This work proposes a dynamic-shape-prior guided snake model (DSP G-snake) that is designed to improve the overall stability of the point-based snake model. The dynamic shape prior is first proposed for snakes, that efficiently unifies different types of high-level priors into a new force term. To be specific, a global-topology regularity is first introduced that settles the inherent self-intersection problem with snakes. The problem that a snake's snaxels tend to unevenly distribute along the contour is also handled, leading to good parameterization. Unlike existing methods that employ learning templates or commonly enforce hard priors, the dynamic-template scheme strongly respects the deformation flexibility of the model, while retaining a decent global topology for the snake. It is verified by experiments that the proposed algorithm can effectively prevent snakes from self-crossing, or automatically untie an already selfintersected contour. In addition, the proposed model is combined with existing forces and applied to the very challenging task of tracking dense biological cell populations. The DSP G-snake model has enabled an improvement of up to 30% in tracking accuracy with respect to regular model-based approaches. Through experiments on real cellular datasets, with highly dense populations and relatively large displacements, it is confirmed that the proposed approach has enabled superior performance, in comparison to modern active-contour competitors as well as state-of-the-art cell tracking frameworks.

Optical flow (OF) estimation needs spatial coherence regularisation, due to local image noise and the well-known aperture problem. More recently, OF local-region regularisation has been extended to larger or non-local region of regularisation to further deal with the aperture problem. After careful literature review, it has been determined that the criteria used for deciding the degree of motion coherence can be further improved. For this reason, we propose an oriented geodesic distance based motion regularisation scheme. The proposed approach is particular useful in reducing errors in estimating motions along object boundaries, and recovering motions for nearby objects with similar appearance. Experiment results, compared to leading-edge non-local regularisation schemes, have confirmed the superior performance of the proposed approach.