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首页> 外文期刊>IEEE Transactions on Pattern Analysis and Machine Intelligence >Thermophysical algebraic invariants from infrared imagery for object recognition
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Thermophysical algebraic invariants from infrared imagery for object recognition

机译:红外图像的热物理代数不变量用于物体识别

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An important issue in developing a model-based vision system is the specification of features that are invariant to viewing and scene conditions and also specific, i.e., the feature must have different values for different classes of objects. We formulate a new approach for establishing invariant features. Our approach is unique in the field since it considers not just surface reflection and surface geometry in the specification of invariant features, but it also takes into account internal object composition and state which affect images sensed in the nonvisible spectrum. A new type of invariance called thermophysical invariance is defined. Features are defined such that they are functions of only the thermophysical properties of the imaged objects. The approach is based on a physics-based model that is derived from the principle of the conservation of energy applied at the surface of the imaged object.
机译:在开发基于模型的视觉系统时,重要的问题是对观看和场景条件不变的特征的规范,并且这些特征也是特定的,即,对于不同类别的对象,特征必须具有不同的值。我们制定了一种建立不变特征的新方法。我们的方法在本领域中是独一无二的,因为它在不变特征的规范中不仅考虑了表面反射和表面几何形状,而且还考虑了影响不可见光谱中图像的内部对象组成和状态。定义了一种称为热物理不变性的新型不变性。特征被定义为使得它们仅是被成像物体的热物理性质的函数。该方法基于基于物理的模型,该模型是从施加在成像对象表面的能量守恒原理得出的。

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