Abstract
In this article we present a massively parallel object recognition system designed to operate on-line, processing data acquired by indoor mobile robots equipped with 3D cameras. Inspired by the properties of the mammalian visual cortex, the proposed method incorporates a learned, selective use of features for the recognition of specific objects of interest, as well as a pre-processing stage of simultaneous localization and mapping (featuring Kinect Fusion) and a new parallel, heuristic scene segmentation algorithm. The benefits of applying class-specific feature spaces are demonstrated in an experiment carried using indoor scenes containing multiple common household objects.
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Harasymowicz-Boggio, B., Chechliński, Ł., Siemiątkowska, B. (2015). Nature-Inspired, Parallel Object Recognition. In: Szewczyk, R., Zieliński, C., Kaliczyńska, M. (eds) Progress in Automation, Robotics and Measuring Techniques. ICA 2015. Advances in Intelligent Systems and Computing, vol 350. Springer, Cham. https://doi.org/10.1007/978-3-319-15796-2_6
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DOI: https://doi.org/10.1007/978-3-319-15796-2_6
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