Existing methods for incorporating subspace model constraints in con- tour tracking use only partial information from the measurements and model dis- tribution. We propose a complete fusion formulation for robust contour tracking, optimally resolving uncertainties from heteroscedastic measurement noise, system dynamics, and a subspace model. The resulting non-orthogonal subspace projec- tion is a natural extension of the traditional model constraint using orthogonal projection. We build models for coupled double-contours, and exploit informa- tion from the ground truth initialization through a strong model adaptation. Our framework is applied for tracking in echocardiograms where the noise is het- eroscedastic, each heart has distinct shape, and the relative motions of epi- and endocardial borders reveal crucial diagnostic features. The proposed method sig- nificantly outperforms the traditional shape-space-constrained tracking algorithm. Due to the joint fusion of heteroscedastic uncertainties, the strong model adapta- tion, and the coupled tracking of double-contours, robust performance is observed even on the most challenging cases.