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Author Luis Chuquimarca, Boris X. Vintimilla & Sergio Velastin
Title Classifying Healthy and Defective Fruits with a Siamese Architecture and CNN Models Type Conference Article
Year 2024 Publication Accepted in 14th International Conference on Pattern Recognition Systems (ICPRS) Abbreviated Journal
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Notes (up) Approved no
Call Number cidis @ cidis @ Serial 245
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Author Jácome Galarza, Luis Roberto
Title Estimation of Corn Crop Yield using Multimodal Deep Learning from Multispectral Images and Environmental Sensors Type Conference Article
Year 2024 Publication In accepted in 19ª Conferência Ibérica de Sistemas e Tecnologias de Informação; CISTI'2024 Abbreviated Journal
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Notes (up) Approved no
Call Number cidis @ cidis @ Serial 246
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Author Patricia L. Suarez, Dario Carpio, Angel D. Sappa
Title Enhancement of Guided Thermal Image Super-Resolution Approaches Type Journal
Year 2024 Publication Neurocomputing Abbreviated Journal
Volume 573 Issue Pages
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Area Expedition Conference Neurocomputing
Notes (up) Approved no
Call Number cidis @ cidis @ Serial 247
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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 (up) 1 Approved no
Call Number gtsi @ user @ Serial 84
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