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Author |
Patricia L. Suarez, Dario Carpio, Angel D. Sappa and Henry O. Velesaca |
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Title |
Transformer based Image Dehazing. |
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2022 |
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16TH International Conference On Signal Image Technology & Internet Based Systems SITIS 2022. |
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148-154 |
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cidis @ cidis @ |
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195 |
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Author |
Patricia L. Suarez, Dario Carpio, Angel Sappa |
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Title |
Boosting Guided Super-Resolution Performance with Synthesized Images |
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Conference Article |
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2023 |
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17th International Conference On Signal Image Technology & Internet Based Systems, Bangkok, 8-10 November 2023 |
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189 - 195 |
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cidis @ cidis @ |
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225 |
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Author |
Patricia L. Suarez, Dario Carpio, Angel Sappa |
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Title |
Depth Map Estimation from a Single 2D Image |
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Conference Article |
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Year |
2023 |
Publication |
17th International Conference On Signal Image Technology & Internet Based Systems, Bangkok, 8-10 November 2023 |
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347-353 |
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cidis @ cidis @ |
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226 |
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Author |
Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla |
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Title |
Colorizing Infrared Images through a Triplet Condictional DCGAN Architecture |
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Conference Article |
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2017 |
Publication |
19th International Conference on Image Analysis and Processing. |
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287-297 |
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no |
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gtsi @ user @ |
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66 |
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Author |
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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Conference Article |
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Year |
2017 |
Publication |
2nd IEEE Ecuador Tehcnnical Chapters Meeting (ETCM) |
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gtsi @ user @ |
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70 |
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Author |
Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla |
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Title |
Vegetation Index Estimation from Monospectral Images |
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Conference Article |
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Year |
2018 |
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15th International Conference, Image Analysis and Recognition (ICIAR 2018), Póvoa de Varzim, Portugal. Lecture Notes in Computer Science |
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10882 |
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353-362 |
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This paper proposes a novel approach to estimate Normalized
Difference Vegetation Index (NDVI) from just the red channel of
a RGB image. The NDVI index is defined as the ratio of the difference
of the red and infrared radiances over their sum. In other words, information
from the red channel of a RGB image and the corresponding
infrared spectral band are required for its computation. In the current
work the NDVI index is estimated just from the red channel by training a
Conditional Generative Adversarial Network (CGAN). The architecture
proposed for the generative network consists of a single level structure,
which combines at the final layer results from convolutional operations
together with the given red channel with Gaussian noise to enhance
details, resulting in a sharp NDVI image. Then, the discriminative model
estimates the probability that the NDVI generated index came from the
training dataset, rather than the index automatically generated. Experimental
results with a large set of real images are provided showing that
a Conditional GAN single level model represents an acceptable approach
to estimate NDVI index. |
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gtsi @ user @ |
Serial |
82 |
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Author |
Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla |
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Title |
Adaptive Harris Corners Detector Evaluated with Cross-Spectral Images |
Type |
Conference Article |
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Year |
2018 |
Publication |
International Conference on Information Technology & Systems (ICITS 2018). ICITS 2018. Advances in Intelligent Systems and Computing |
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721 |
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This paper proposes a novel approach to use cross-spectral
images to achieve a better performance with the proposed Adaptive Harris
corner detector comparing its obtained results with those achieved
with images of the visible spectra. The images of urban, field, old-building
and country category were used for the experiments, given the variety of
the textures present in these images, with which the complexity of the
proposal is much more challenging for its verification. It is a new scope,
which means improving the detection of characteristic points using crossspectral
images (NIR, G, B) and applying pruning techniques, the combination
of channels for this fusion is the one that generates the largest
variance based on the intensity of the merged pixels, therefore, it is that
which maximizes the entropy in the resulting Cross-spectral images.
Harris is one of the most widely used corner detection algorithm, so
any improvement in its efficiency is an important contribution in the
field of computer vision. The experiments conclude that the inclusion of
a (NIR) channel in the image as a result of the combination of the spectra,
greatly improves the corner detection due to better entropy of the
resulting image after the fusion, Therefore the fusion process applied to
the images improves the results obtained in subsequent processes such as
identification of objects or patterns, classification and/or segmentation. |
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Call Number |
gtsi @ user @ |
Serial |
84 |
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Author |
Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla |
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Title |
Cross-spectral image dehaze through a dense stacked conditional GAN based approach. |
Type |
Conference Article |
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Year |
2018 |
Publication |
14th IEEE International Conference on Signal Image Technology & Internet based Systems (SITIS 2018) |
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358-364 |
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This paper proposes a novel approach to remove haze from RGB images using a near infrared images based on a dense stacked conditional Generative Adversarial Network (CGAN). The architecture of the deep network implemented receives, besides the images with haze, its corresponding image in the near infrared spectrum, which serve to accelerate the learning process of the details of the characteristics of the images. The model uses a triplet layer that allows the independence learning of each channel of the visible spectrum image to remove the haze on each color channel separately. A multiple loss function scheme is proposed, which ensures balanced learning between the colors and the structure of the images. Experimental results have shown that the proposed method effectively removes the haze from the images. Additionally, the proposed approach is compared with a state of the art approach showing better results. |
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Call Number |
gtsi @ user @ |
Serial |
92 |
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Author |
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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Conference Article |
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2017 |
Publication |
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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Call Number |
cidis @ cidis @ |
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57 |
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Author |
Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla |
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Title |
Learning to Colorize Infrared Images |
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Conference Article |
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2017 |
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15th International Conference on Practical Applications of Agents and Multi-Agent Systems |
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cidis @ cidis @ |
Serial |
58 |
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