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| Author | Rafael E. Rivadeneira; Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla. | ||||
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Thermal Image SuperResolution through Deep Convolutional Neural Network. | Type | Conference Article | ||
| Year | 2019 | Publication | 16th International Conference on Image Analysis and Recognition (ICIAR 2019); Waterloo, Canadá | Abbreviated Journal | |
| Volume | Issue | Pages | 417-426 | ||
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| 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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| Notes | Approved | no | |||
| Call Number | gtsi @ user @ | Serial | 103 | ||
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| Author | Xavier Soria, Yachuan Li, Mohammad Rouhani & Angel D. Sappa | ||||
| Title |
Tiny and Efficient Model for the Edge Detection Generalization | Type | Conference Article | ||
| Year | 2023 | Publication | Proceedings – 2023 IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2023 | Abbreviated Journal | |
| Volume | Issue | Pages | 1356 - 1365 | ||
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| Notes | Approved | no | |||
| Call Number | cidis @ cidis @ | Serial | 229 | ||
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| Author | Jorge L. Charco; Angel D. Sappa; Boris X. Vintimilla; Henry O. Velesaca | ||||
| Title |
Transfer Learning from Synthetic Data in the Camera Pose Estimation Problem | Type | Conference Article | ||
| Year | 2020 | Publication | The 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020); Valletta, Malta; 27-29 Febrero 2020 | Abbreviated Journal | |
| Volume | 4 | Issue | Pages | 498-505 | |
| Keywords | Relative Camera Pose Estimation, Siamese Architecture, Synthetic Data, Deep Learning, Multi-View Environments, Extrinsic Camera Parameters. | ||||
| 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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| ISSN | ISBN | 978-989758402-2 | Medium | ||
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| Notes | Approved | no | |||
| Call Number | gtsi @ user @ | Serial | 120 | ||
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| Author | Patricia L. Suarez, Dario Carpio, Angel D. Sappa and Henry O. Velesaca | ||||
| Title |
Transformer based Image Dehazing. | Type | Conference Article | ||
| Year | 2022 | Publication | 16TH International Conference On Signal Image Technology & Internet Based Systems SITIS 2022. | Abbreviated Journal | |
| Volume | Issue | Pages | 148-154 | ||
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| Notes | Approved | no | |||
| Call Number | cidis @ cidis @ | Serial | 195 | ||
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| Author | Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla | ||||
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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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| Notes | Approved | no | |||
| Call Number | gtsi @ user @ | Serial | 82 | ||
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| Author | Henry O. Velesaca, Patricia L. Suárez, Dario Carpio, Rafael E. Rivadeneira, Ángel Sánchez, Angel D. Sappa. | ||||
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Video Analytics in Urban Environments: Challenges and Approaches. | Type | Book Chapter | ||
| Year | 2022 | Publication | ICT Applications for Smart Cities Part of the Intelligent Systems Reference Library book series | Abbreviated Journal | BOOK |
| Volume | 224 | Issue | Pages | 101-122 | |
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| Call Number | cidis @ cidis @ | Serial | 196 | ||
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| Author | Angel D. Sappa; Juan A. Carvajal; Cristhian A. Aguilera; Miguel Oliveira; Dennis G. Romero; Boris X. Vintimilla | ||||
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Wavelet-Based Visible and Infrared Image Fusion: A Comparative Study | Type | Journal Article | ||
| Year | 2016 | Publication | Sensors Journal | Abbreviated Journal | |
| Volume | Vol. 16 | Issue | Pages | pp. 1-15 | |
| Keywords | image fusion; fusion evaluation metrics; visible and infrared imaging; discrete wavelet transform | ||||
| Abstract | This paper evaluates different wavelet-based cross-spectral image fusion strategies adopted to merge visible and infrared images. The objective is to find the best setup independently of the evaluation metric used to measure the performance. Quantitative performance results are obtained with state of the art approaches together with adaptations proposed in the current work. The options evaluated in the current work result from the combination of different setups in the wavelet image decomposition stage together with different fusion strategies for the final merging stage that generates the resulting representation. Most of the approaches evaluate results according to the application for which they are intended for. Sometimes a human observer is selected to judge the quality of the obtained results. In the current work, quantitative values are considered in order to find correlations between setups and performance of obtained results; these correlations can be used to define a criteria for selecting the best fusion strategy for a given pair of cross-spectral images. The whole procedure is evaluated with a large set of correctly registered visible and infrared image pairs, including both Near InfraRed (NIR) and LongWave InfraRed (LWIR). | ||||
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| Language | English | Summary Language | English | Original Title | |
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| Call Number | cidis @ cidis @ | Serial | 47 | ||
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| Author | Xavier Soria; Angel D. Sappa; Riad Hammoud | ||||
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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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| Notes | Approved | no | |||
| Call Number | gtsi @ user @ | Serial | 96 | ||
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