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Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla |
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Title |
Infrared Image Colorization based on a Triplet DCGAN Architecture. |
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Conference Article |
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2017 |
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13th IEEE Workshop on Perception Beyond the Visible Spectrum – In conjunction with CVPR 2017. (This paper has been selected as “Best Paper Award” ) |
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2017-July |
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212-217 |
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cidis @ cidis @ |
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62 |
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Cristhian A. Aguilera; Cristhian Aguilera; Angel D. Sappa |
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Title |
Melamine faced panels defect classification beyond the visible spectrum. |
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Journal Article |
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2018 |
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In Sensors 2018 |
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Vol. 11 |
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Issue 11 |
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In this work, we explore the use of images from different spectral bands to classify defects in melamine faced panels, which could appear through the production process. Through experimental evaluation, we evaluate the use of images from the visible (VS), near-infrared (NIR), and long wavelength infrared (LWIR), to classify the defects using a feature descriptor learning approach together with a support vector machine classifier. Two descriptors were evaluated, Extended Local Binary Patterns (E-LBP) and SURF using a Bag of Words (BoW) representation. The evaluation was carried on with an image set obtained during this work, which contained five different defect categories that currently occurs in the industry. Results show that using images from beyond
the visual spectrum helps to improve classification performance in contrast with a single visible spectrum solution. |
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gtsi @ user @ |
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89 |
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Author |
Milton Mendieta; F. Panchana; B. Andrade; B. Bayot; C. Vaca; Boris X. Vintimilla; Dennis G. Romero |
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Organ identification on shrimp histological images: A comparative study considering CNN and feature engineering. |
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Conference Article |
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2018 |
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IEEE Ecuador Technical Chapters Meeting ETCM 2018. Cuenca, Ecuador |
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1-6 |
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The identification of shrimp organs in biology using
histological images is a complex task. Shrimp histological images
poses a big challenge due to their texture and similarity among
classes. Image classification by using feature engineering and
convolutional neural networks (CNN) are suitable methods to
assist biologists when performing organ detection. This work
evaluates the Bag-of-Visual-Words (BOVW) and Pyramid-Bagof-
Words (PBOW) models for image classification leveraging big
data techniques; and transfer learning for the same classification
task by using a pre-trained CNN. A comparative analysis
of these two different techniques is performed, highlighting
the characteristics of both approaches on the shrimp organs
identification problem. |
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gtsi @ user @ |
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87 |
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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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Conference Article |
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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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