2021 |
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Luis C. Herrera, L. del R. L., Nayeth I. Solorzano, Jonathan S. Paillacho & Dennys Paillacho. (2021). Metrics Design of Usability and Behavior Analysis of a Human-Robot-Game Platform. In The 2nd International Conference on Applied Technologies (ICAT 2020), diciembre 2-4. Communication in Computer and Information Science (Vol. 1388, pp. 164–178).
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Luis Jacome-Galarza, M. V. - C., Miguel Realpe-Robalino, Jose Benavides-Maldonado. (2021). Software Engineering and Distributed Computing in image processing intelligent systems: a systematic literature review. In 19th LACCEI International Multi-Conference for Engineering, Education, and Technology.
Abstract: 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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Mehri, A., Ardakani, P.B., Sappa, A.D. (2021). LiNet: A Lightweight Network for Image Super Resolution. In 25th International Conference on Pattern Recognition (ICPR), enero 10-15, 2021 (pp. 7196–7202).
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Mehri, A., Ardakani, P.B., Sappa, A.D. (2021). MPRNet: Multi-Path Residual Network for Lightweight Image Super Resolution. In In IEEE Winter Conference on Applications of Computer Vision WACV 2021, enero 5-9, 2021 (pp. 2703–2712).
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Miguel A. Murillo, J. E. A., & Miguel Realpe. (2021). Beyond visual and radio line of sight UAVs monitoring system through open software in a simulated environment. In The 2nd International Conference on Applied Technologies (ICAT 2020), diciembre 2-4. Communications in Computer and Information Science (Vol. 1388, pp. 629–642).
Abstract: The problem of loss of line of sight when operating drones has be-come a reality with adverse effects for professional and amateur drone opera-tors, since it brings technical problems such as loss of data collected by the de-vice in one or more instants of time during the flight and even misunderstand-ings of legal nature when the drone flies over prohibited or private places. This paper describes the implementation of a drone monitoring system using the In-ternet as a long-range communication network in order to avoid the problem of loss of communication between the ground station and the device. For this, a simulated environment is used through an appropriate open software tool. The operation of the system is based on a client that makes requests to a server, the latter in turn communicates with several servers, each of which has a drone connected to it. In the proposed system when a drone is ready to start a flight, its server informs the main server of the system, which in turn gives feedback to the client informing it that the device is ready to carry out the flight; this way customers can send a mission to the device and keep track of its progress in real time on the screen of their web application.
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Patricia L. Suárez, A. D. S., Boris X. Vintimilla. (2021). Cycle generative adversarial network: towards a low-cost vegetation index estimation. In IEEE International Conference on Image Processing (ICIP 2021) (Vol. 2021-September, pp. 2783–2787).
Abstract: This paper presents a novel unsupervised approach to estimate the Normalized Difference Vegetation Index (NDVI).The NDVI is obtained as the ratio between information from the visible and near infrared spectral bands; in the current work, the NDVI is estimated just from an image of the visible spectrum through a Cyclic Generative Adversarial Network (CyclicGAN). This unsupervised architecture learns to estimate the NDVI index by means of an image translation between the red channel of a given RGB image and the NDVI unpaired index’s image. The translation is obtained by means of a ResNET architecture and a multiple loss function. Experimental results obtained with this unsupervised scheme show the validity of the implemented model. Additionally, comparisons with the state of the art approaches are provided showing improvements with the proposed approach.
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Patricia L. Suárez, D. C., and Angel Sappa. (2021). Non-Homogeneous Haze Removal through a Multiple Attention Module Architecture. In 16 International Symposium on Visual Computing. Octubre 4-6, 2021. Lecture Notes in Computer Science (Vol. 13018, pp. 178–190).
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Rivadeneira R.E., S. A. D., Vintimilla B.X., Nathan S., Kansal P., Mehri A et al. (2021). Thermal Image Super-Resolution Challenge – PBVS 2021. In In IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2021., junio 19 – 25, 2021 (pp. 4354–4362).
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Steven Silva, D. P., David Soque, María Guerra & Jonathan Paillacho. (2021). Autonomous Intelligent Navigation For Mobile Robots In Closed Environments. In The 2nd International Conference on Applied Technologies (ICAT 2020), diciembre 2-4. Communications in Computer and Information Science (Vol. 1388, pp. 391–402).
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2020 |
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Henry O. Velesaca, Raul A. Mira, Patricia L. Suarez, Christian X. Larrea, & Angel D. Sappa. (2020). Deep Learning based Corn Kernel Classification. In The 1st International Workshop and Prize Challenge on Agriculture-Vision: Challenges & Opportunities for Computer Vision in Agriculture on the Conference Computer on Vision and Pattern Recongnition (CVPR 2020) (Vol. 2020-June, pp. 294–302).
Abstract: This paper presents a full pipeline to classify sample sets of corn kernels. The proposed approach follows a segmentation-classification scheme. The image segmentation is performed through a well known deep learning based
approach, the Mask R-CNN architecture, while the classification is performed by means of a novel-lightweight network specially designed for this task—good corn kernel, defective corn kernel and impurity categories are considered.
As a second contribution, a carefully annotated multitouching corn kernel dataset has been generated. This dataset has been used for training the segmentation and
the classification modules. Quantitative evaluations have been performed and comparisons with other approaches provided showing improvements with the proposed pipeline.
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