Robust Integration of Thermal and Visual Imagery for OutdoorScene Analysis

这位调查者过去的研究建立了一种分析户外场景的新方法。结合热图像和视觉图像来提取内部对象属性，作为区域标记的物理意义特征。这项新的研究提出了一种用于此目的的能量交换模型的改进公式。能量交换模型被表示为线性回归模型。统计稳健性技术将被用于提取内部对象属性的可靠估计，例如热容。这些技术允许可靠的估计，尽管几乎50%的数据由于图像配准错误和分割错误而被任意破坏。统计稳健性技术已被证明在许多重要的计算机视觉问题中是有益的，如姿态估计、直线提取、图像结构计算和曲面拟合。这项拟议的研究将探索另一个重要问题的统计稳健方法，即传感器融合。对于所选择的传感器融合任务，将探索稳健算法的计算特性。新的公式还允许迭代改进表面参数(如热发射率)，这些参数在以前的模型公式中假定为已知。因此，新方法提高了内部属性值估计的精度，并提供了关于曲面参数值的新信息。因此，该方法为目标分类提供了更多稳定的特征。

Past research by this investigator established a new method for analyzing outdoor scenes. Thermal and visual imagery was combined to extract internal object properties that serve as physically meaningful features for region labeling. This new research presents an improved formulation of the energy exchange model used for this purpose. The energy exchange model is formulated as a linear regression model. Statistically robust techniques will be used to extract reliable estimates of internal object properties such as thermal capacitance. These techniques allow reliable estimates in spite of almost 50% of the data being arbitrarily corrupted due to misregistration of images and segmentation errors. Statistically robust techniques have been shown to be beneficial in many important computer vision problems such as pose estimation, straight-line extraction, computation of image structure, and surface fitting. The proposed research will explore statistical robust approaches to another important problem, i.e., sensor fusion. The computational characteristics of the robust algorithms will be explored for the chosen sensor fusion task. The new formulation also allows iterative refinement of surface parameters (such as thermal emissivity) which were assumed known in the previous formulation of the model. The new approach thus improves the accuracy of the internal property value estimates and also provides new information regarding surface parameter values. The method thus provides a greater number of stable features for object classification.