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A Multi-view Images Generation Method for Object Recognition

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Intelligent Robotics and Applications (ICIRA 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10985))

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Abstract

Recently convolutional neural network has achieved great success in the field of object recognition, but it is a hard work to get enough labeled data for training a neural network, especially for novel object instances. In this paper, we address this problem by generating synthetic images in simulation environment. We propose a method that generates a large amount multi-view synthetic images automatically to avoid manual collection and annotation. When applying our method to object recognition in real scenarios, the robot picks up the object first, then gets the object images using the same method of getting training images, which reduces the domain gap between real images and synthetic images. Experiments show that our method can recognize various objects with different poses efficiently.

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Acknowledgments

This work is supported by the National Natural Science Foundation of China (Grant No. 61573333, U1613216) and the Joint Funds of the National Natural Science Foundation of China (Grand No. U1613216).

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Correspondence to Xiaoping Chen .

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Jin, Z., Cui, G., Chen, G., Chen, X. (2018). A Multi-view Images Generation Method for Object Recognition. In: Chen, Z., Mendes, A., Yan, Y., Chen, S. (eds) Intelligent Robotics and Applications. ICIRA 2018. Lecture Notes in Computer Science(), vol 10985. Springer, Cham. https://doi.org/10.1007/978-3-319-97589-4_26

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  • DOI: https://doi.org/10.1007/978-3-319-97589-4_26

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-97588-7

  • Online ISBN: 978-3-319-97589-4

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