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文件名称:Bayesian Tactile Face
文件大小:1.65MB
文件格式:PDF
更新时间:2012-06-08 08:26:37
Face recognition
Computer users with visual impairment cannot access
the rich graphical contents in print or digital media unless
relying on visual-to-tactile conversion, which is done
primarily by human specialists. Automated approaches to
this conversion are an emerging research field, in which
currently only simple graphics such as diagrams are
handled. This paper proposes a systematic method for
automatically converting a human portrait image into its
tactile form. We model the face based on deformable Active
Shape Model (ASM)[4], which is enriched by local
appearance models in terms of gradient profiles along the
shape. The generic face model including the appearance
components is learnt from a set of training face images.
Given a new portrait image, the prior model is updated
through Bayesian inference. To facilitate the incorporation
of a pose-dependent appearance model, we propose a
statistical sampling scheme for the inference task.
Furthermore, to compensate for the simplicity of the face
model, edge segments of a given image are used to enrich
the basic face model in generating the final tactile printout.
Experiments are designed to evaluate the performance of
the proposed method.