Using fourier-based shape alignment to add geometric prior to snakes

Abstract

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.

Segmentation of Tomatoes in Open Field Images with Shape and Temporal Constraints

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Feasibility of level-set analysis of enface OCT retinal images in diabetic retinopathy

“…These approaches either use specific shapes known a priori, or the shape parameters are obtained from available training data [17][18][19][20][21][22]. Fourier descriptors are a highly effective and compact means of representing object shape and thus make an ideal candidate for use as a shape prior in the level-set formulation [23][24][25][26]. In this work, we focus our study on the description of shape priors using Fourier descriptors to simulate pathologies occurring in the human retina.…”

Shape-based Segmentation of Tomatoes for Agriculture Monitoring

“…In our case, the region information is not significant due to the presence of leaves (occlusions) and other tomatoes of similar intensity profile. In (Charmi et al, 2009), Fourier descriptors are used in order to align the active contour with the reference curve of suitable shape and orientation. In our work, the tomato in each image is assumed to have an ellipse shape.…”