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| Author | Roberto Jacome Galarza; Miguel-Andrés Realpe-Robalino; Chamba-Eras LuisAntonio; Viñán-Ludeña MarlonSantiago and Sinche-Freire Javier-Francisco | ||||
| Title | Computer vision for image understanding. A comprehensive review | Type | Conference Article | ||
| Year | 2019 | Publication | International Conference on Advances in Emerging Trends and Technologies (ICAETT 2019); Quito, Ecuador | Abbreviated Journal | |
| Volume | Issue | Pages | 248-259 | ||
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| Abstract | Computer Vision has its own Turing test: Can a machine describe the contents of an image or a video in the way a human being would do? In this paper, the progress of Deep Learning for image recognition is analyzed in order to know the answer to this question. In recent years, Deep Learning has increased considerably the precision rate of many tasks related to computer vision. Many datasets of labeled images are now available online, which leads to pre-trained models for many computer vision applications. In this work, we gather information of the latest techniques to perform image understanding and description. As a conclusion we obtained that the combination of Natural Language Processing (using Recurrent Neural Networks and Long Short-Term Memory) plus Image Understanding (using Convolutional Neural Networks) could bring new types of powerful and useful applications in which the computer will be able to answer questions about the content of images and videos. In order to build datasets of labeled images, we need a lot of work and most of the datasets are built using crowd work. These new applications have the potential to increase the human machine interaction to new levels of usability and user’s satisfaction. | ||||
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| Call Number | gtsi @ user @ | Serial | 97 | ||
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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 | ||
| Year | 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 | Juan A. Carvajal; Dennis G. Romero; Angel D. Sappa | ||||
| Title | Fine-tuning deep convolutional networks for lepidopterous genus recognition | Type | Journal Article | ||
| Year | 2017 | Publication | Lecture Notes in Computer Science | Abbreviated Journal | |
| Volume | Vol. 10125 LNCS | Issue | Pages | pp. 467-475 | |
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| Call Number | gtsi @ user @ | Serial | 63 | ||
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| Author | Cristhian A. Aguilera; Angel D. Sappa; Ricardo Toledo | ||||
| Title | Cross-Spectral Local Descriptors via Quadruplet Network | Type | Journal Article | ||
| Year | 2017 | Publication | In Sensors Journal | Abbreviated Journal | |
| Volume | Vol. 17 | Issue | Pages | pp. 873 | |
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| Call Number | gtsi @ user @ | Serial | 64 | ||
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| Author | Victor Santos; Angel D. Sappa; Miguel Oliveira | ||||
| Title | Special Issue on Autonomous Driving an Driver Assistance Systems | Type | Journal Article | ||
| Year | 2017 | Publication | In Robotics and Autonomous Systems Journal | Abbreviated Journal | |
| Volume | Vol. 91 | Issue | Pages | pp. 208-209 | |
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| Call Number | gtsi @ user @ | Serial | 65 | ||
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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 | 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 | ||||
| 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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