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Luis Jacome-Galarza, Monica Villavicencio-Cabezas, Miguel Realpe-Robalino, Jose Benavides-Maldonado |
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Software Engineering and Distributed Computing in image processing intelligent systems: a systematic literature review. |
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2021 |
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19th LACCEI International Multi-Conference for Engineering, Education, and Technology |
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processing, software engineering, deep learning, intelligent vision systems, cloud computing. |
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Deep learning is experiencing an upward technology trend that is revolutionizing intelligent systems in several domains, such as image and speech recognition, machine translation, social network filtering, and the like. By reviewing a total of 80 studies reported from 2016 to 2020, the present article evaluates the application of software engineering to the field
of intelligent image processing systems, it also offers insights about aspects related to distributed computing for this type of systems. Results indicate that several topics of software engineering are mostly applied when academics are involved in developing projects associated to this kind of intelligent systems. The findings provide evidences that Apache Spark is the most
utilized distributed computing framework for image processing. In addition, Tensorflow is a popular framework used to build convolutional neural networks, which are the prevailing deep learning algorithms used in intelligent image processing systems.
Also, among big cloud providers, Amazon Web Services is the preferred computing platform across the industry sectors, followed by Google cloud. |
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English |
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cidis @ cidis @ |
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154 |
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Author |
Roberto Jacome Galarza; Miguel-Andrés Realpe-Robalino; Chamba-Eras LuisAntonio; Viñán-Ludeña MarlonSantiago and Sinche-Freire Javier-Francisco |
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Title |
Computer vision for image understanding. A comprehensive review |
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Conference Article |
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2019 |
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International Conference on Advances in Emerging Trends and Technologies (ICAETT 2019); Quito, Ecuador |
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248-259 |
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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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97 |
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Cristhian A. Aguilera; Cristhian Aguilera; Angel D. Sappa |
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Title |
Melamine faced panels defect classification beyond the visible spectrum. |
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2018 |
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In Sensors 2018 |
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Vol. 11 |
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Issue 11 |
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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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gtsi @ user @ |
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89 |
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Juan A. Carvajal; Dennis G. Romero; Angel D. Sappa |
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Fine-tuning deep convolutional networks for lepidopterous genus recognition |
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2017 |
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Lecture Notes in Computer Science |
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Vol. 10125 LNCS |
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pp. 467-475 |
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63 |
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Cristhian A. Aguilera; Angel D. Sappa; Ricardo Toledo |
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Title |
Cross-Spectral Local Descriptors via Quadruplet Network |
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2017 |
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In Sensors Journal |
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Vol. 17 |
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pp. 873 |
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64 |
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Author |
Victor Santos; Angel D. Sappa; Miguel Oliveira |
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Special Issue on Autonomous Driving an Driver Assistance Systems |
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2017 |
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In Robotics and Autonomous Systems Journal |
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Vol. 91 |
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pp. 208-209 |
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65 |
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Author |
Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla |
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Title |
Colorizing Infrared Images through a Triplet Condictional DCGAN Architecture |
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2017 |
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19th International Conference on Image Analysis and Processing. |
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287-297 |
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66 |
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Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla |
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Title |
Learning Image Vegetation Index through a Conditional Generative Adversarial Network |
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2017 |
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2nd IEEE Ecuador Tehcnnical Chapters Meeting (ETCM) |
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70 |
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Lukas Danev; Marten Hamann; Nicolas Fricke; Tobias Hollarek; Dennys Paillacho |
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Development of animated facial expression to express emotions in a robot: RobotIcon. |
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2017 |
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IEEE Ecuador Technical Chapter Meeting (ETCM) |
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2017-January |
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1-6 |
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71 |
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Xavier Soria; Angel D. Sappa; Arash Akbarinia |
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Multispectral Single-Sensor RGB-NIR Imaging: New Challenges an Oppotunities |
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2017 |
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The 7th International Conference on Image Processing Theory, Tools and Application |
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72 |
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