Jorge García-González
University of Málaga
20 Papers
16 Citations
Jorge García-González is an academic researcher from University of Málaga. The author has contributed to research in topics: Computer science & Object detection. The author has an hindex of 5, co-authored 10 publications.
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Papers
Bayesian and Neural Inference on LSTM-Based Object Recognition From Tactile and Kinesthetic Information
Francisco Pastor,Jorge García-González,Juan M. Gandarias,Daniel Medina,Pau Closas,Alfonso García-Cerezo,Jesús M. Gómez-de-Gabriel +6 more
- 01 Jan 2021
TL;DR: An experiment with 36-classes is carried out to evaluate and compare the performance of the fused, tactile, and kinesthetic perception systems and the results show that the Bayesian-based classifiers improves capabilities for object recognition and outperforms the Neural-based approach.
Foreground detection by probabilistic modeling of the features discovered by stacked denoising autoencoders in noisy video sequences
Jorge García-González,Juan Miguel Ortiz-de-Lazcano-Lobato,Rafael Marcos Luque-Baena,Miguel A. Molina-Cabello,Ezequiel López-Rubio +4 more
TL;DR: A robust foreground detection system is presented, which is resilient to noise in video sequences and the proposal appears among the first three positions in most case and its the one that best performs on average.
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Road pollution estimation from vehicle tracking in surveillance videos by deep convolutional neural networks
Jorge García-González,Miguel A. Molina-Cabello,Rafael Marcos Luque-Baena,Juan Miguel Ortiz-de-Lazcano-Lobato,Ezequiel López-Rubio +4 more
TL;DR: In this paper, a method which detects the pollution levels of transport vehicles from the images of IP cameras by means of computer vision techniques and neural networks is proposed, and the trajectory of each vehicle is computed by applying convolutional neural networks for object detection and tracking algorithms.
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Background modeling by shifted tilings of stacked denoising autoencoders
Jorge García-González,Juan Miguel Ortiz-de-Lazcano-Lobato,Rafael Marcos Luque-Baena,Ezequiel López-Rubio +3 more
- 03 Jun 2019
TL;DR: A methodology for background modeling and foreground detection, whose main characteristic is its robustness against stationary noise, is presented in the paper, based on a stacked denoising autoencoder which extracts a set of significant features for each patch of several shifted tilings of the video frame.
The effect of downsampling–upsampling strategy on foreground detection algorithms
Miguel A. Molina-Cabello,Jorge García-González,Rafael Marcos Luque-Baena,Ezequiel López-Rubio +3 more
TL;DR: This paper proposes a methodology to reduce the computational requirements of the algorithms, consisting of a reduction of the input frame and an interpolation of the segmentation mask of each method to recover the original frame size.
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