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Author Ricaurte P; Chilán C; Cristhian A. Aguilera; Boris X. Vintimilla; Angel D. Sappa pdf  url
openurl 
  Title (down) Feature Point Descriptors: Infrared and Visible Spectra Type Journal Article
  Year 2014 Publication Sensors Journal Abbreviated Journal  
  Volume Vol. 14 Issue Pages pp. 3690-3701  
  Keywords cross-spectral imaging; feature point descriptors  
  Abstract This manuscript evaluates the behavior of classical feature point descriptors when they are used in images from long-wave infrared spectral band and compare them with the results obtained in the visible spectrum. Robustness to changes in rotation, scaling, blur, and additive noise are analyzed using a state of the art framework. Experimental results using a cross-spectral outdoor image data set are presented and conclusions from these experiments are given.  
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  Language English Summary Language English Original Title  
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  Area Expedition Conference  
  Notes Approved no  
  Call Number cidis @ cidis @ Serial 28  
Permanent link to this record
 
 
Author Cristhian A. Aguilera, Cristhian Aguilera, Cristóbal A. Navarro, & Angel D. Sappa pdf  openurl
  Title (down) Fast CNN Stereo Depth Estimation through Embedded GPU Devices Type Journal Article
  Year 2020 Publication Sensors 2020 Abbreviated Journal  
  Volume Vol. 2020-June Issue 11 Pages pp. 1-13  
  Keywords stereo matching; deep learning; embedded GPU  
  Abstract Current CNN-based stereo depth estimation models can barely run under real-time

constraints on embedded graphic processing unit (GPU) devices. Moreover, state-of-the-art

evaluations usually do not consider model optimization techniques, being that it is unknown what is

the current potential on embedded GPU devices. In this work, we evaluate two state-of-the-art models

on three different embedded GPU devices, with and without optimization methods, presenting

performance results that illustrate the actual capabilities of embedded GPU devices for stereo depth

estimation. More importantly, based on our evaluation, we propose the use of a U-Net like architecture

for postprocessing the cost-volume, instead of a typical sequence of 3D convolutions, drastically

augmenting the runtime speed of current models. In our experiments, we achieve real-time inference

speed, in the range of 5–32 ms, for 1216  368 input stereo images on the Jetson TX2, Jetson Xavier,

and Jetson Nano embedded devices.		  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language English Summary Language English Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 14248220 ISBN Medium  
  Area Expedition Conference  
  Notes Approved no  
  Call Number cidis @ cidis @ Serial 132  
Permanent link to this record
 
 
Author Angel D. Sappa, Patricia L. Suárez, Henry O. Velesaca, Darío Carpio pdf  openurl
  Title (down) Domain adaptation in image dehazing: exploring the usage of images from virtual scenarios. Type Conference Article
  Year 2022 Publication 16th International Conference on Computer Graphics, Visualization, Computer Vision and Image Processing (CGVCVIP 2022), julio 20-22 Abbreviated Journal  
  Volume Issue Pages 85-92  
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  Series Editor Series Title Abbreviated Series Title  
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  Area Expedition Conference  
  Notes Approved no  
  Call Number cidis @ cidis @ Serial 182  
Permanent link to this record
 
 
Author Xavier Soria; Edgar Riba; Angel D. Sappa pdf  isbn
openurl 
  Title (down) Dense Extreme Inception Network: Towards a Robust CNN Model for Edge Detection Type Conference Article
  Year 2020 Publication 2020 IEEE Winter Conference on Applications of Computer Vision (WACV) Abbreviated Journal  
  Volume Issue 9093290 Pages 1912-1921  
  Keywords  
  Abstract This paper proposes a Deep Learning based edge de- tector, which is inspired on both HED (Holistically-Nested Edge Detection) and Xception networks. The proposed ap- proach generates thin edge-maps that are plausible for hu- man eyes; it can be used in any edge detection task without previous training or fine tuning process. As a second contri- bution, a large dataset with carefully annotated edges, has been generated. This dataset has been used for training the proposed approach as well the state-of-the-art algorithms for comparisons. Quantitative and qualitative evaluations have been performed on different benchmarks showing im- provements with the proposed method when F-measure of ODS and OIS are considered.  
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  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN 978-172816553-0 Medium  
  Area Expedition Conference  
  Notes Approved no  
  Call Number cidis @ cidis @ Serial 126  
Permanent link to this record
 
