A novel method of snakes with shape prior is presented in this paper. We propose to add a new force which makes the curve evolve to particular shape corresponding to a template to overcome some well-known problems of snakes. The template is an instance or a sketch of the researched contour without knowing its exact geometric pose in the image. The prior information is introduced through a set of complete and locally stable invariants to Euclidean transformations (translation, rotation and scale factor) computed using Fourier Transform on contours. The method is evaluated with the segmentation of myocardial scintigraphy slices and the tracking of an object in a video sequence.

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In this paper, we present a novel method to incorporate geometric shape prior into region-based active contours. Prior knowledge is obtained from a reference shape. This shape reference is used to define a new energy term obtained through a Fourier-based shape alignment. The new energy is invariant with respect to Euclidean transformations. Experimental results show the ability of the geometric shape prior to constraint an evolving curve to meet target shape and to show the new method benefits on segmentation results in presence of occlusion and noise.

In this paper, we present a new algorithm of snakes with geometric prior. A method of shape alignment using Fourier coefficients is introduced to estimate the Euclidean transformation between the evolving snake and a template of the searched object. This allows the definition of a new field of forces making the evolving snake to have a shape similar to the template one. Furthermore, this strategy can be used to manage several possible templates by computing a shape distance to select the best one at each iteration. The new method also solves some well-known limitations of snakes such as evolution in concave boundaries, and enhances the robustness to noise and partially occluded objects. A series of experimental results is presented to illustrate performances.

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