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Automatic Segmentation of Mauritia flexuosa in Unmanned Aerial Vehicle (UAV) Imagery Using Deep Learning
- Giorgio Morales, G. Kemper, Grace Sevillano, D. Arteaga, Ivan Ortega, J. Telles
- 26 November 2018
It is shown that the proposed segmentation and measurement method is able not only to detect full-grown isolated Mauritia flexuosa palms, but also young palms or palms partially covered by other types of vegetation.
End-to-end Cloud Segmentation in High-Resolution Multispectral Satellite Imagery Using Deep Learning
- Giorgio Morales, Alejandro Ramírez, J. Telles
- Computer Science, EngineeringIEEE XXVI International Conference on Electronics…
- 29 April 2019
The dataset is presented, consisting of 22,400 images of 512 × 512 pixels and their respective hand-drawn cloud masks, as well as the proposal of an end-to-end segmentation method for clouds using a Convolutional Neural Network based on the Deeplab v3+ architecture.
Cloud Detection in High-Resolution Multispectral Satellite Imagery Using Deep Learning
It is proved that an agile Convolutional Neural Network is able to distinguish between non-clouds and different types of clouds, including thin and very small ones, and achieve a classification accuracy of 99.94%.
A novel fuzzy logic-based metric for audio quality assessment: Objective audio quality assessment
- Luis F. Abanto-Leon, Guillermo Kemper Vasquez, J. Telles
- Computer Science, EngineeringCONATEL
- 17 May 2011
A new metric of low computational complexity called FQI (Fuzzy Quality Index) which is based on Fuzzy Logic reasoning and has been incorporated into the existing PEAZ model to improve its overall performance and results show that the modified version slightly outperforms PEAQ.
An Algorithm for Plant Disease Visual Symptom Detection in Digital Images Based on Superpixels
- Itamar Salazar-Reque, Samuel G. Huamán, G. Kemper, J. Telles, Daniel Díaz
- Computer ScienceInternational Journal on Advanced Science…
- 23 February 2019
This work proposes a new method for the automatic segmentation of diseased leaf areas using the Simple Linear Iterative Clustering (SLIC) algorithm to group similar-color pixels together into regions called superpixels, which is higher than that found by the other approaches reported in the literature.
Shadow Detection in High-Resolution Multispectral Satellite Imagery Using Generative Adversarial Networks
- Giorgio Morales, D. Arteaga, Samuel G. Huamán, J. Telles, Walther Palomino
- Computer ScienceIEEE XXV International Conference on Electronics…
- 1 August 2018
This work trained a generator network that produces shadow masks with condition on a satellite image patch and tries to fool a discriminator, which is trained to discern if a given mask comes from the ground truth or from the generator model.
Detecting Violent Robberies in CCTV Videos Using Deep Learning
The contribution of this paper is the presentation of a video dataset called UNI-Crime, and the proposal of a violent robbery detection method in CCTV videos using a deep-learning sequence model.
PETEFA: Geographic Information System for Precision Agriculture
- Walther Palomino, Giorgio Morales, Samuel G. Huamán, J. Telles
- Environmental ScienceIEEE XXV International Conference on Electronics…
- 1 August 2018
Providing timely information through remote sensing tools to the farmers and to the National Institute of Agrarian Innovation (INIA) is important to manage the production of yellow corn crops (Zea…
Cloud Detection for PERUSAT-1 Imagery Using Spectral and Texture Descriptors, ANN, and Panchromatic Fusion
- Giorgio Morales, Samuel G. Huamán, J. Telles
- Computer ScienceProceedings of the 3rd Brazilian Technology…
- 5 December 2017
A method to detect clouds in high-resolution images of 2.8 m per pixel approximately is presented, performed over those pixels that exceed a defined threshold of blue normalized difference vegetation index to reduce the execution time.
Shadow Removal in High-Resolution Satellite Images Using Conditional Generative Adversarial Networks
A shadow removal method in high-resolution satellite images using conditional Generative Adversarial Networks (cGANs) that is tested in the proposed dataset achieving an error ratio comparable with the state of the art.