Classification of Radar Targets Using Invariant Features

Automatic target recognition ATR using radar commonly relies on modeling a target as a collection of point scattering centers, Features extracted from these scattering centers for input to a target classifier may be constructed that are invariant to translation and rotation, i.e., they are independent of the position and aspect angle of the target in the radar scene. Here an iterative approach for building effective scattering center models is developed, and the shape space of these models is investigated. Experimental results are obtained for three-dimensional scattering centers compressed to nineteen-dimensional feature sets, each consisting of the singular values of the matrix of scattering center locations augmented with the singular values of its second and third order monomial expansions. These feature sets are invariant to translation and rotation and permit the comparison of targets modeled by different numbers of scattering centers. A metric distance metric is used that effectively identifies targets under real world conditions that include noise and obscuration

Model-Based Recognition of 3D Objects from One View

In this work we treat major problems of object recognition which have previously received little attention. Among them are the loss of depth information in the projection from 3D to 2D, and the complexity of finding feature correspondences in general cases. This treatment enables us to recognize objects in difficult real-world situations. It is well known that there are no geometric invariants of a projection from 3D to 2D. However, given some modeling assumptions about the 3D object, such invariants can be found. The modeling assumptions can be either a particular model or a generic assumption about a class of models. Here we deal with both situations. We find invariant connections between a 2D image and a 3D model under general projective projection. We give a geometric interpretation of the method as an invariant model in 3D invariant space, illuminated by invariant light rays, converging to an invariant camera center in the same space. We demonstrate the method on real images. This..