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Author Julien Poujol; Cristhian A. Aguilera; Etienne Danos; Boris X. Vintimilla; Ricardo Toledo; Angel D. Sappa
Title A visible-Thermal Fusion based Monocular Visual Odometry Type Conference Article
Year 2015 Publication Iberian Robotics Conference (ROBOT 2015), International Conference on, Lisbon, Portugal, 2015 Abbreviated Journal
Volume 417 Issue Pages 517-528
Keywords Monocular Visual Odometry; LWIR-RGB cross-spectral Imaging; Image Fusion
Abstract The manuscript evaluates the performance of a monocular visual odometry approach when images from different spectra are considered, both independently and fused. The objective behind this evaluation is to analyze if classical approaches can be improved when the given images, which are from different spectra, are fused and represented in new domains. The images in these new domains should have some of the following properties: i) more robust to noisy data; ii) less sensitive to changes (e.g., lighting); iii) more rich in descriptive information, among other. In particular in the current work two different image fusion strategies are considered. Firstly, images from the visible and thermal spectrum are fused using a Discrete Wavelet Transform (DWT) approach. Secondly, a monochrome threshold strategy is considered. The obtained representations are evaluated under a visual odometry framework, highlighting their advantages and disadvantages, using different urban and semi-urban scenarios. Comparisons with both monocular-visible spectrum and monocular-infrared spectrum, are also provided showing the validity of the proposed approach.
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Call Number cidis @ cidis @ Serial 44
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Author Dennis G. Romero; A. Frizera; Angel D. Sappa; Boris X. Vintimilla; T.F. Bastos
Title A predictive model for human activity recognition by observing actions and context Type Conference Article
Year 2015 Publication ACIVS 2015 (Advanced Concepts for Intelligent Vision Systems), International Conference on, Catania, Italy, 2015 Abbreviated Journal
Volume Issue Pages 323 - 333
Keywords Edge width, Image blu,r Defocus map, Edge model
Abstract This paper presents a novel model to estimate human activities – a human activity is defined by a set of human actions. The proposed approach is based on the usage of Recurrent Neural Networks (RNN) and Bayesian inference through the continuous monitoring of human actions and its surrounding environment. In the current work human activities are inferred considering not only visual analysis but also additional resources; external sources of information, such as context information, are incorporated to contribute to the activity estimation. The novelty of the proposed approach lies in the way the information is encoded, so that it can be later associated according to a predefined semantic structure. Hence, a pattern representing a given activity can be defined by a set of actions, plus contextual information or other kind of information that could be relevant to describe the activity. Experimental results with real data are provided showing the validity of the proposed approach.
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Call Number cidis @ cidis @ Serial 43
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Author Angel D. Sappa.
Title ICT Applications for Smart Cities Type Book Chapter
Year 2022 Publication Intelligent Systems Reference Library Abbreviated Journal BOOK
Volume 224 Issue Pages
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Call Number cidis @ cidis @ Serial 198
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Author Michael Teutsch, Angel Sappa & Riad Hammoud
Title Computer Vision in the Infrared Spectrum: Challenges and ApproachesComputer Vision in the Infrared Spectrum: Challenges and Approaches Type Journal Article
Year 2021 Publication Synthesis Lectures on Computer Vision Abbreviated Journal
Volume Vol. 10 No. 2 Issue Pages pp. 138
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Call Number cidis @ cidis @ Serial 166
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Author Silva Steven, Paillacho Dennys, Verdezoto Nervo, Hernandez Juan David
Title TOWARDS ONLINE SOCIALLY ACCEPTABLE ROBOT NAVIGATION Type Conference Article
Year 2022 Publication IEEE INTERNATIONAL CONFERENCE ON AUTOMATION SCIENCE AND ENGINEERING, Abbreviated Journal
Volume 2022-August Issue Pages 707-714
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Call Number cidis @ cidis @ Serial 199
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Author Xavier Soria , Gonzalo Pomboza-Junez & Angel Sappa.
Title LDC: Lightweight Dense CNN for Edge Detection. Type Journal Article
Year 2022 Publication IEEE Access journal Abbreviated Journal
Volume Vol. 10 Issue Pages pp. 68281-68290
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Call Number cidis @ cidis @ Serial 183
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Author Pereira J., Mora M. & W. Agila
Title Qualitative Model to Maximize Shrimp Growth at Low Cost Type Journal Article
Year 2021 Publication 5th Ecuador Technical Chapters Meeting (ETCM 2021), Octubre 12 – 15 Abbreviated Journal
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Call Number cidis @ cidis @ Serial 167
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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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Call Number cidis @ cidis @ Serial 195
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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
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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