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| Author | Cristhian A. Aguilera; Xaver Soria; Angel D. Sappa; Ricardo Toledo | ||||
| Title | RGBN Multispectral Images: a Novel Color Restoration Approach | Type | Conference Article | ||
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2017 | Publication | 15th International Conference on Practical Applications of Agents and Multi-Agent Systems | Abbreviated Journal | |
| Volume | 619 | Issue | Pages | 155-163 | |
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| Notes | Approved | no | |||
| Call Number | cidis @ cidis @ | Serial | 59 | ||
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| Author | Angel J. Valencia; Roger M. Idrovo; Angel D. Sappa; Douglas Plaza G.; Daniel Ochoa | ||||
| Title | A 3D Vision Based Approach for Optimal Grasp of Vacuum Grippers | Type | Conference Article | ||
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2017 | Publication | 2017 IEEE International Workshop of Electronics, Control, Measurement, Signals and their application to Mechatronics (ECMSM) | Abbreviated Journal | |
| Volume | Issue | Pages | 1-6 | ||
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| Call Number | cidis @ cidis @ | Serial | 60 | ||
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| Author | Dennis G. Romero; Roberto Yoncon; Angel Guale; Bonny Bayot; Fanny Panchana | ||||
| Title | Evaluación de técnicas de clasificación orientadas a la identificación automática de órganos del camarón a partir de imágenes histológicas | Type | Conference Article | ||
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2017 | Publication | 15th LACCEI International Multi-Conference for Engineering, Education, and Technology | Abbreviated Journal | |
| Volume | 2017-July | Issue | Pages | 1-6 | |
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| Call Number | cidis @ cidis @ | Serial | 61 | ||
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| Author | Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla | ||||
| Title | Infrared Image Colorization based on a Triplet DCGAN Architecture. | Type | Conference Article | ||
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2017 | Publication | 13th IEEE Workshop on Perception Beyond the Visible Spectrum – In conjunction with CVPR 2017. (This paper has been selected as “Best Paper Award” ) | Abbreviated Journal | |
| Volume | 2017-July | Issue | Pages | 212-217 | |
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| Call Number | cidis @ cidis @ | Serial | 62 | ||
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| Author | Cristhian A. Aguilera; Cristhian Aguilera; Angel D. Sappa | ||||
| Title | Melamine faced panels defect classification beyond the visible spectrum. | Type | Journal Article | ||
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2018 | Publication | In Sensors 2018 | Abbreviated Journal | |
| Volume | Vol. 11 | Issue | Issue 11 | Pages | |
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| Abstract | In this work, we explore the use of images from different spectral bands to classify defects in melamine faced panels, which could appear through the production process. Through experimental evaluation, we evaluate the use of images from the visible (VS), near-infrared (NIR), and long wavelength infrared (LWIR), to classify the defects using a feature descriptor learning approach together with a support vector machine classifier. Two descriptors were evaluated, Extended Local Binary Patterns (E-LBP) and SURF using a Bag of Words (BoW) representation. The evaluation was carried on with an image set obtained during this work, which contained five different defect categories that currently occurs in the industry. Results show that using images from beyond the visual spectrum helps to improve classification performance in contrast with a single visible spectrum solution. |
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| Call Number | gtsi @ user @ | Serial | 89 | ||
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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 | ||
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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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| Notes | Approved | no | |||
| Call Number | gtsi @ user @ | Serial | 87 | ||
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| Author | Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla | ||||
| Title | Vegetation Index Estimation from Monospectral Images | Type | Conference Article | ||
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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 | ||
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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 | Wilton Agila; Gomer Rubio; L. Miranda; L. Vázquez | ||||
| Title | Qualitative Model of Control in the Pressure Stabilization of PEM Fuel Cell | Type | Conference Article | ||
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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 | Xavier Soria; Angel D. Sappa | ||||
| Title | Improving Edge Detection in RGB Images by Adding NIR Channel. | Type | Conference Article | ||
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2018 | Publication | 14th IEEE International Conference on Signal Image Technology & Internet based Systems (SITIS 2018) | Abbreviated Journal | |
| Volume | Issue | Pages | 266-273 | ||
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| Call Number | gtsi @ user @ | Serial | 95 | ||
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