Shape modeling with front propagation: A level set approach

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Shape modeling with front propagation: A level set approach (1995)

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by Ravikanth Malladi , James A. Sethian , Baba C. Vemuri

Venue:	IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
Citations:	808 - 20 self

BibTeX

@ARTICLE{Malladi95shapemodeling,
    author = {Ravikanth Malladi and James A. Sethian and Baba C. Vemuri},
    title = {Shape modeling with front propagation: A level set approach},
    journal = {IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE},
    year = {1995},
    volume = {17},
    pages = {158--175}
}

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Abstract

Shape modeling is an important constituent of computer vision as well as computer graphics research. Shape models aid the tasks of object representation and recognition. This paper presents a new approach to shape modeling which retains some of the attractive features of existing methods and over-comes some of their limitations. Our techniques can be applied to model arbitrarily complex shapes, which include shapes with significant protrusions, and to situations where no a priori assumption about the object’s topology is made. A single instance of our model, when presented with an image having more than one object of interest, has the ability to split freely to represent each object. This method is based on the ideas developed by Osher and Sethian to model propagating solidhiquid interfaces with curvature-dependent speeds. The interface (front) is a closed, nonintersecting, hypersurface flowing along its gradient field with constant speed or a speed that depends on the curvature. It is moved by solving a “Hamilton-Jacob? ’ type equation written for a function in which the interface is a particular level set. A speed term synthesizpd from the image is used to stop the interface in the vicinity of object boundaries. The resulting equation of motion is solved by employing entropy-satisfying upwind finite difference schemes. We present a variety of ways of computing evolving front, including narrow bands, reinitializations, and different stopping criteria. The efficacy of the scheme is demonstrated with numerical experiments on some synthesized images and some low contrast medical images.

Keyphrases

front propagation computer graphic research new approach resulting equation type equation low contrast medical image narrow band curva-ture-dependent speed index term shape modeling computer vision priori as-sumption complex shape object representation solidhiquid interface object boundary interface mo-tion abstract shape modeling shape model significant protrusion entropy-satisfying upwind finite difference scheme single instance speed term synthesizpd con-stant speed numerical experiment attractive feature shape recovery object topology level set hyperbolic conservation law important constituent gradient field particular level

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