Image Classification Using Sum-Product Networks for Autonomous Flight of Micro Aerial Vehicles

Bruno Massoni Sguerra, Fabio G. Cozman

Resultado de la investigación: Contribución a una conferenciaArtículo de conferencia

7 Citas (Scopus)

Resumen

© 2016 IEEE. Flying autonomous micro aerial vehicles (MAVs) in indoor environments is still a challenging task, as MAVs are not capable of carrying heavy sensors as Lidar or RGD-B, and GPS signals are not reliable indoors. We investigate a strategy where image classification is used to guide a MAV, one of the main requirements then is to have a classifier that can produce results quickly during operation. The goal here is to explore the performance of Sum-Product Networks and Arithmetic Circuits as image classifiers, because these formalisms lead to deep probabilistic models that are tractable during operation. We have trained and tested our classifiers using the Libra toolkit and real images. We describe our approach and report the result of our experiments in the paper.
Idioma originalInglés estadounidense
Páginas139-144
Número de páginas6
DOI
EstadoPublicada - 1 feb 2017
Publicado de forma externa
EventoProceedings - 2016 5th Brazilian Conference on Intelligent Systems, BRACIS 2016 -
Duración: 1 feb 2017 → …

Conferencia

ConferenciaProceedings - 2016 5th Brazilian Conference on Intelligent Systems, BRACIS 2016
Período1/02/17 → …

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    Sguerra, B. M., & Cozman, F. G. (2017). Image Classification Using Sum-Product Networks for Autonomous Flight of Micro Aerial Vehicles. 139-144. Papel presentado en Proceedings - 2016 5th Brazilian Conference on Intelligent Systems, BRACIS 2016, . https://doi.org/10.1109/BRACIS.2016.035