A n important issue in developing a Model-Based Vision approach is the specification of features that are -(a) invariant t o viewing and scene conditions, and also -(b) 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 considers not just surface reflection and surface geometry, but it also takes into account internal object composition and state which affect images sensed in the non-visible spectrum. This new type of invariance is called Thermophysical Invariance. 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.

An important issue in developing a model-based vision system is the speci cation of features that are-(a) invariant to viewing and scene conditions, and also-(b) speci c, i.e., the feature must have di erent values for di erent classes of objects. We formulate a new approach for establishing invariant features. Our approach is unique in the eld since it considers not just surface re ection and surface geometry in the speci cation of invariant features, but it also takes into account internal object composition and state which a ect images sensed in the non-visible spectrum. A new type of invariance called Thermophysical Invariance is de ned. Features are de ned 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.

We previously formulated a new approach for computing invariant features from infrared (IR) images. That 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 thermal state that affect images sensed in the nonvisible spectrum. In this paper, we extend the thermophysical algebraic invariance (TAI) formulation for the interpretation of uncalibrated infrared imagery and further reduce the information that is required to be known about the environment. Features are defined such that they are functions of only the thermophysical properties of the imaged objects. In addition, we show that the distribution of the TAI features can be accurately modeled by symmetric alpha-stable models. This approach is shown to yield robust classifier performance. Results on ground truth data and real infrared imagery are presented. The application of this scheme for site change detection is discussed.

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