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http://cimat.repositorioinstitucional.mx/jspui/handle/1008/608| Convex Quadratic Programming for Image Segmentation | |
| MARIANO JOSE JUAN RIVERA MERAZ | |
| Acceso Abierto | |
| Atribución-NoComercial | |
| Segmentación de Imágenes | |
| Abstract. A quadratic programming formulation for multiclass image segmentation is investigated. It is proved that, in the con- vex case, the global minima of Quadratic Markov Measure Field (QMMF) models holds the non-negativity constraint. This allows one to design e ffi cient optimization algorithms. We also proposed a (free parameter) inter–pixel a ffi nity measure more related with the classes memberships than with color or gray gradient based standard methods. Moreover, it is introduced a formulation for computing the pixel likelihoods by taking into account local con- text and texture properties. We demonstrate the QMMFs capabil- ities by experiments and numerical comparisons with interactive two-class segmentation as well as in the simultaneous estimation of segmentation and (parametric and non-parametric) generative models. | |
| Centro de Investigación en Matemáticas AC | |
| 09-02-2009 | |
| Reporte | |
| Inglés | |
| Investigadores | |
| LENGUAJES ALGORÍTMICOS | |
| Versión publicada | |
| publishedVersion - Versión publicada | |
| Aparece en las colecciones: | Reportes Técnicos - Ciencias de la Computación |
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| Fichero | Tamaño | Formato | |
|---|---|---|---|
| I-09-01.pdf | 1.22 MB | Adobe PDF | Visualizar/Abrir |