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Author | Cristhian A. Aguilera; Angel D. Sappa; Ricardo Toledo | ||||
Title | Cross-Spectral Local Descriptors via Quadruplet Network | Type | Journal Article | ||
Year | 2017 | Publication | In Sensors Journal | Abbreviated Journal | |
Volume | Vol. 17 | Issue | Pages | pp. 873 | |
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Call Number | gtsi @ user @ | Serial | 64 | ||
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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 | Victor Santos; Angel D. Sappa; Miguel Oliveira | ||||
Title | Special Issue on Autonomous Driving an Driver Assistance Systems | Type | Journal Article | ||
Year | 2017 | Publication | In Robotics and Autonomous Systems Journal | Abbreviated Journal | |
Volume | Vol. 91 | Issue | Pages | pp. 208-209 | |
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Call Number | gtsi @ user @ | Serial | 65 | ||
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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 | Patricia Suarez & Angel D. Sappa | ||||
Title | Haze-Free Imaging through Haze-Aware Transformer Adaptations | Type | Conference Article | ||
Year | 2024 | Publication | In Fourth International Conference on Innovations in Computational Intelligence and Computer Vision (ICICV 2024) | Abbreviated Journal | |
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Call Number | cidis @ cidis @ | Serial | 236 | ||
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Author | Patricia Suarez, Angel D. Sappa | ||||
Title | A Generative Model for Guided Thermal Image Super-Resolution | Type | Conference Article | ||
Year | 2024 | Publication | In 19th International Conference on Computer Vision Theory and Applications VISAPP 2024 | Abbreviated Journal | |
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Call Number | cidis @ cidis @ | Serial | 240 | ||
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Author | Cristhian A. Aguilera; Angel D. Sappa; R. Toledo | ||||
Title | LGHD: A feature descriptor for matching across non-linear intensity variations | Type | Conference Article | ||
Year | 2015 | Publication | IEEE International Conference on, Quebec City, QC, 2015 | Abbreviated Journal | |
Volume | Issue | Pages | 178 - 181 | ||
Keywords | Feature descriptor, multi-modal, multispectral, NIR, LWIR | ||||
Abstract | This paper presents a new feature descriptor suitable to the task of matching features points between images with nonlinear intensity variations. This includes image pairs with significant illuminations changes, multi-modal image pairs and multi-spectral image pairs. The proposed method describes the neighbourhood of feature points combining frequency and spatial information using multi-scale and multi-oriented Log- Gabor filters. Experimental results show the validity of the proposed approach and also the improvements with respect to the state of the art. | ||||
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Publisher | IEEE | Place of Publication | Quebec City, QC, Canada | Editor | |
Language | English | Summary Language | English | Original Title | |
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Area | Expedition | Conference | 2015 IEEE International Conference on Image Processing (ICIP) | ||
Notes | Approved | no | |||
Call Number | cidis @ cidis @ | Serial | 40 | ||
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Author | Patricia L. Suárez, Angel D. Sappa, Boris X. Vintimilla | ||||
Title | Cycle generative adversarial network: towards a low-cost vegetation index estimation | Type | Conference Article | ||
Year | 2021 | Publication | IEEE International Conference on Image Processing (ICIP 2021) | Abbreviated Journal | |
Volume | 2021-September | Issue | Pages | 2783-2787 | |
Keywords | CyclicGAN, NDVI, near infrared spectra, instance normalization. | ||||
Abstract | This paper presents a novel unsupervised approach to estimate the Normalized Difference Vegetation Index (NDVI).The NDVI is obtained as the ratio between information from the visible and near infrared spectral bands; in the current work, the NDVI is estimated just from an image of the visible spectrum through a Cyclic Generative Adversarial Network (CyclicGAN). This unsupervised architecture learns to estimate the NDVI index by means of an image translation between the red channel of a given RGB image and the NDVI unpaired index’s image. The translation is obtained by means of a ResNET architecture and a multiple loss function. Experimental results obtained with this unsupervised scheme show the validity of the implemented model. Additionally, comparisons with the state of the art approaches are provided showing improvements with the proposed approach. | ||||
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Call Number | cidis @ cidis @ | Serial | 164 | ||
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Author | Cristhian A. Aguilera; Francisco J. Aguilera; Angel D. Sappa; Ricardo Toledo | ||||
Title | Learning crossspectral similarity measures with deep convolutional neural networks | Type | Conference Article | ||
Year | 2016 | Publication | IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) Workshops | Abbreviated Journal | |
Volume | Issue | Pages | 267-275 | ||
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Abstract | The simultaneous use of images from different spectra can be helpful to improve the performance of many com- puter vision tasks. The core idea behind the usage of cross- spectral approaches is to take advantage of the strengths of each spectral band providing a richer representation of a scene, which cannot be obtained with just images from one spectral band. In this work we tackle the cross-spectral image similarity problem by using Convolutional Neural Networks (CNNs). We explore three different CNN archi- tectures to compare the similarity of cross-spectral image patches. Specifically, we train each network with images from the visible and the near-infrared spectrum, and then test the result with two public cross-spectral datasets. Ex- perimental results show that CNN approaches outperform the current state-of-art on both cross-spectral datasets. Ad- ditionally, our experiments show that some CNN architec- tures are capable of generalizing between different cross- spectral domains. | ||||
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Language | English | Summary Language | English | Original Title | |
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Call Number | cidis @ cidis @ | Serial | 48 | ||
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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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