We present two estimation methods for camera rotation from two images obtained by the active camera before and after rotation. Based on the representation of the projected rotation group, quasi moment features are constructed. Camera rotation can be estimated by applying the singular value decomposition (SVD) or Newton's method to tensor quasi moment features. In both cases, we can estimate 3D rotation of the active camera from only two projected images. We also give some experiments for the estimation of the actual active camera rotation to show the effectiveness of these methods.

This paper presents Dynamic Attention Map by Ising model for face detection. In general, a face detector can not know where faces there are and how many faces there are in advance. Therefore, the face detector must search the whole regions on the image and requires much computational time. To speed up the search, the information obtained at previous search points should be used effectively. In order to use the likelihood of face obtained at previous search points effectively, Ising model is adopted to face detection. Ising model has the two-state spins; "up" and "down". The state of a spin is updated by depending on the neighboring spins and an external magnetic field. Ising spins are assigned to "face" and "non-face" states of face detection. In addition, the measured likelihood of face is integrated into the energy function of Ising model as the external magnetic field. It is confirmed that face candidates would be reduced effectively by spin flip dynamics. To improve the search performance further, the single level Ising search method is extended to the multilevel Ising search. The interactions between two layers which are characterized by the renormalization group method is used to reduce the face candidates. The effectiveness of the multilevel Ising search method is also confirmed by the comparison with the single level Ising search method.