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Author | Raul A. Mira; Patricia L. Suarez; Rafael E. Rivadeneira; Angel D. Sappa | ||||
Title | PETRA: A Crowdsourcing-Based Platform for Rocks Data Collection and Characterization | Type | Conference Article | ||
Year | 2019 | Publication | IEEE ETCM 2019 Fourth Ecuador Technical Chapters Meeting; Guayaquil, Ecuador | Abbreviated Journal | |
Volume | Issue | Pages | 1-6 | ||
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Abstract | This paper presents details of a distributed platform intended for data acquisition, evaluation, storage and visualization, which is fully implemented under the crowdsourcing paradigm. The proposed platform is the result from collaboration between computer science and petrology researchers and it is intended for academic purposes. The platform is designed within a MTV (Model, Template and View) architecture and also designed for a collaborative data store and managing of rocks from multiple readers and writers, taking advantage of ubiquity of web applications, and neutrality of researchers from different communities to validate the data. The platform is being used and validated by students and academics from our university; in the near future it will be open to other users interested on this topic. |
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Call Number | gtsi @ user @ | Serial | 112 | ||
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Author | Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla | ||||
Title | Image patch similarity through a meta-learning metric based approach | Type | Conference Article | ||
Year | 2019 | Publication | 15th International Conference on Signal Image Technology & Internet based Systems (SITIS 2019); Sorrento, Italia | Abbreviated Journal | |
Volume | Issue | Pages | 511-517 | ||
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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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Call Number | gtsi @ user @ | Serial | 115 | ||
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Author | Santos V.; Angel D. Sappa.; Oliveira M. & de la Escalera A. | ||||
Title | Special Issue on Autonomous Driving and Driver Assistance Systems | Type | Journal Article | ||
Year | 2019 | Publication | In Robotics and Autonomous Systems | Abbreviated Journal | |
Volume | 121 | Issue | Pages | ||
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Call Number | gtsi @ user @ | Serial | 119 | ||
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Author | Cristhian A. Aguilera; Cristhian Aguilera; Angel D. Sappa | ||||
Title | Melamine faced panels defect classification beyond the visible spectrum. | Type | Journal Article | ||
Year | 2018 | Publication | In Sensors 2018 | Abbreviated Journal | |
Volume | Vol. 11 | Issue | Issue 11 | Pages | |
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Abstract | 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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Call Number | gtsi @ user @ | Serial | 89 | ||
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Author | Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla; Riad I. Hammoud | ||||
Title | Deep Learning based Single Image Dehazing | Type | Conference Article | ||
Year | 2018 | Publication | 14th IEEE Workshop on Perception Beyond the Visible Spectrum – In conjunction with CVPR 2018. Salt Lake City, Utah. USA | Abbreviated Journal | |
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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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Call Number | gtsi @ user @ | Serial | 83 | ||
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Author | Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla | ||||
Title | Vegetation Index Estimation from Monospectral Images | Type | Conference Article | ||
Year | 2018 | Publication | 15th International Conference, Image Analysis and Recognition (ICIAR 2018), Póvoa de Varzim, Portugal. Lecture Notes in Computer Science | Abbreviated Journal | |
Volume | 10882 | Issue | Pages | 353-362 | |
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Abstract | 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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Call Number | gtsi @ user @ | Serial | 82 | ||
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Author | Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla; Riad I. Hammoud | ||||
Title | Near InfraRed Imagery Colorization | Type | Conference Article | ||
Year | 2018 | Publication | 25 th IEEE International Conference on Image Processing, ICIP 2018 | Abbreviated Journal | |
Volume | Issue | Pages | 2237-2241 | ||
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Abstract | This paper proposes a stacked conditional Generative Adversarial Network-based method for Near InfraRed (NIR) imagery colorization. We propose a variant architecture of Generative Adversarial Network (GAN) that uses multiple loss functions over a conditional probabilistic generative model. We show that this new architecture/loss-function yields better generalization and representation of the generated colored IR images. The proposed approach is evaluated on a large test dataset and compared to recent state of the art methods using standard metrics.1 Index Terms—Convolutional Neural Networks (CNN), Generative Adversarial Network (GAN), Infrared Imagery colorization. |
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Call Number | gtsi @ user @ | Serial | 81 | ||
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Author | Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla | ||||
Title | Adaptive Harris Corners Detector Evaluated with Cross-Spectral Images | Type | Conference Article | ||
Year | 2018 | Publication | International Conference on Information Technology & Systems (ICITS 2018). ICITS 2018. Advances in Intelligent Systems and Computing | Abbreviated Journal | |
Volume | 721 | Issue | Pages | ||
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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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Notes | 1 | Approved | no | ||
Call Number | gtsi @ user @ | Serial | 84 | ||
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Author | Xavier Soria; Angel D. Sappa | ||||
Title | Improving Edge Detection in RGB Images by Adding NIR Channel. | Type | Conference Article | ||
Year | 2018 | Publication | 14th IEEE International Conference on Signal Image Technology & Internet based Systems (SITIS 2018) | Abbreviated Journal | |
Volume | Issue | Pages | 266-273 | ||
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Call Number | gtsi @ user @ | Serial | 95 | ||
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Author | Xavier Soria; Angel D. Sappa; Riad Hammoud | ||||
Title | Wide-Band Color Imagery Restoration for RGB-NIR Single Sensor Image. Sensors 2018 ,2059. | Type | Journal Article | ||
Year | 2018 | Publication | Abbreviated Journal | ||
Volume | Vol. 18 | Issue | Issue 7 | Pages | |
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Abstract | Multi-spectral RGB-NIR sensors have become ubiquitous in recent years. These sensors allow the visible and near-infrared spectral bands of a given scene to be captured at the same time. With such cameras, the acquired imagery has a compromised RGB color representation due to near-infrared bands (700–1100 nm) cross-talking with the visible bands (400–700 nm). This paper proposes two deep learning-based architectures to recover the full RGB color images, thus removing the NIR information from the visible bands. The proposed approaches directly restore the high-resolution RGB image by means of convolutional neural networks. They are evaluated with several outdoor images; both architectures reach a similar performance when evaluated in different scenarios and using different similarity metrics. Both of them improve the state of the art approaches. | ||||
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Call Number | gtsi @ user @ | Serial | 96 | ||
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