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| Author | Juan A. Carvajal; Dennis G. Romero; Angel D. Sappa | ||||
| Title | Fine-tuning based deep covolutional networks for lepidopterous genus recognition | Type | Conference Article | ||
| Year | 2016 | Publication | XXI IberoAmerican Congress on Pattern Recognition | Abbreviated Journal | |
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Issue | Pages | 1-9 | ||
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| Abstract | This paper describes an image classi cation approach ori- ented to identify specimens of lepidopterous insects recognized at Ecuado- rian ecological reserves. This work seeks to contribute to studies in the area of biology about genus of butter ies and also to facilitate the reg- istration of unrecognized specimens. The proposed approach is based on the ne-tuning of three widely used pre-trained Convolutional Neural Networks (CNNs). This strategy is intended to overcome the reduced number of labeled images. Experimental results with a dataset labeled by expert biologists, is presented|a recognition accuracy above 92% is reached. 1 Introductio | ||||
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| Notes | Approved | no | |||
| Call Number | cidis @ cidis @ | Serial | 53 | ||
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| Author | Angely Oyola; Dennis G. Romero; Boris X. Vintimilla | ||||
| Title | A Dijkstra-based algorithm for selecting the Shortest-Safe Evacuation Routes in dynamic environments (SSER) | Type | Conference Article | ||
| Year | 2017 | Publication | The 30th International Conference on Industrial, Engineering, Other Applications of Applied Intelligent Systems (IEA/AIE 2017) | Abbreviated Journal | |
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Issue | Pages | 131-135 | ||
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| Notes | Approved | no | |||
| Call Number | cidis @ cidis @ | Serial | 55 | ||
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| Author | Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla | ||||
| Title | Cross-spectral Image Patch Similarity using Convolutional Neural Network | Type | Conference Article | ||
| Year | 2017 | Publication | 2017 IEEE International Workshop of Electronics, Control, Measurement, Signals and their application to Mechatronics (ECMSM) | Abbreviated Journal | |
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Issue | Pages | 1-5 | ||
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| Notes | Approved | no | |||
| Call Number | cidis @ cidis @ | Serial | 57 | ||
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| Author | Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla | ||||
| Title | Learning to Colorize Infrared Images | Type | Conference Article | ||
| Year | 2017 | Publication | 15th International Conference on Practical Applications of Agents and Multi-Agent Systems | Abbreviated Journal | |
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| Notes | Approved | no | |||
| Call Number | cidis @ cidis @ | Serial | 58 | ||
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| Author | Angel J. Valencia; Roger M. Idrovo; Angel D. Sappa; Douglas Plaza G.; Daniel Ochoa | ||||
| Title | A 3D Vision Based Approach for Optimal Grasp of Vacuum Grippers | Type | Conference Article | ||
| Year | 2017 | Publication | 2017 IEEE International Workshop of Electronics, Control, Measurement, Signals and their application to Mechatronics (ECMSM) | Abbreviated Journal | |
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Issue | Pages | 1-6 | ||
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| Notes | Approved | no | |||
| Call Number | cidis @ cidis @ | Serial | 60 | ||
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| Author | Jorge L. Charco; Boris X. Vintimilla; Angel D. Sappa | ||||
| Title | Deep learning based camera pose estimation in multi-view environment. | Type | Conference Article | ||
| Year | 2018 | Publication | 14th IEEE International Conference on Signal Image Technology & Internet based Systems (SITIS 2018) | Abbreviated Journal | |
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Issue | Pages | 224-228 | ||
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| Abstract | This paper proposes to use a deep learning network architecture for relative camera pose estimation on a multi-view environment. The proposed network is a variant architecture of AlexNet to use as regressor for prediction the relative translation and rotation as output. The proposed approach is trained from scratch on a large data set that takes as input a pair of images from the same scene. This new architecture is compared with a previous approach using standard metrics, obtaining better results on the relative camera pose. | ||||
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| Notes | Approved | no | |||
| Call Number | gtsi @ user @ | Serial | 93 | ||
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| Author | Alex Ferrin; Julio Larrea; Miguel Realpe; Daniel Ochoa | ||||
