In recent years, various shadow detection methods from a single image have
been proposed and used in vision systems; however, most of them are not
appropriate for the robotic applications due to the expensive time complexity.
This paper introduces a fast shadow detection method using a deep learning
framework, with a time cost that is appropriate for robotic applications. In
our solution, we first obtain a shadow prior map with the help of multi-class
support vector machine using statistical features. Then, we use a semantic-
aware patch-level Convolutional Neural Network that efficiently trains on
shadow examples by combining the original image and the shadow prior map.
Experiments on benchmark datasets demonstrate the proposed method significantly
decreases the time complexity of shadow detection, by one or two orders of
magnitude compared with state-of-the-art methods, without losing accuracy.

本研究提出了一种基于深度学习框架的快速阴影检测方法，使用多类支持向量机获取阴影先验图，并使用语义感知的小区域级卷积神经网络对阴影进行训练，实验结果表明该方法显著降低了阴影检测的时间复杂度，并且与最先进的方法相比，不失准确性。

使用打补丁卷积神经网络从单张图像快速检测阴影

Fast Shadow Detection from a Single Image Using a Patched Convolutional  Neural Network

Location estimation is a fundamental sensing task in robotic applications,
where the world is uncertain, and sensors and effectors are noisy. Most systems
make various assumptions about the dependencies between state variables, and
especially about how these dependencies change as a result of actions. Building
on a general framework by Bacchus, Halpern and Levesque for reasoning about
degrees of belief in the situation calculus, and a recent extension to it for
continuous domains, in this paper we illustrate location estimation in the
presence of a rich theory of actions using an example. We also show that while
actions might affect prior distributions in nonstandard ways, suitable
posterior beliefs are nonetheless entailed as a side-effect of the overall
specification.

在不确定的世界中，使用 Bacchus、Halpern 和 Levesque 的框架和连续领域的扩展，以期望推理的方式进行基于状态的位置估计，并揭示了行为理论下的位置估计问题。此外，本文还表明，虽然行动可能以非标准的方式影响先验分布，但总体规范的副作用是产生了适当的后验信念。