A method employing independent component analysis (ICA) to measure the position of a camera and the pose of an object from images in the presence of occlusion is presented. ICA is used to provide a low-dimensional representation of a set of images taken throughout a range of camera positions and object poses. Using the low-dimensional independent component subspace arbitrary camera locations or object poses can then be determined. The ICA technique is compared with principal component analysis (PCA) in the presence of varying degrees of occlusion. Occlusions of translation, pan and pose images were experimentally applied to provide a demonstration of the performance of this method. The independent component subspace is shown to provide more accurate position and pose information than PCA.