Abstract. Active vision systems, and especially foveated vision systems, depend on efficient attentional mechanisms. We propose that machine visual attention should consist of both high-level, context-dependent components, and low-level, context free components. As a basis for the context-free component, we present an attention operator based on the intuitive notion of symmetry, which generalized many of the existing methods of detecting regions of interest. It is a low-level operator that can be applied successfully without a priori knowledge of the world. The resulting symmetry edge map can be applied in various low, intermediate-and high-level tasks, such as extraction of interest points, grouping, and object recognition. In particular, we have implemented an algorithm that locates interest points in real time, and can be incorporated in active and purposive vision systems. The results agree with some psychophysical findings concerning symmetry as well as evidence concerning selection of fixation points. We demonstrate the performance of the transform on natural, cluttered images.

No abstract

Transformations based on radial basis junctions have proven to be a powerful tool in image warping. In the present work we decompose these transformations into linear and radial terms, and show examples where such a decomposition is advantageous. Locally supported basis functions are introduced. Several applications are demonstrated, and a comparison with other warping techniques is carried out. Finally, some fine points of image warping are discussed.

Locating facial features is crucial for various face recognition schemes. We suggest a robust facial feature detector based on a generalized symmetry interest operator. No special tuning is required af the face occupies 15-60% of the image. The operator was tested on a large face data base with a success rate of over 95%.

No abstract