 
Author Patricia L. Suárez, Angel D. Sappa and Boris X. Vintimilla url  openurl
  Title (down) Deep learning-based vegetation index estimation Type Book Chapter
  Year 2021 Publication Generative Adversarial Networks for Image-to-Image Translation Book. Abbreviated Journal  
  Volume Chapter 9 Issue Issue 2 Pages 205-232  
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  Notes Approved no  
  Call Number cidis @ cidis @ Serial 137  
Permanent link to this record
 
 
Author Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla; Riad I. Hammoud pdf  openurl
  Title (down) Deep Learning based Single Image Dehazing Type Conference Article
  Year 2018 Publication 14th IEEE Workshop on Perception Beyond the Visible Spectrum – In conjunction with CVPR 2018. Salt Lake City, Utah. USA Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  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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  Series Editor Series Title Abbreviated Series Title  
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  Area Expedition Conference  
  Notes Approved no  
  Call Number gtsi @ user @ Serial 83  
Permanent link to this record
 
 
Author Henry O. Velesaca; Raul A. Mira; Patricia L. Suarez; Christian X. Larrea; Angel D. Sappa. pdf  isbn
openurl 
  Title (down) Deep Learning based Corn Kernel Classification. Type Conference Article
  Year 2020 Publication The 1st International Workshop and Prize Challenge on Agriculture-Vision: Challenges & Opportunities for Computer Vision in Agriculture on the Conference Computer on Vision and Pattern Recongnition (CVPR 2020) Abbreviated Journal  
  Volume 2020-June Issue 9150684 Pages 294-302  
  Keywords  
  Abstract This paper presents a full pipeline to classify sample sets of corn kernels. The proposed approach follows a segmentation-classification scheme. The image segmentation is performed through a well known deep learning based

approach, the Mask R-CNN architecture, while the classification is performed by means of a novel-lightweight network specially designed for this task—good corn kernel, defective corn kernel and impurity categories are considered.

As a second contribution, a carefully annotated multitouching corn kernel dataset has been generated. This dataset has been used for training the segmentation and

the classification modules. Quantitative evaluations have been performed and comparisons with other approaches provided showing improvements with the proposed pipeline.		  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 21607508 ISBN 978-172819360-1 Medium  
  Area Expedition Conference  
  Notes Approved no  
  Call Number cidis @ cidis @ Serial 124  
Permanent link to this record
 
 
Author Jorge L. Charco; Boris X. Vintimilla; Angel D. Sappa pdf  openurl
  Title (down) Deep learning based camera pose estimation in multi-view environment. Type Conference Article
  Year 2018 Publication 14th IEEE International Conference on Signal Image Technology & Internet based Systems (SITIS 2018) Abbreviated Journal  
  Volume Issue Pages 224-228  
  Keywords  
  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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  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
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  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes Approved no  
  Call Number gtsi @ user @ Serial 93  
Permanent link to this record
 
 
Author Patricia L. Suárez, Angel D. Sappa, Boris X. Vintimilla pdf  openurl
  Title (down) Cycle generative adversarial network: towards a low-cost vegetation index estimation Type Conference Article
  Year 2021 Publication 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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  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
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  Area Expedition Conference  
  Notes Approved no  
  Call Number cidis @ cidis @ Serial 164  
Permanent link to this record
 
 
Author N. Onkarappa; Cristhian A. Aguilera; B. X. Vintimilla; Angel D. Sappa pdf  url
openurl 
  Title (down) Cross-spectral Stereo Correspondence using Dense Flow Fields Type Conference Article
  Year 2014 Publication Computer Vision Theory and Applications (VISAPP), 2014 International Conference on, Lisbon, Portugal, 2014 Abbreviated Journal  
  Volume 3 Issue Pages 613 - 617  
  Keywords Cross-spectral Stereo Correspondence, Dense Optical Flow, Infrared and Visible Spectrum  
  Abstract This manuscript addresses the cross-spectral stereo correspondence problem. It proposes the usage of a dense flow field based representation instead of the original cross-spectral images, which have a low correlation. In this way, working in the flow field space, classical cost functions can be used as similarity measures. Preliminary experimental results on urban environments have been obtained showing the validity of the proposed approach.  
  Address  
  Corporate Author Thesis  
  Publisher IEEE Place of Publication Editor  
  Language English Summary Language English Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference 2014 International Conference on Computer Vision Theory and Applications (VISAPP)  
  Notes Approved no  
  Call Number cidis @ cidis @ Serial 27  
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