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Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla |
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Title |
Cross-spectral Image Patch Similarity using Convolutional Neural Network |
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2017 |
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2017 IEEE International Workshop of Electronics, Control, Measurement, Signals and their application to Mechatronics (ECMSM) |
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1-5 |
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cidis @ cidis @ |
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57 |
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Author |
Marjorie Chalen; Boris X. Vintimilla |
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Title |
Towards Action Prediction Applying Deep Learning |
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Journal Article |
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2019 |
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Latin American Conference on Computational Intelligence (LA-CCI); Guayaquil, Ecuador; 11-15 Noviembre 2019 |
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pp. 1-3 |
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action prediction, early recognition, early detec- tion, action anticipation, cnn, deep learning, rnn, lstm. |
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Considering the incremental development future action prediction by video analysis task of computer vision where it is done based upon incomplete action executions. Deep learning is playing an important role in this task framework. Thus, this paper describes recently techniques and pertinent datasets utilized in human action prediction task. |
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cidis @ cidis @ |
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129 |
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Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla |
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Title |
Learning Image Vegetation Index through a Conditional Generative Adversarial Network |
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2017 |
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2nd IEEE Ecuador Tehcnnical Chapters Meeting (ETCM) |
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gtsi @ user @ |
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70 |
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Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla; Riad I. Hammoud |
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Title |
Deep Learning based Single Image Dehazing |
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2018 |
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14th IEEE Workshop on Perception Beyond the Visible Spectrum – In conjunction with CVPR 2018. Salt Lake City, Utah. USA |
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This paper proposes a novel approach to remove haze
degradations in RGB images using a stacked conditional
Generative Adversarial Network (GAN). It employs a triplet
of GAN to remove the haze on each color channel independently.
A multiple loss functions scheme, applied over a
conditional probabilistic model, is proposed. The proposed
GAN architecture learns to remove the haze, using as conditioned
entrance, the images with haze from which the clear
images will be obtained. Such formulation ensures a fast
model training convergence and a homogeneous model generalization.
Experiments showed that the proposed method
generates high-quality clear images. |
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gtsi @ user @ |
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83 |
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