| Records |
| Author |
Miguel Oliveira; Vítor Santos; Angel D. Sappa; Paulo Dias; A. Paulo Moreira |
| Title |
Incremental Texture Mapping for Autonomous Driving |
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Journal Article |
| Year |
2016 |
Publication |
Robotics and Autonomous Systems Journal |
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| Volume |
Vol. 84 |
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pp. 113-128 |
| Keywords |
Scene reconstruction, Autonomous driving, Texture mapping |
| Abstract |
Autonomous vehicles have a large number of on-board sensors, not only for providing coverage all around the vehicle, but also to ensure multi-modality in the observation of the scene. Because of this, it is not trivial to come up with a single, unique representation that feeds from the data given by all these sensors. We propose an algorithm which is capable of mapping texture collected from vision based sensors onto a geometric description of the scenario constructed from data provided by 3D sensors. The algorithm uses a constrained Delaunay triangulation to produce a mesh which is updated using a specially devised sequence of operations. These enforce a partial configuration of the mesh that avoids bad quality textures and ensures that there are no gaps in the texture. Results show that this algorithm is capable of producing fine quality textures. |
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cidis @ cidis @ |
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50 |
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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 |
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1-9 |
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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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cidis @ cidis @ |
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53 |
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| Author |
Angel D. Sappa; Cristhian A. Aguilera; Juan A. Carvajal Ayala; Miguel Oliveira; Dennis Romero; Boris X. Vintimilla; Ricardo Toledo |
| Title |
Monocular visual odometry: a cross-spectral image fusion based approach |
Type |
Journal Article |
| Year |
2016 |
Publication |
Robotics and Autonomous Systems Journal |
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Vol. 86 |
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pp. 26-36 |
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Monocular visual odometry LWIR-RGB cross-spectral imaging Image fusion |
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This manuscript evaluates the usage of fused cross-spectral images in a monocular visual odometry approach. Fused images are obtained through a Discrete Wavelet Transform (DWT) scheme, where the best setup is em- pirically obtained by means of a mutual information based evaluation met- ric. The objective is to have a exible scheme where fusion parameters are adapted according to the characteristics of the given images. Visual odom- etry is computed from the fused monocular images using an off the shelf approach. Experimental results using data sets obtained with two different platforms are presented. Additionally, comparison with a previous approach as well as with monocular-visible/infrared spectra are also provided showing the advantages of the proposed scheme. |
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cidis @ cidis @ |
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54 |
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| Author |
Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla |
| Title |
Cross-spectral Image Patch Similarity using Convolutional Neural Network |
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Conference Article |
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2017 |
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2017 IEEE International Workshop of Electronics, Control, Measurement, Signals and their application to Mechatronics (ECMSM) |
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1-5 |
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no |
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cidis @ cidis @ |
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57 |
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| Author |
Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla |
| Title |
Learning to Colorize Infrared Images |
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Conference Article |
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2017 |
Publication |
15th International Conference on Practical Applications of Agents and Multi-Agent Systems |
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no |
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cidis @ cidis @ |
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58 |
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| Author |
Cristhian A. Aguilera; Xaver Soria; Angel D. Sappa; Ricardo Toledo |
| Title |
RGBN Multispectral Images: a Novel Color Restoration Approach |
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Conference Article |
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2017 |
Publication |
15th International Conference on Practical Applications of Agents and Multi-Agent Systems |
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619 |
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155-163 |
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no |
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cidis @ cidis @ |
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59 |
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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 |
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Conference Article |
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2017 |
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2017 IEEE International Workshop of Electronics, Control, Measurement, Signals and their application to Mechatronics (ECMSM) |
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1-6 |
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no |
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cidis @ cidis @ |
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60 |
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| Author |
Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla |
| Title |
Infrared Image Colorization based on a Triplet DCGAN Architecture. |
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Conference Article |
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2017 |
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13th IEEE Workshop on Perception Beyond the Visible Spectrum – In conjunction with CVPR 2017. (This paper has been selected as “Best Paper Award” ) |
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2017-July |
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212-217 |
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cidis @ cidis @ |
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62 |
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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. |
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Conference Article |
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2018 |
Publication |
14th IEEE International Conference on Signal Image Technology & Internet based Systems (SITIS 2018) |
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224-228 |
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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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gtsi @ user @ |
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93 |
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| Author |
Armin Mehri; Angel D. Sappa |
| Title |
Colorizing Near Infrared Images through a Cyclic Adversarial Approach of Unpaired Samples |
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Conference Article |
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2019 |
Publication |
Conference on Computer Vision and Pattern Recognition Workshops (CVPR 2019); Long Beach, California, United States |
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971-979 |
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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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gtsi @ user @ |
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105 |
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