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Collective-reward based approach for detection of semi-transparent objects in single images

机译:基于集体奖励的单幅图像中半透明物体检测方法

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Most computer and robot vision algorithms, be it for object detection, recognition, or reconstruction, are designed for opaque objects. Non-opaque objects have received less attention, although various special cases have been the subject of research efforts, especially the case of specular objects. The main objective of this paper is to provide a research work in the case of semi-transparent objects, i.e. objects that are transparent but also reflect light, typically objects made of glass. They are rather omnipresent in manmade environments (especially, windows and doors). Detection of these objects provides important information that can be used in a robot's navigational strategies such as obstacle avoidance, detection of oil/water spills on the floor, localization, etc. In order to achieve the detection of semi-transparent objects we developed a novel approach using a collective-reward based technique on an image captured by an uncalibrated camera. Several experiments were conducted over different scenarios to test the efficacy of the algorithm.
机译:大多数用于对象检测,识别或重建的计算机和机器人视觉算法都是为不透明的对象设计的。非透明物体受到的关注较少,尽管各种特殊情况已成为研究工作的主题,尤其是镜面物体。本文的主要目的是在半透明物体(即透明但又反射光的物体)的情况下提供研究工作,通常是玻璃制成的物体。它们在人造环境(尤其是门窗)中无处不在。对这些物体的检测提供了可用于机器人导航策略的重要信息,例如避开障碍物,检测地板上的油/水溢出,定位等。为实现对半透明物体的检测,我们开发了一种新型该方法对未经校准的相机捕获的图像使用基于集体奖励的技术。在不同情况下进行了几次实验,以测试算法的有效性。

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