Conventional radiography is insensitive for early and accurate estimation of the mal-alignment and wear of knee prostheses. The two-staged (rough and fine) registration of the model-based RSA technique has recently been developed to in vivo estimate the prosthetic pose (i.e, location and orientation). In the literature, rough registration often uses template match or manual adjustment of the roentgen images. Additionally, possible error induced by the nonorthogonality of taking two roentgen images neither examined nor calibrated prior to fine registration. This study developed two RSA methods for automate the estimation of the prosthetic pose and decrease the nonorthogonality-induced error. The predicted results were validated by both simulative and experimental tests and compared with reported findings in the literature. The outcome revealed that the feature-recognized method automates pose estimation and significantly increases the execution efficiency up to about 50 times in comparison with the literature counterparts. Although the nonorthogonal images resulted in undesirable errors, the outline-optimized method can effectively compensate for the induced errors prior to fine registration. The superiority in automation, efficiency, and accuracy demonstrated the clinical practicability of the two proposed methods especially for the numerous fluoroscopic images of dynamic motion.