Statistical tuning of Adaptive-Weight Depth Map Algorithm
In depth map generation, the settings of the algorithm parameters to yield an accurate disparity estimation are usually chosen empirically or based on unplanned experiments -- A systematic statistical approach including classical and exploratory data analyses on over 14000 images to measure the rela...
- Autores:
-
Hoyos, Alejandro
Congote, John
Barandiaran, Iñigo
Acosta, Diego
Ruíz, Óscar
- Tipo de recurso:
- Fecha de publicación:
- 2011
- Institución:
- Universidad EAFIT
- Repositorio:
- Repositorio EAFIT
- Idioma:
- eng
- OAI Identifier:
- oai:repository.eafit.edu.co:10784/9726
- Acceso en línea:
- http://hdl.handle.net/10784/9726
- Palabra clave:
- PROGRAMACIÓN HEURÍSTICA
PROCESAMIENTO DE IMÁGENES
ANÁLISIS MULTIVARIANTE
ANÁLISIS DE REGRESIÓN
ESTIMACIÓN DE PARÁMETROS
DISEÑO EXPERIMENTAL DE FACTORES
Heuristic programming
Image processing
Multivariate analysis
Regression analysis
Parameter estimation
Factorial experiments designs
Reconstrucción de la profundidad
Mapas de profundidad
Distancia Euclidiana
Visión estéreo
- Rights
- License
- Acceso cerrado
Summary: | In depth map generation, the settings of the algorithm parameters to yield an accurate disparity estimation are usually chosen empirically or based on unplanned experiments -- A systematic statistical approach including classical and exploratory data analyses on over 14000 images to measure the relative influence of the parameters allows their tuning based on the number of bad pixels -- Our approach is systematic in the sense that the heuristics used for parameter tuning are supported by formal statistical methods -- The implemented methodology improves the performance of dense depth map algorithms -- As a result of the statistical based tuning, the algorithm improves from 16.78% to 14.48% bad pixels rising 7 spots as per the Middlebury Stereo Evaluation Ranking Table -- The performance is measured based on the distance of the algorithm results vs. the Ground Truth by Middlebury -- Future work aims to achieve the tuning by using signicantly smaller data sets on fractional factorial and surface-response designs of experiments |
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