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Author | Patricia L. Suarez, Dario Carpio, Angel Sappa | ||||
Title | Depth Map Estimation from a Single 2D Image | Type | Conference Article | ||
Year | 2023 | Publication | 17th International Conference On Signal Image Technology & Internet Based Systems | Abbreviated Journal | |
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Call Number | cidis @ cidis @ | Serial | 226 | ||
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Author | Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla | ||||
Title | Colorizing Infrared Images through a Triplet Condictional DCGAN Architecture | Type | Conference Article | ||
Year | 2017 | Publication | 19th International Conference on Image Analysis and Processing. | Abbreviated Journal | |
Volume | Issue | Pages | 287-297 | ||
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Call Number | gtsi @ user @ | Serial | 66 | ||
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Author | Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla | ||||
Title | Cross-spectral Image Patch Similarity using Convolutional Neural Network | Type | Conference Article | ||
Year | 2017 | Publication | 2017 IEEE International Workshop of Electronics, Control, Measurement, Signals and their application to Mechatronics (ECMSM) | Abbreviated Journal | |
Volume | Issue | Pages | 1-5 | ||
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Call Number | cidis @ cidis @ | Serial | 57 | ||
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Author | Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla; Riad I. Hammoud | ||||
Title | Near InfraRed Imagery Colorization | Type | Conference Article | ||
Year | 2018 | Publication | 25 th IEEE International Conference on Image Processing, ICIP 2018 | Abbreviated Journal | |
Volume | Issue | Pages | 2237-2241 | ||
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Abstract | This paper proposes a stacked conditional Generative Adversarial Network-based method for Near InfraRed (NIR) imagery colorization. We propose a variant architecture of Generative Adversarial Network (GAN) that uses multiple loss functions over a conditional probabilistic generative model. We show that this new architecture/loss-function yields better generalization and representation of the generated colored IR images. The proposed approach is evaluated on a large test dataset and compared to recent state of the art methods using standard metrics.1 Index Terms—Convolutional Neural Networks (CNN), Generative Adversarial Network (GAN), Infrared Imagery colorization. |
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Call Number | gtsi @ user @ | Serial | 81 | ||
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Author | Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla | ||||
Title | Learning Image Vegetation Index through a Conditional Generative Adversarial Network | Type | Conference Article | ||
Year | 2017 | Publication | 2nd IEEE Ecuador Tehcnnical Chapters Meeting (ETCM) | Abbreviated Journal | |
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Call Number | gtsi @ user @ | Serial | 70 | ||
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Author | Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla; Riad I. Hammoud | ||||
Title | Image Vegetation Index through a Cycle Generative Adversarial Network | Type | Conference Article | ||
Year | 2019 | Publication | Conference on Computer Vision and Pattern Recognition Workshops (CVPR 2019); Long Beach, California, United States | Abbreviated Journal | |
Volume | Issue | Pages | 1014-1021 | ||
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Abstract | This paper proposes a novel approach to estimate the Normalized Difference Vegetation Index (NDVI) just from an RGB image. The NDVI values are obtained by using images from the visible spectral band together with a synthetic near infrared image obtained by a cycled GAN. The cycled GAN network is able to obtain a NIR image from a given gray scale image. It is trained by using unpaired set of gray scale and NIR images by using a U-net architecture and a multiple loss function (gray scale images are obtained from the provided RGB images). Then, the NIR image estimated with the proposed cycle generative adversarial network is used to compute the NDVI index. Experimental results are provided showing the validity of the proposed approach. Additionally, comparisons with previous approaches are also provided. |
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Call Number | gtsi @ user @ | Serial | 106 | ||
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Author | Patricia L. Suarez | ||||
Title | Procesamiento y representación de imágenes multiespectrales usando técnicas de aprendizaje profundo (Ph.D. Angel Sappa, Director & Ph.D. Boris Vintimilla, Codirector.). Ph.D. thesis. | Type | Book Chapter | ||
Year | 2020 | Publication | Ediciones FIEC-ESPOL. | Abbreviated Journal | |
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Corporate Author | Ph.D. Angel Sappa, Director & Ph.D. Boris Vintimilla, Codirector. | Thesis | |||
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Language | Español | Summary Language | Original Title | ||
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Call Number | cidis @ cidis @ | Serial | 144 | ||
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Author | Raul A. Mira; Patricia L. Suarez; Rafael E. Rivadeneira; Angel D. Sappa | ||||
Title | PETRA: A Crowdsourcing-Based Platform for Rocks Data Collection and Characterization | Type | Conference Article | ||
Year | 2019 | Publication | IEEE ETCM 2019 Fourth Ecuador Technical Chapters Meeting; Guayaquil, Ecuador | Abbreviated Journal | |
Volume | Issue | Pages | 1-6 | ||
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Abstract | This paper presents details of a distributed platform intended for data acquisition, evaluation, storage and visualization, which is fully implemented under the crowdsourcing paradigm. The proposed platform is the result from collaboration between computer science and petrology researchers and it is intended for academic purposes. The platform is designed within a MTV (Model, Template and View) architecture and also designed for a collaborative data store and managing of rocks from multiple readers and writers, taking advantage of ubiquity of web applications, and neutrality of researchers from different communities to validate the data. The platform is being used and validated by students and academics from our university; in the near future it will be open to other users interested on this topic. |
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Call Number | gtsi @ user @ | Serial | 112 | ||
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Author | Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla | ||||
Title | Adaptive Harris Corners Detector Evaluated with Cross-Spectral Images | Type | Conference Article | ||
Year | 2018 | Publication | International Conference on Information Technology & Systems (ICITS 2018). ICITS 2018. Advances in Intelligent Systems and Computing | Abbreviated Journal | |
Volume | 721 | Issue | Pages | ||
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Abstract | This paper proposes a novel approach to use cross-spectral images to achieve a better performance with the proposed Adaptive Harris corner detector comparing its obtained results with those achieved with images of the visible spectra. The images of urban, field, old-building and country category were used for the experiments, given the variety of the textures present in these images, with which the complexity of the proposal is much more challenging for its verification. It is a new scope, which means improving the detection of characteristic points using crossspectral images (NIR, G, B) and applying pruning techniques, the combination of channels for this fusion is the one that generates the largest variance based on the intensity of the merged pixels, therefore, it is that which maximizes the entropy in the resulting Cross-spectral images. Harris is one of the most widely used corner detection algorithm, so any improvement in its efficiency is an important contribution in the field of computer vision. The experiments conclude that the inclusion of a (NIR) channel in the image as a result of the combination of the spectra, greatly improves the corner detection due to better entropy of the resulting image after the fusion, Therefore the fusion process applied to the images improves the results obtained in subsequent processes such as identification of objects or patterns, classification and/or segmentation. |
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Notes | 1 | Approved | no | ||
Call Number | gtsi @ user @ | Serial | 84 | ||
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Author | Henry O. Velesaca; Raul A. Mira; Patricia L. Suarez; Christian X. Larrea; Angel D. Sappa. | ||||
Title | 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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Language | English | Summary Language | Original Title | ||
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ISSN | 21607508 | ISBN | 978-172819360-1 | Medium | |
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Call Number | cidis @ cidis @ | Serial | 124 | ||
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