Abstract
We present a supervised classification model based on a vari- ational approach. This model is devoted to find an optimal partition com- pound of homogeneous classes with regular interfaces. We represent the regions of the image defined by the classes and their interfaces by level set functions, and we define a functional whose minimum is an optimal partition. The coupled Partial Differential Equations (PDE) related to the minimization of the functional are considered through a dynamical scheme. Given an initial interface set (zero level set), the different terms of the PDE’s are governing the motion of interfaces such that, at con- vergence, we get an optimal partition as defined above. Each interface is guided by internal forces (regularity of the interface), and external ones (data term, no vacuum, no regions overlapping). Several experiments were conducted on both synthetic an real images.
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© 1999 Springer-Verlag Berlin Heidelberg
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Samson, C., Blanc-Féraud, L., Zerubia, J., Aubert, G. (1999). A Level Set Model for Image Classification. In: Nielsen, M., Johansen, P., Olsen, O.F., Weickert, J. (eds) Scale-Space Theories in Computer Vision. Scale-Space 1999. Lecture Notes in Computer Science, vol 1682. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48236-9_27
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DOI: https://doi.org/10.1007/3-540-48236-9_27
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