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Author Patricia L. Suárez, Angel D. Sappa, Boris X. Vintimilla
Title (up) 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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Call Number cidis @ cidis @ Serial 164
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Author Cristina L. Abad; Yi Lu; Roy H. Campbell
Title (up) DARE: Adaptive Data Replication for Efficient Cluster Scheduling Type Conference Article
Year 2011 Publication 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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Call Number cidis @ cidis @ Serial 21
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Author Jorge L. Charco; Boris X. Vintimilla; Angel D. Sappa
Title (up) 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
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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 Henry O. Velesaca; Raul A. Mira; Patricia L. Suarez; Christian X. Larrea; Angel D. Sappa.
Title (up) 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
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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.
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ISSN 21607508 ISBN 978-172819360-1 Medium
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Notes Approved no
Call Number cidis @ cidis @ Serial 124
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Author Patricia Suarez, Henry Velesaca, Dario Carpio, Angel Sappa, Patricia Urdiales, Francisca Burgos
Title (up) Deep Learning based Shrimp Classification Type Conference Article
Year 2022 Publication 17th International Symposium on Visual Computing, San Diego, USA, Octubre 3-5. Lecture Notes in Computer Science (LNCS) Abbreviated Journal
Volume 13598 LNCS Issue Pages 36-45
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Call Number cidis @ cidis @ Serial 194
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Author Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla; Riad I. Hammoud
Title (up) 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
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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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Notes Approved no
Call Number gtsi @ user @ Serial 83
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Author Boris Vintimilla, Jorge Vulgarin, Henry Velesaca
Title (up) Deep Learning-based Human Height Estimation from a Stereo Vision System Type Conference Article
Year 2023 Publication IEEE 13th International Conference on Pattern Recognition Systems (ICPRS) 2023, julio 4-7 Abbreviated Journal
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ISSN ISBN 979-835033337-4 Medium
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Notes Approved no
Call Number cidis @ cidis @ Serial 215
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Author Henry Velesaca, Boris Vintimilla, Jorge Vulgarin, Coen Antens & Alberto Rubio Pérez
Title (up) Deep Learning-based Multimodal Sensing Framework for AntiSpoofing Systems Type Journal Article
Year 2024 Publication In Fourth International Conference on Innovations in Computational Intelligence and Computer Vision (ICICV 2024) Abbreviated Journal
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Call Number cidis @ cidis @ Serial 238
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Author Patricia L. Suárez, Angel D. Sappa and Boris X. Vintimilla
Title (up) 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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Call Number cidis @ cidis @ Serial 137
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Author Xavier Soria, Angel Sappa, Patricio Humanante, Arash Akbarinia
Title (up) Dense extreme inception network for edge detection. Type Journal Article
Year 2023 Publication Pattern Recognition Abbreviated Journal
Volume Vol. 139 Issue Pages
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ISSN 00313203 ISBN Medium
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Notes Approved no
Call Number cidis @ cidis @ Serial 216
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