| Title | Detection of utility poles from noisy Point Cloud Data in Urban environments. | Type | Conference Article | ||
| Year | 2018 | Publication | Artificial Intelligence and Cloud Computing Conference (AICCC 2018) | Abbreviated Journal | |
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Issue | Pages | 53-57 | ||
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| Abstract | In recent years 3D urban maps have become more common, thus providing complex point clouds that include diverse urban furniture such as pole-like objects. Utility poles detection in urban environment is of particular interest for electric utility companies in order to maintain an updated inventory for better planning and management. The present study develops an automatic method for the detection of utility poles from noisy point cloud data of Guayaquil – Ecuador, where many poles are located next to buildings, or houses are built until the border of the sidewalk getting very close to poles, which increases the difficulty of discriminating poles, walls, columns, fences and building corners. | ||||
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| Notes | Approved | no | |||
| Call Number | gtsi @ user @ | Serial | 94 | ||
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| Author | Marjorie Chalen; Boris X. Vintimilla | ||||
| Title | Towards Action Prediction Applying Deep Learning | Type | Journal Article | ||
| Year | 2019 | Publication | Latin American Conference on Computational Intelligence (LA-CCI); Guayaquil, Ecuador; 11-15 Noviembre 2019 | Abbreviated Journal | |
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Issue | Pages | pp. 1-3 | ||
| Keywords | action prediction, early recognition, early detec- tion, action anticipation, cnn, deep learning, rnn, lstm. | ||||
| Abstract | Considering the incremental development future action prediction by video analysis task of computer vision where it is done based upon incomplete action executions. Deep learning is playing an important role in this task framework. Thus, this paper describes recently techniques and pertinent datasets utilized in human action prediction task. | ||||
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| Notes | Approved | no | |||
| Call Number | cidis @ cidis @ | Serial | 129 | ||
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| Author | Armin Mehri; Angel D. Sappa | ||||
| Title | Colorizing Near Infrared Images through a Cyclic Adversarial Approach of Unpaired Samples | Type | Conference Article | ||
| Year | 2019 | Publication | Conference on Computer Vision and Pattern Recognition Workshops (CVPR 2019); Long Beach, California, United States | Abbreviated Journal | |
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Issue | Pages | 971-979 | ||
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| Abstract | This paper presents a novel approach for colorizing near infrared (NIR) images. The approach is based on image-to-image translation using a Cycle-Consistent adversarial network for learning the color channels on unpaired dataset. This architecture is able to handle unpaired datasets. The approach uses as generators tailored networks that require less computation times, converge faster and generate high quality samples. The obtained results have been quantitatively—using standard evaluation metrics—and qualitatively evaluated showing considerable improvements with respect to the state of the art |
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| Notes | Approved | no | |||
| Call Number | gtsi @ user @ | Serial | 105 | ||
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| Author | Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla; Riad I. Hammoud | ||||
| Title | Image Vegetation Index through a Cycle Generative Adversarial Network | Type | Conference Article | ||
| Year | 2019 | Publication | Conference on Computer Vision and Pattern Recognition Workshops (CVPR 2019); Long Beach, California, United States | Abbreviated Journal | |
| Volume |
Issue | Pages | 1014-1021 | ||
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| Abstract | This paper proposes a novel approach to estimate the Normalized Difference Vegetation Index (NDVI) just from an RGB image. The NDVI values are obtained by using images from the visible spectral band together with a synthetic near infrared image obtained by a cycled GAN. The cycled GAN network is able to obtain a NIR image from a given gray scale image. It is trained by using unpaired set of gray scale and NIR images by using a U-net architecture and a multiple loss function (gray scale images are obtained from the provided RGB images). Then, the NIR image estimated with the proposed cycle generative adversarial network is used to compute the NDVI index. Experimental results are provided showing the validity of the proposed approach. Additionally, comparisons with previous approaches are also provided. |
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| Notes | Approved | no | |||
| Call Number | gtsi @ user @ | Serial | 106 | ||
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