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Raul A. Mira; Patricia L. Suarez; Rafael E. Rivadeneira; Angel D. Sappa |
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PETRA: A Crowdsourcing-Based Platform for Rocks Data Collection and Characterization |
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2019 |
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IEEE ETCM 2019 Fourth Ecuador Technical Chapters Meeting; Guayaquil, Ecuador |
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1-6 |
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This paper presents details of a distributed platform intended for data acquisition, evaluation, storage and visualization, which is fully implemented under the crowdsourcing paradigm. The proposed platform is the result from collaboration between computer science and petrology researchers and it is intended for academic purposes. The platform is designed within a MTV (Model, Template and View) architecture and also designed for a collaborative data store and managing of rocks from multiple readers and writers, taking advantage of ubiquity of web applications, and neutrality of researchers from different
communities to validate the data. The platform is being used and validated by students and academics from our university; in the near future it will be open to other users interested on this topic. |
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112 |
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Author |
Santos V.; Angel D. Sappa.; Oliveira M. & de la Escalera A. |
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Special Issue on Autonomous Driving and Driver Assistance Systems |
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2019 |
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In Robotics and Autonomous Systems |
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121 |
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119 |
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Cristhian A. Aguilera; Cristhian Aguilera; Angel D. Sappa |
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Melamine faced panels defect classification beyond the visible spectrum. |
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2018 |
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In Sensors 2018 |
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Vol. 11 |
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Issue 11 |
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In this work, we explore the use of images from different spectral bands to classify defects in melamine faced panels, which could appear through the production process. Through experimental evaluation, we evaluate the use of images from the visible (VS), near-infrared (NIR), and long wavelength infrared (LWIR), to classify the defects using a feature descriptor learning approach together with a support vector machine classifier. Two descriptors were evaluated, Extended Local Binary Patterns (E-LBP) and SURF using a Bag of Words (BoW) representation. The evaluation was carried on with an image set obtained during this work, which contained five different defect categories that currently occurs in the industry. Results show that using images from beyond
the visual spectrum helps to improve classification performance in contrast with a single visible spectrum solution. |
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89 |
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Jorge L. Charco; Boris X. Vintimilla; Angel D. Sappa |
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Deep learning based camera pose estimation in multi-view environment. |
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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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93 |
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