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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 | Lukas Danev; Marten Hamann; Nicolas Fricke; Tobias Hollarek; Dennys Paillacho | ||||
Title | Development of animated facial expression to express emotions in a robot: RobotIcon. | Type | Conference Article | ||
Year | 2017 | Publication | IEEE Ecuador Technical Chapter Meeting (ETCM) | Abbreviated Journal | |
Volume | 2017-January | Issue | Pages | 1-6 | |
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Call Number | gtsi @ user @ | Serial | 71 | ||
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Author | Xavier Soria; Angel D. Sappa; Arash Akbarinia | ||||
Title | Multispectral Single-Sensor RGB-NIR Imaging: New Challenges an Oppotunities | Type | Conference Article | ||
Year | 2017 | Publication | The 7th International Conference on Image Processing Theory, Tools and Application | Abbreviated Journal | |
Volume | Issue | Pages | 1-6 | ||
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Call Number | gtsi @ user @ | Serial | 72 | ||
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Author | Milton Mendieta; F. Panchana; B. Andrade; B. Bayot; C. Vaca; Boris X. Vintimilla; Dennis G. Romero | ||||
Title | Organ identification on shrimp histological images: A comparative study considering CNN and feature engineering. | Type | Conference Article | ||
Year | 2018 | Publication | IEEE Ecuador Technical Chapters Meeting ETCM 2018. Cuenca, Ecuador | Abbreviated Journal | |
Volume | Issue | Pages | 1-6 | ||
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Abstract | The identification of shrimp organs in biology using histological images is a complex task. Shrimp histological images poses a big challenge due to their texture and similarity among classes. Image classification by using feature engineering and convolutional neural networks (CNN) are suitable methods to assist biologists when performing organ detection. This work evaluates the Bag-of-Visual-Words (BOVW) and Pyramid-Bagof- Words (PBOW) models for image classification leveraging big data techniques; and transfer learning for the same classification task by using a pre-trained CNN. A comparative analysis of these two different techniques is performed, highlighting the characteristics of both approaches on the shrimp organs identification problem. |
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Call Number | gtsi @ user @ | Serial | 87 | ||
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Author | Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla; Riad I. Hammoud | ||||
Title | 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 | |
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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 | Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla | ||||
Title | Vegetation Index Estimation from Monospectral Images | Type | Conference Article | ||
Year | 2018 | Publication | 15th International Conference, Image Analysis and Recognition (ICIAR 2018), Póvoa de Varzim, Portugal. Lecture Notes in Computer Science | Abbreviated Journal | |
Volume | 10882 | Issue | Pages | 353-362 | |
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Abstract | This paper proposes a novel approach to estimate Normalized Difference Vegetation Index (NDVI) from just the red channel of a RGB image. The NDVI index is defined as the ratio of the difference of the red and infrared radiances over their sum. In other words, information from the red channel of a RGB image and the corresponding infrared spectral band are required for its computation. In the current work the NDVI index is estimated just from the red channel by training a Conditional Generative Adversarial Network (CGAN). The architecture proposed for the generative network consists of a single level structure, which combines at the final layer results from convolutional operations together with the given red channel with Gaussian noise to enhance details, resulting in a sharp NDVI image. Then, the discriminative model estimates the probability that the NDVI generated index came from the training dataset, rather than the index automatically generated. Experimental results with a large set of real images are provided showing that a Conditional GAN single level model represents an acceptable approach to estimate NDVI index. |
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Call Number | gtsi @ user @ | Serial | 82 | ||
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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 | 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 | Wilton Agila; Gomer Rubio; L. Miranda; L. Vázquez | ||||
Title | Qualitative Model of Control in the Pressure Stabilization of PEM Fuel Cell | Type | Conference Article | ||
Year | 2018 | Publication | 7th International Conference on Renewable Energy Research and Applications, ICRERA 2018. Paris, Francia. | Abbreviated Journal | |
Volume | Issue | Pages | 1221-1226 | ||
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Abstract | This work describes an approximate reasoning technique to deal with the non-linearity that occurs in the stabilization of the pressure of anodic and cathodic gases of a proton exchange membrane fuel cell (PEM). The implementation of a supervisory element in the stabilization of the pressure of the PEM cell is described. The fuzzy supervisor is a reference control, it varies the value of the reference given to the classic low-level controller, Proportional – Integral – Derivative (PID), according to the speed of change of the measured pressure and the change in the error of the pressure. The objective of the fuzzy supervisor is to achieve a rapid response over time of the variable pressure, avoiding unwanted overruns with respect to the reference value. A comparative analysis is detailed with the classic PID control to evaluate the operation of the “fuzzy supervisor”, with different flow values and different sizes of active area of the PEM cell (electric power generated). |
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Call Number | gtsi @ user @ | Serial | 88 | ||
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Author | Gomer Rubio; Wilton Agila | ||||
Title | Dynamic Modeling of Fuel Cells in a Strategic Context | Type | Conference Article | ||
Year | 2018 | Publication | 7th International Conference on Renewable Energy Research and Applications, ICRERA 2018. Paris, Francia. | Abbreviated Journal | |
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Call Number | gtsi @ user @ | Serial | 86 | ||
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