Title :
3D landmark model discovery from a registered set of organic shapes
Author :
Creusot, Clement ; Pears, Nick ; Austin, Jim
Author_Institution :
Dept. of Comput. Sci., Univ. of York, York, UK
Abstract :
We present a machine learning framework that automatically generates a model set of landmarks for some class of registered 3D objects: here we use human faces. The aim is to replace heuristically-designed landmark models by something that is learned from training data. The value of this automatically generated model is an expected improvement in robustness and precision of learning-based 3D landmarking systems. Simultaneously, our framework outputs optimal detectors, derived from a prescribed pool of surface descriptors, for each landmark in the model. The model and detectors can then be used as key components of a landmark-localization system for the set of meshes belonging to that object class. Automatic models have some intrinsic advantages; for example, the fact that repetitive shapes are automatically detected and that local surface shapes are ordered by their degree of saliency in a quantitative way. We compare our automatically generated face landmark model with a manually designed model, employed in existing literature.
Keywords :
image registration; learning (artificial intelligence); shape recognition; 3D landmark model discovery; 3D object registration; heuristically designed landmark model; human faces; landmark localization system; machine learning framework; optimal detectors; organic shapes; registered set; Computational modeling; Detectors; Humans; Measurement; Shape; Solid modeling; Training;
Conference_Titel :
Computer Vision and Pattern Recognition Workshops (CVPRW), 2012 IEEE Computer Society Conference on
Conference_Location :
Providence, RI
Print_ISBN :
978-1-4673-1611-8
Electronic_ISBN :
2160-7508
DOI :
10.1109/CVPRW.2012.6238915