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Rafael E. Rivadeneira, Angel D. Sappa, & Boris X. Vintimilla. (2020). Thermal Image Super-Resolution: a Novel Architecture and Dataset. In The 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020); Valletta, Malta; 27-29 Febrero 2020 (Vol. 4, pp. 111–119).
Abstract: This paper proposes a novel CycleGAN architecture for thermal image super-resolution, together with a large
dataset consisting of thermal images at different resolutions. The dataset has been acquired using three thermal
cameras at different resolutions, which acquire images from the same scenario at the same time. The thermal
cameras are mounted in rig trying to minimize the baseline distance to make easier the registration problem.
The proposed architecture is based on ResNet6 as a Generator and PatchGAN as Discriminator. The novelty
on the proposed unsupervised super-resolution training (CycleGAN) is possible due to the existence of aforementioned thermal images—images of the same scenario with different resolutions. The proposed approach
is evaluated in the dataset and compared with classical bicubic interpolation. The dataset and the network are
available.
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Jorge L. Charco, Angel D. Sappa, Boris X. Vintimilla, & Henry O. Velesaca. (2020). Transfer Learning from Synthetic Data in the Camera Pose Estimation Problem. In The 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020); Valletta, Malta; 27-29 Febrero 2020 (Vol. 4, pp. 498–505).
Abstract: This paper presents a novel Siamese network architecture, as a variant of Resnet-50, to estimate the relative camera pose on multi-view environments. In order to improve the performance of the proposed model
a transfer learning strategy, based on synthetic images obtained from a virtual-world, is considered. The
transfer learning consist of first training the network using pairs of images from the virtual-world scenario
considering different conditions (i.e., weather, illumination, objects, buildings, etc.); then, the learned weight
of the network are transferred to the real case, where images from real-world scenarios are considered. Experimental results and comparisons with the state of the art show both, improvements on the relative pose
estimation accuracy using the proposed model, as well as further improvements when the transfer learning
strategy (synthetic-world data – transfer learning – real-world data) is considered to tackle the limitation on
the training due to the reduced number of pairs of real-images on most of the public data sets.
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Patricia L. Suarez, Angel D. Sappa, & Boris X. Vintimilla. (2019). Image patch similarity through a meta-learning metric based approach. In 15th International Conference on Signal Image Technology & Internet based Systems (SITIS 2019); Sorrento, Italia (pp. 511–517).
Abstract: Comparing images regions are one of the core methods used on computer vision for tasks like image classification, scene understanding, object detection and recognition. Hence, this paper proposes a novel approach to determine similarity of image regions (patches), in order to obtain the best representation of image patches. This problem has been studied by many researchers presenting different approaches, however, the ability to find the better criteria to measure the similarity on image regions are still a challenge. The present work tackles this problem using a few-shot metric based meta-learning framework able to compare image regions and determining a similarity measure to decide if there is similarity between the compared patches. Our model is training end-to-end from scratch. Experimental results
have shown that the proposed approach effectively estimates the similarity of the patches and, comparing it with the state of the art approaches, shows better results.
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Patricia L. Suarez, Angel D. Sappa, Boris X. Vintimilla, & Riad I. Hammoud. (2019). Image Vegetation Index through a Cycle Generative Adversarial Network. In Conference on Computer Vision and Pattern Recognition Workshops (CVPR 2019); Long Beach, California, United States (pp. 1014–1021).
Abstract: This paper proposes a novel approach to estimate the
Normalized Difference Vegetation Index (NDVI) just from
an RGB image. The NDVI values are obtained by using
images from the visible spectral band together with a synthetic near infrared image obtained by a cycled GAN. The
cycled GAN network is able to obtain a NIR image from
a given gray scale image. It is trained by using unpaired
set of gray scale and NIR images by using a U-net architecture and a multiple loss function (gray scale images are
obtained from the provided RGB images). Then, the NIR
image estimated with the proposed cycle generative adversarial network is used to compute the NDVI index. Experimental results are provided showing the validity of the proposed approach. Additionally, comparisons with previous
approaches are also provided.
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Rafael E. Rivadeneira, Patricia L. Suarez, Angel D. Sappa, & Boris X. Vintimilla. (2019). Thermal Image SuperResolution through Deep Convolutional Neural Network. In 16th International Conference on Image Analysis and Recognition (ICIAR 2019); Waterloo, Canadá (pp. 417–426).
Abstract: Due to the lack of thermal image datasets, a new dataset has been acquired for proposed a superesolution approach using a Deep Convolution Neural Network schema. In order to achieve this image enhancement process a new thermal images dataset is used. Di?erent experiments have been carried out, ?rstly, the proposed architecture has been trained using only images of the visible spectrum, and later it has been trained with images of the thermal spectrum, the results showed that with the network trained with thermal images, better results are obtained in the process of enhancing the images, maintaining the image details and perspective. The thermal dataset is available at http://www.cidis.espol.edu.ec/es/dataset
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Jorge L. Charco, Boris X. Vintimilla, & Angel D. Sappa. (2018). Deep learning based camera pose estimation in multi-view environment. In 14th IEEE International Conference on Signal Image Technology & Internet based Systems (SITIS 2018) (pp. 224–228).
Abstract: This paper proposes to use a deep learning network architecture for relative camera pose estimation on a multi-view environment. The proposed network is a variant architecture of AlexNet to use as regressor for prediction the relative translation and rotation as output. The proposed approach is trained from scratch on a large data set that takes as input a pair of images from the same scene. This new architecture is compared with a previous approach using standard metrics, obtaining better results on the relative camera pose.
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Patricia L. Suarez, Angel D. Sappa, & Boris X. Vintimilla. (2018). Cross-spectral image dehaze through a dense stacked conditional GAN based approach. In 14th IEEE International Conference on Signal Image Technology & Internet based Systems (SITIS 2018) (pp. 358–364).
Abstract: 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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Milton Mendieta, F. Panchana, B. Andrade, B. Bayot, C. Vaca, Boris X. Vintimilla, et al. (2018). Organ identification on shrimp histological images: A comparative study considering CNN and feature engineering. In IEEE Ecuador Technical Chapters Meeting ETCM 2018. Cuenca, Ecuador (pp. 1–6).
Abstract: 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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Patricia L. Suarez, Angel D. Sappa, & Boris X. Vintimilla. (2018). Adaptive Harris Corners Detector Evaluated with Cross-Spectral Images. In International Conference on Information Technology & Systems (ICITS 2018). ICITS 2018. Advances in Intelligent Systems and Computing (Vol. 721).
Abstract: 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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Patricia L. Suarez, Angel D. Sappa, Boris X. Vintimilla, & Riad I. Hammoud. (2018). Deep Learning based Single Image Dehazing. In 14th IEEE Workshop on Perception Beyond the Visible Spectrum – In conjunction with CVPR 2018. Salt Lake City, Utah. USA.
Abstract: 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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