使用因子化3向限制玻尔兹曼机的背景减除
Background subtraction using the factored 3-way restricted Boltzmann machines
摘要：
本文提出了一种基于连续感觉输入的三维模型重建方法。机器人可以使用各种传感器从现实世界中获取非常大的数据。然而，感官输入通常是太嘈杂和高维数据。当机器人试图构建3D模型时，机器人使用这样的原始数据进行处理是非常困难和耗时的。因此，需要一种方法，可以从这些感官输入中提取有用的信息。为了解决这个问题，我们的方法利用对象语义层次（OSH）的概念。与以往的工作，使用这种层次结构的框架，我们提取的运动信息，而不是应用经典的计算机视觉方法使用深度信念网络技术。我们已经训练了两个大的随机点图像集（10，000），它们分别被平移和旋转，并成功地提取了几个解释平移和旋转运动的基础。基于这种平移和旋转基础，使用对象语义层次结构进行背景减除已经成为可能。
英文摘要：
In this paper, we proposed a method for reconstructing the 3D model based on continuous sensory input. The robot can draw on extremely large data from the real world using various sensors. However, the sensory inputs are usually too noisy and high-dimensional data. It is very difficult and time consuming for robot to process using such raw data when the robot tries to construct 3D model. Hence, there needs to be a method that can extract useful information from such sensory inputs. To address this problem our method utilizes the concept of Object Semantic Hierarchy (OSH). Different from the previous work that used this hierarchy framework, we extract the motion information using the Deep Belief Network technique instead of applying classical computer vision approaches. We have trained on two large sets of random dot images (10,000) which are translated and rotated, respectively, and have successfully extracted several bases that explain the translation and rotation motion. Based on this translation and rotation bases, background subtraction have become possible using Object Semantic Hierarchy.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
