Automatic image registration is the technique to align images in different coordinate systems to the same coordinate system which has found wide industrial applications for control automation and quality inspection. Focusing on the industrial applications where product models are available and transformations between models and images are global, this thesis presents the research works on two registration problems based on different features and different transformation models. The first image registration problem is a 2D/2D one with a 2D similarity transformation and based on geometric primitives selected from models and extracted from images. Featured-based methods using geometric primitives like point, line segment and circle have been widely studied. This thesis proposes a number of novel registration methods based on elliptic features, which include a point matching algorithm based on local search method for ellipse correspondence search and rough pose estimation, a numerical approach to refine the estimation result by using the non-overlapping area ratio (NAR) of corresponding ellipses and an elliptic are matching algorithm based on integral of squared distances (JSD) between points on corresponding arcs. The major advantage of JSD is that its optimal solution can be obtained analytically, which makes it applicable to efficient elliptic arc correspondence search. The second image registration problem is a 3D/2D one with an orthographic projection transformation and based on silhouette features. A novel algorithm has been developed and presented in this thesis based on a 3D triangular-mesh model, which can be applied to approximate a de facto NURBS model, and images in which silhouette features can be extracted. The algorithm consists of a rough pose estimation process with shape comparison methods and a pose refinement process with 3D/2D iterative closest point (ICP) method. The computer simulation results show that the algorithm can perform very effective and efficient 3D/2D registration.