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Robust image completion and masking with application to robotic bin picking
Robotics and Autonomous Systems ( IF 4.3 ) Pub Date : 2020-09-01 , DOI: 10.1016/j.robot.2020.103563
Sukhan Lee , Naeem Ul Islam , Soojin Lee

Abstract Automated image completion and masking have been emerged as a subject of keen interest due to their impact on image modification and interpretation. The current state-of-the-art approaches require a fixed format of missing parts and are ineffective for handling corrupted images. Besides, they focus exclusively on the image completion without taking into consideration the image masking as an inverse process of completion. This paper proposes a deep learning approach to an integrated framework of image completion and masking based on the cross-mapping generative adversarial network or CM-GAN, in short. CM-GAN offers the robustness in image completion under corruptions as well as the capability of synthesizing various masked images with arbitrary mask locations and shapes. In particular, the capability of CM-GAN in image masking is shown to be extended into the removal of unwanted backgrounds in images. We verify the superior performance of CM-GAN for image completion and masking based on extensive experiments. Furthermore, we implement a deep learning based robotic bin picking to demonstrate that the background removal capability of CM-GAN plays a key role for estimating the 3D pose of randomly filed multiple industrial parts in a bin.

中文翻译:

稳健的图像补全和掩蔽,适用于机器人拣选

摘要 自动图像补全和掩蔽由于它们对图像修改和解释的影响而成为一个非常感兴趣的主题。当前最先进的方法需要固定格式的缺失部分,并且对于处理损坏的图像无效。此外,他们只关注图像补全,而没有考虑作为补全的逆过程的图像掩蔽。简而言之,本文提出了一种基于交叉映射生成对抗网络或 CM-GAN 的图像补全和掩蔽集成框架的深度学习方法。CM-GAN 提供了在损坏情况下图像完成的鲁棒性,以及合成具有任意掩码位置和形状的各种掩码图像的能力。特别是,CM-GAN 在图像掩蔽方面的能力被证明可以扩展到去除图像中不需要的背景。我们基于大量实验验证了 CM-GAN 在图像补全和掩蔽方面的优越性能。此外,我们实施了基于深度学习的机器人拣选机器人,以证明 CM-GAN 的背景去除能力在估计垃圾箱中随机归档的多个工业零件的 3D 姿态方面起着关键作用。
更新日期:2020-09-01
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