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Alex Ferrin; Julio Larrea; Miguel Realpe; Daniel Ochoa |
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Detection of utility poles from noisy Point Cloud Data in Urban environments. |
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2018 |
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Artificial Intelligence and Cloud Computing Conference (AICCC 2018) |
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53-57 |
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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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94 |
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Xavier Soria; Angel D. Sappa |
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Title |
Improving Edge Detection in RGB Images by Adding NIR Channel. |
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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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266-273 |
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95 |
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Xavier Soria; Angel D. Sappa; Riad Hammoud |
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Wide-Band Color Imagery Restoration for RGB-NIR Single Sensor Image. Sensors 2018 ,2059. |
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2018 |
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Vol. 18 |
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Issue 7 |
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Multi-spectral RGB-NIR sensors have become ubiquitous in recent years. These sensors allow the visible and near-infrared spectral bands of a given scene to be captured at the same time. With such cameras, the acquired imagery has a compromised RGB color representation due to near-infrared bands (700–1100 nm) cross-talking with the visible bands (400–700 nm). This paper proposes two deep learning-based architectures to recover the full RGB color images, thus removing the NIR information from the visible bands. The proposed approaches directly restore the high-resolution RGB image by means of convolutional neural networks. They are evaluated with several outdoor images; both architectures reach a similar performance when evaluated in different scenarios and using different similarity metrics. Both of them improve the state of the art approaches. |
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gtsi @ user @ |
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96 |
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