|
|
Charco, J. L., Sappa, A.D., Vintimilla, B.X., Velesaca, H.O. (2021). Camera pose estimation in multi-view environments:from virtual scenarios to the real world. In Image and Vision Computing Journal. (Article number 104182), Vol. 110.
Abstract: This paper presents a domain adaptation strategy to efficiently train network architectures for estimating the relative camera pose in multi-view scenarios. The network architectures are fed by a pair of simultaneously acquired
images, hence in order to improve the accuracy of the solutions, and due to the lack of large datasets with pairs of
overlapped images, a domain adaptation strategy is proposed. The domain adaptation strategy consists on transferring the knowledge learned from synthetic images to real-world scenarios. For this, the networks are firstly
trained using pairs of synthetic images, which are captured at the same time by a pair of cameras in a virtual environment; and then, the learned weights of the networks are transferred to the real-world case, where the networks are retrained with a few real images. Different virtual 3D scenarios are generated to evaluate the
relationship between the accuracy on the result and the similarity between virtual and real scenarios—similarity
on both geometry of the objects contained in the scene as well as relative pose between camera and objects in the
scene. Experimental results and comparisons are provided showing that the accuracy of all the evaluated networks for estimating the camera pose improves when the proposed domain adaptation strategy is used,
highlighting the importance on the similarity between virtual-real scenarios.
|
|
|
|
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).
|
|
|
|
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).
|
|
|
|
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).
|
|
|
|
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).
|
|
|
|
Rubio, G. A., Agila, W.E. (2021). A fuzzy model to manage water in polymer electrolyte membrane fuel cells. In Processes Journal. (Article number 904), Vol. 9(Issue 6).
Abstract: In this paper, a fuzzy model is presented to determine in real-time the degree of dehydration or flooding of a proton exchange membrane of a fuel cell, to optimize its electrical response and consequently, its autonomous operation. By applying load, current and flux variations in the dry, normal, and flooded states of the membrane, it was determined that the temporal evolution of the fuel cell voltage is characterized by changes in slope and by its voltage oscillations. The results were validated using electrochemical impedance spectroscopy and show slope changes from 0.435 to 0.52 and oscillations from 3.6 mV to 5.2 mV in the dry state, and slope changes from 0.2 to 0.3 and oscillations from 1 mV to 2 mV in the flooded state. The use of fuzzy logic is a novelty and constitutes a step towards the progressive automation of the supervision, perception, and intelligent control of fuel cells, allowing them to reduce their risks and increase their economic benefits.
|
|
|
|
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.
|
|
|
|
Michael Teutsch, A. S. & R. H. (2021). Computer Vision in the Infrared Spectrum: Challenges and ApproachesComputer Vision in the Infrared Spectrum: Challenges and Approaches. Synthesis Lectures on Computer Vision, Vol. 10 No. 2, pp. 138.
|
|
|
|
Santos, V., Sappa, A.D., Oliveira, M. & de la Escalera, A. (2021). Editorial: Special Issue on Autonomous Driving and Driver Assistance Systems – Some Main Trends. In Journal: Robotics and Autonomous Systems. (Article number 103832), Vol. 144.
|
|
|
|
Velesaca, H. O., Suárez, P. L., Mira, R., & Sappa, A.D. (2021). Computer Vision based Food Grain Classification: a Comprehensive Survey. In Computers and Electronics in Agriculture Journal. (Article number 106287), Vol. 187.
|
|
