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Patricia L. Suárez, Angel D. Sappa, Boris X. Vintimilla |
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Title |
Cycle generative adversarial network: towards a low-cost vegetation index estimation |
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2021 |
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IEEE International Conference on Image Processing (ICIP 2021) |
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2021-September |
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2783-2787 |
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CyclicGAN, NDVI, near infrared spectra, instance normalization. |
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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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cidis @ cidis @ |
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164 |
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Patricia L. Suárez, Dario Carpio, and Angel Sappa |
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Title |
Non-Homogeneous Haze Removal through a Multiple Attention Module Architecture. |
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2021 |
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16 International Symposium on Visual Computing. Octubre 4-6, 2021. Lecture Notes in Computer Science |
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13018 |
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178-190 |
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cidis @ cidis @ |
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162 |
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Henry O. Velesaca; Raul A. Mira; Patricia L. Suarez; Christian X. Larrea; Angel D. Sappa. |
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Deep Learning based Corn Kernel Classification. |
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2020 |
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The 1st International Workshop and Prize Challenge on Agriculture-Vision: Challenges & Opportunities for Computer Vision in Agriculture on the Conference Computer on Vision and Pattern Recongnition (CVPR 2020) |
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2020-June |
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9150684 |
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294-302 |
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This paper presents a full pipeline to classify sample sets of corn kernels. The proposed approach follows a segmentation-classification scheme. The image segmentation is performed through a well known deep learning based
approach, the Mask R-CNN architecture, while the classification is performed by means of a novel-lightweight network specially designed for this task—good corn kernel, defective corn kernel and impurity categories are considered.
As a second contribution, a carefully annotated multitouching corn kernel dataset has been generated. This dataset has been used for training the segmentation and
the classification modules. Quantitative evaluations have been performed and comparisons with other approaches provided showing improvements with the proposed pipeline. |
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21607508 |
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978-172819360-1 |
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no |
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cidis @ cidis @ |
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124 |
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Henry O. Velesaca, Steven Araujo, Patricia L. Suarez, Ángel Sanchez & Angel D. Sappa |
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Off-the-Shelf Based System for Urban Environment Video Analytics. |
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2020 |
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The 27th International Conference on Systems, Signals and Image Processing (IWSSIP 2020) |
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2020-July |
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9145121 |
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459-464 |
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Greenhouse gases, carbon footprint, object detection, object tracking, website framework, off-the-shelf video analytics. |
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This paper presents the design and implementation details of a system build-up by using off-the-shelf algorithms for urban video analytics. The system allows the connection to public video surveillance camera networks to obtain the necessary
information to generate statistics from urban scenarios (e.g., amount of vehicles, type of cars, direction, numbers of persons, etc.). The obtained information could be used not only for traffic management but also to estimate the carbon footprint of urban scenarios. As a case study, a university campus is selected to
evaluate the performance of the proposed system. The system is implemented in a modular way so that it is being used as a testbed to evaluate different algorithms. Implementation results are provided showing the validity and utility of the proposed approach. |
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21578672 |
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978-172817539-3 |
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no |
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cidis @ cidis @ |
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125 |
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