Título:
The whole and the parts : The MDL principle and the a-contrario framework
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Autor:
Grompone von Gioi, Rafael
Ramírez Paulino, Ignacio
Randall, Gregory
Ramírez Paulino, Ignacio
Randall, Gregory
Tutor:
Tipo de documento:
Preprint
Editor:
Palabras clave:
Model selection
Structure detection
MDL
A-contrario framework
Non accidentalness principle
NFA
Polygonal approximation
Line segment detection
Structure detection
MDL
A-contrario framework
Non accidentalness principle
NFA
Polygonal approximation
Line segment detection
Descriptores:
Año de publicación:
2021
Contenido:
Resumen:
This work explores the connections between the Minimum Description Length (MDL) principle as developed by Rissanen, and the a-contrario framework for structure detection proposed by Desolneux, Moisan and Morel. The MDL principle focuses on the best interpretation for the whole data while the a-contrario approach concentrates on detecting parts of the data with anomalous statistics. Although framed in different theoretical formalisms, we show that both methodologies share many common concepts and tools in their machinery and yield very similar formulations in a number of interesting scenarios ranging from simple toy examples to practical applications such as polygonal approximation of curves and line segment detection in images. We also formulate the conditions under which both approaches are formally equivalent.
Descripción:
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Editorial:
arXiv
EN:
Computer Science (cs.CV-Computer Vision and Pattern Recognition), arXiv:2112.06853, Dec. 2021, pp. 1-32.
Financiadores:
Citación:
Grompone von Gioi, R., Ramírez Paulino, I. y Randall, G. The whole and the parts : The MDL principle and the a-contrario framework [Preprint]. Publicado en : Computer Science (cs.CV-Computer Vision and Pattern Recognition), 2021, pp. 1-32. arXiv:2112.06853.
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Departamento académico:
Procesamiento de Señales
Grupo de investigación:
Tratamiento de Imágenes
Licencia:
Licencia Creative Commons Atribución - No Comercial - Compartir Igual (CC - By-NC-SA 4.0)
Colecciones:
| Ficheros | Descripción | Tamaño | Formato | ||
|---|---|---|---|---|---|
| GRR21.pdf | Preprint | 5.61 MB | Adobe PDF |