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Exploiting space-time statistics of ...

Dedeoglu, Goksel.

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Exploiting space-time statistics of videos for face "Hallucination".

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Exploiting space-time statistics of videos for face "Hallucination"./
作者:	Dedeoglu, Goksel.
面頁冊數:	126 p.
附註:	Adviser: Takeo Kanade.
Contained By:	Dissertation Abstracts International68-06B.
標題:	Artificial Intelligence. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3270860
ISBN:	9780549086611

Exploiting space-time statistics of videos for face "Hallucination".
Dedeoglu, Goksel.

Exploiting space-time statistics of videos for face "Hallucination". - 126 p.

Adviser: Takeo Kanade.

Thesis (Ph.D.)--Carnegie Mellon University, 2007.

Face "Hallucination" aims to recover high quality, high-resolution images of human faces from low-resolution, blurred, and degraded images or video. This thesis presents person-specific solutions to this problem through careful exploitation of space (image) and space-time (video) models. The results demonstrate accurate restoration of facial details, with resolution enhancements upto a scaling factor of 16.

ISBN: 9780549086611Subjects--Topical Terms:

769149
Artificial Intelligence.

Exploiting space-time statistics of videos for face "Hallucination".
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245 1 0 $a Exploiting space-time statistics of videos for face "Hallucination".
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502 $a Thesis (Ph.D.)--Carnegie Mellon University, 2007.
520 $a Face "Hallucination" aims to recover high quality, high-resolution images of human faces from low-resolution, blurred, and degraded images or video. This thesis presents person-specific solutions to this problem through careful exploitation of space (image) and space-time (video) models. The results demonstrate accurate restoration of facial details, with resolution enhancements upto a scaling factor of 16.
520 $a The algorithms proposed in this thesis follow the analysis-by-synthesis paradigm; they explain the observed (low-resolution) data by fitting a (high-resolution) model. In this context, the first contribution is the discovery of a scaling-induced bias that plagues most model-to-image (or image-to-image) fitting algorithms. It was found that models and observations should be treated asymmetrically , both to formulate an unbiased objective function and to derive an accurate optimization algorithm. This asymmetry is most relevant to Face Hallucination: when applied to the popular Active Appearance Model, it leads to a novel face tracking and reconstruction algorithm that is significantly more accurate than state-of-the-art methods. The analysis also reveals the inherent trade-off between computational efficiency and estimation accuracy in low-resolution regimes.
520 $a The second contribution is a statistical generative model of face videos. By treating a video as a composition of space-time patches, this model efficiently encodes the temporal dynamics of complex visual phenomena such as eye-blinks and the occlusion or appearance of teeth. The same representation is also used to define a data-driven prior on a three-dimensional Markov Random Field in space and time. Experimental results demonstrate that temporal representation and reasoning about facial expressions improves robustness by regularizing the Face Hallucination problem.
520 $a The final contribution is an approximate compensation scheme against illumination effects. It is observed that distinct illumination subspaces of a face (each coming from a different pose and expression) still exhibit similar variation with respect to illumination. This motivates augmenting the video model with a low-dimensional illumination subspace, whose parameters are estimated jointly with high-resolution face details. Successful Face Hallucinations beyond the lighting conditions of the training videos are reported.
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