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Roberto Jacome Galarza; Miguel-Andrés Realpe-Robalino; Chamba-Eras LuisAntonio; Viñán-Ludeña MarlonSantiago and Sinche-Freire Javier-Francisco |
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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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gtsi @ user @ |
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97 |
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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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cidis @ cidis @ |
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154 |
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Miguel Realpe; Boris X. Vintimilla; Ljubo Vlacic |
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A Fault Tolerant Perception system for autonomous vehicles |
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2016 |
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35th Chinese Control Conference (CCC2016), International Conference on, Chengdu |
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1-6 |
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Fault Tolerant Perception, Sensor Data Fusion, Fault Tolerance, Autonomous Vehicles, Federated Architecture |
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Driverless vehicles are currently being tested on public roads in order to examine their ability to perform in a safe and reliable way in real world situations. However, the long-term reliable operation of a vehicle’s diverse sensors and the effects of potential sensor faults in the vehicle system have not been tested yet. This paper is proposing a sensor fusion architecture that minimizes the influence of a sensor fault. Experimental results are presented simulating faults by introducing displacements in the sensor information from the KITTI dataset. |
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cidis @ cidis @ |
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52 |
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Rafael E. Rivadeneira; Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla. |
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Thermal Image SuperResolution through Deep Convolutional Neural Network. |
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2019 |
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16th International Conference on Image Analysis and Recognition (ICIAR 2019); Waterloo, Canadá |
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417-426 |
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Due to the lack of thermal image datasets, a new dataset has been acquired for proposed a superesolution approach using a Deep Convolution Neural Network schema. In order to achieve this image enhancement process a new thermal images dataset is used. Di?erent experiments have been carried out, ?rstly, the proposed architecture has been trained using only images of the visible spectrum, and later it has been trained with images of the thermal spectrum, the results showed that with the network trained with thermal images, better results are obtained in the process of enhancing the images, maintaining the image details and perspective. The thermal dataset is available at http://www.cidis.espol.edu.ec/es/dataset |
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gtsi @ user @ |
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103 |
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