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Author Roberto Jacome Galarza; Miguel-Andrés Realpe-Robalino; Chamba-Eras LuisAntonio; Viñán-Ludeña MarlonSantiago and Sinche-Freire Javier-Francisco pdf  openurl
  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 pdf  openurl
  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 pdf  openurl
  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 pdf  openurl
  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 pdf  openurl
  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 pdf  openurl
  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 pdf  openurl
  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 pdf  openurl
  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 pdf  openurl
  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 pdf  openurl
  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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  Notes Approved no  
  Call Number gtsi @ user @ Serial 87  
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