 159 - 168  
  Keywords MapReduce, replication, scheduling, locality  
  Abstract Placing data as close as possible to computation is a common practice of data intensive systems, commonly referred to as the data locality problem. By analyzing existing production systems, we confirm the benefit of data locality and find that data have different popularity and varying correlation of accesses. We propose DARE, a distributed adaptive data replication algorithm that aids the scheduler to achieve better data locality. DARE solves two problems, how many replicas to allocate for each file and where to place them, using probabilistic sampling and a competitive aging algorithm independently at each node. It takes advantage of existing remote data accesses in the system and incurs no extra network usage. Using two mixed workload traces from Facebook, we show that DARE improves data locality by more than 7 times with the FIFO scheduler in Hadoop and achieves more than 85% data locality for the FAIR scheduler with delay scheduling. Turnaround time and job slowdown are reduced by 19% and 25%, respectively.  
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  Notes Approved yes  
  Call Number cidis @ cidis @ Serial 21  
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