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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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Steven Silva, N. V., Dennys Paillacho, Samuel Millan-Norman & Juan David Hernandez. (2023). Online Social Robot Navigation in Indoor, Large and Crowded Environments. In IEEE International Conference on Robotics and Automation (ICRA 2023) (Vol. 2023-May, pp. 9749–9756).
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Cristhian A. Aguilera, Angel D. Sappa, & R. Toledo. (2015). LGHD: A feature descriptor for matching across non-linear intensity variations. In IEEE International Conference on, Quebec City, QC, 2015 (pp. 178–181). Quebec City, QC, Canada: IEEE.
Abstract: This paper presents a new feature descriptor suitable to the task of matching features points between images with nonlinear intensity variations. This includes image pairs with significant illuminations changes, multi-modal image pairs and multi-spectral image pairs. The proposed method describes the neighbourhood of feature points combining frequency and spatial information using multi-scale and multi-oriented Log- Gabor filters. Experimental results show the validity of the proposed approach and also the improvements with respect to the state of the art.
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Dennys Paillacho, Cecilio Angulo, & Marta Díaz. (2015). An Exploratory Study of Group-Robot Social Interactions in a Cultural Center. In IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2015, International Conference on, Hamburg, Germany, 2015.
Abstract: This article describes an exploratory study of social human-robot interaction with the experimental robotic platform MASHI. The experiences were carried out in La B`obila Cultural Center in Barcelona, Spain to study the visitor preferences, characterize the groups and their spatial relationships in this open and unstructured environment. Results showed that visitors prefers to play and dialogue with the robot. Children have the highest interest in interacting with the robot, more than young and adult visitors. Most of the groups consisted of more than 3 visitors, however the size of the groups during interactions was continuously changed. In static situations, the observed spatial relationships denotes a social cohesion in the human-robot interactions.
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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.
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Suárez P. (2021). Processing and Representation of Multispectral Images Using Deep Learning Techniques. In Electronic Letters on Computer Vision and Image Analysis, Vol. 19(Issue 2), pp. 5–8.
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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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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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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.
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Patricia Súarez, H. V., Dario Carpio & Angel Sappa. (2023). Corn Kernel Classification From Few Training Samples. In journal Artificial Intelligence in Agriculture, Vol. 9, pp. 89–99.
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