Head pose estimation is one of the essential tasks in computer vision, which predicts the Euler angles of the head in an image. In recent years, CNN-based methods for head pose estimation have achieved excellent performance. Their training relies on RGB images providing facial landmarks or depth images from RGBD cameras. However, labeling facial landmarks is complex for large angular head poses in RGB images, and RGBD cameras are unsuitable for outdoor scenes. We propose a simple and effective annotation method for the head pose in RGB images. The novelty method uses a 3D virtual human head to simulate the head pose in the RGB image. The Euler angle can be calculated from the change in coordinates of the 3D virtual head. We then create a dataset using our annotation method: 2DHeadPose dataset, which contains a rich set of attributes, dimensions, and angles. Finally, we propose Gaussian label smoothing to suppress annotation noises and reflect inter-class relationships. A baseline approach is established using Gaussian label smoothing. Experiments demonstrate that our annotation method, datasets, and Gaussian label smoothing are very effective. Our baseline approach surpasses most current state-of-the-art methods. The annotation tool, dataset, and source code are publicly available at https://github.com/youngnuaa/2DHeadPose.

This paper proposes a numerical algorithm for image registration using energy minimization and nonlinear elasticity regularization. Application to the registration of gene expression data to a neuroanatomical mouse atlas in two dimensions is shown. We apply a nonlinear elasticity regularization to allow larger and smoother deformations, and further enforce optimality constraints on the landmark points distance for better feature matching. To overcome the difficulty of minimizing the nonlinear elasticity functional due to the nonlinearity in the derivatives of the displacement vector field, we introduce a matrix variable to approximate the Jacobian matrix and solve for the simplified Euler-Lagrange equations. By comparison with image registration using linear regularization, experimental results show that the proposed nonlinear elasticity model also needs fewer numerical corrections such as regridding steps for binary image registration, it renders better ground truth, and produces larger mutual information; most importantly, the landmark points distance and L2 dissimilarity measure between the gene expression data and corresponding mouse atlas are smaller compared with the registration model with biharmonic regularization.