Robust Integration of Thermal and Visual Imagery for OutdoorScene Analysis

该研究人员过去的研究建立了一种新的方法， 分析户外场景 热成像和视觉成像是 组合以提取内部对象属性， 对区域标记有物理意义的特征。 这个新 研究提出了能量交换的改进公式 模型用于此目的。 能量交换模型是 用线性回归模型表示。 统计稳健 技术将被用来提取可靠的估计内部 对象属性，如热容量。 这些技术 允许可靠的估计，尽管几乎50%的数据 由于图像配准错误而任意损坏， 分割错误。 统计上可靠的技术已经被 在许多重要的计算机视觉问题上都是有益的 诸如姿态估计、直线提取、 图像结构和表面拟合。 拟议的研究将 探索另一个重要的统计方法 问题，即，传感器融合 计算特性 的鲁棒算法将探讨所选的传感器 融合任务 新公式还允许迭代 表面参数（如热发射率）的细化 这是假定已知的，在以前的公式， 模型 因此，新方法提高了 内部财产价值估计，并提供新的 关于表面参数值的信息。 该方法因此 为对象提供更多的稳定功能 分类.

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.