Abstract
While bottom-up approaches to object recognition are simple to design and implement, they do not yield the same performance as top-down approaches. On the other hand, it is not trivial to obtain a moderate number of plausible hypotheses to be efficiently verified by top-down approaches. To address these shortcomings, we propose a hybrid top-down bottom-up approach to object recognition where a bottom-up procedure that generates a set of hypothesis based on data is combined with a top-down process for evaluating those hypotheses. We use the recognition of rectangular cuboid shaped objects from 3D point cloud data as a benchmark problem for our research. Results obtained using this approach demonstrate promising recognition performances.
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Acknowledgments
This work has been supported by the “Fundação para a Ciência e Tecnologia” (Portuguese Foundation for Science and Technology) under grant agreements SFRH/BPD/109651/2015 and National Funds within projects UID/EEA/50014/2013 and UID/CEC/00127/2013. This work was also financed by the ERDF “European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation - COMPETE 2020 Programme within project POCI-01-0145-FEDER-006961 and project ” NORTE -01 -0145 -FEDER- 000020”, financed by the North Portugal Regional Operational Programme (NORTE 2020, under the PORTUGAL 2020 Partnership Agreement). Finally, this work was also funded by the European Union’s Seventh Framework Programme under grant n\(^{\circ }\) 610917 (STAMINA).
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Arrais, R., Oliveira, M., Toscano, C., Veiga, G. (2016). A Hybrid Top-Down Bottom-Up Approach for the Detection of Cuboid Shaped Objects. In: Campilho, A., Karray, F. (eds) Image Analysis and Recognition. ICIAR 2016. Lecture Notes in Computer Science(), vol 9730. Springer, Cham. https://doi.org/10.1007/978-3-319-41501-7_57
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DOI: https://doi.org/10.1007/978-3-319-41501-7_57
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