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Author |
Miguel Oliveira; Vítor Santos; Angel D. Sappa; Paulo Dias |
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Title |
Scene representations for autonomous driving: an approach based on polygonal primitives |
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Conference Article |
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2015 |
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Iberian Robotics Conference (ROBOT 2015), Lisbon, Portugal, 2015 |
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417 |
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503-515 |
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Scene reconstruction, Point cloud, Autonomous vehicles |
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In this paper, we present a novel methodology to compute a 3D scene representation. The algorithm uses macro scale polygonal primitives to model the scene. This means that the representation of the scene is given as a list of large scale polygons that describe the geometric structure of the environment. Results show that the approach is capable of producing accurate descriptions of the scene. In addition, the algorithm is very efficient when compared to other techniques. |
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Springer International Publishing Switzerland 2016 |
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English |
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Second Iberian Robotics Conference |
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no |
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Call Number |
cidis @ cidis @ |
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45 |
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Author |
Cristhian A. Aguilera; Francisco J. Aguilera; Angel D. Sappa; Ricardo Toledo |
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Title |
Learning crossspectral similarity measures with deep convolutional neural networks |
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Conference Article |
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Year |
2016 |
Publication |
IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) Workshops |
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267-275 |
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The simultaneous use of images from different spectra can be helpful to improve the performance of many com- puter vision tasks. The core idea behind the usage of cross- spectral approaches is to take advantage of the strengths of each spectral band providing a richer representation of a scene, which cannot be obtained with just images from one spectral band. In this work we tackle the cross-spectral image similarity problem by using Convolutional Neural Networks (CNNs). We explore three different CNN archi- tectures to compare the similarity of cross-spectral image patches. Specifically, we train each network with images from the visible and the near-infrared spectrum, and then test the result with two public cross-spectral datasets. Ex- perimental results show that CNN approaches outperform the current state-of-art on both cross-spectral datasets. Ad- ditionally, our experiments show that some CNN architec- tures are capable of generalizing between different cross- spectral domains. |
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no |
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cidis @ cidis @ |
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48 |
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Author |
Juan A. Carvajal; Dennis G. Romero; Angel D. Sappa |
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Title |
Fine-tuning based deep covolutional networks for lepidopterous genus recognition |
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Conference Article |
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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 |
Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla |
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Title |
Cross-spectral Image Patch Similarity using Convolutional Neural Network |
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Conference Article |
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Year |
2017 |
Publication |
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 |
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Title |
Learning to Colorize Infrared Images |
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Conference Article |
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2017 |
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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 |
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Title |
RGBN Multispectral Images: a Novel Color Restoration Approach |
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Conference Article |
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Year |
2017 |
Publication |
15th International Conference on Practical Applications of Agents and Multi-Agent Systems |
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Volume |
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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Author |
Angel J. Valencia; Roger M. Idrovo; Angel D. Sappa; Douglas Plaza G.; Daniel Ochoa |
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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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Call Number |
cidis @ cidis @ |
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60 |
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Author |
Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla |
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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 |
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Title |
Deep learning based camera pose estimation in multi-view environment. |
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Conference Article |
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2018 |
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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 |
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Title |
Colorizing Near Infrared Images through a Cyclic Adversarial Approach of Unpaired Samples |
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2019 |
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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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