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Shape from defocus in computer vision.

Favaro, Paolo.

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Shape from defocus in computer vision.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Shape from defocus in computer vision./
作者:	Favaro, Paolo.
面頁冊數:	126 p.
附註:	Source: Dissertation Abstracts International, Volume: 64-09, Section: B, page: 4454.
Contained By:	Dissertation Abstracts International64-09B.
標題:	Computer Science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3105949

Shape from defocus in computer vision.
Favaro, Paolo.

Shape from defocus in computer vision. - 126 p.

Source: Dissertation Abstracts International, Volume: 64-09, Section: B, page: 4454.

Thesis (D.Sc.)--Washington University, 2003.

Shape from defocus is the problem of reconstructing the three-dimensional (3D) geometry of a scene from a collection of blurred images captured by a finite aperture lens. The geometry of the scene is described by a depth map, that is the graph of a continuous function with the image plane as domain and the positive reals as co-domain.Subjects--Topical Terms:

626642
Computer Science.

Shape from defocus in computer vision.
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300 $a 126 p.
500 $a Source: Dissertation Abstracts International, Volume: 64-09, Section: B, page: 4454.
500 $a Adviser: Stefano Soatto.
502 $a Thesis (D.Sc.)--Washington University, 2003.
520 $a Shape from defocus is the problem of reconstructing the three-dimensional (3D) geometry of a scene from a collection of blurred images captured by a finite aperture lens. The geometry of the scene is described by a depth map, that is the graph of a continuous function with the image plane as domain and the positive reals as co-domain.
520 $a This problem is ill-posed since the solution is not unique given the data (the collection of defocused images). This motivates us to first study the conditions under which shape reconstruction is possible and to what degree.
520 $a Then, we propose a number of algorithms to optimally reconstruct 3D shape from blurred images. We do so by exploiting different aspects of the structure of the problem. The solutions we propose can be divided into four groups. In the first we exploit the linearity of the problem with respect to some unknowns to arrive at a very general solution which only entails a minimization with respect to shape. In the second, we exploit the nonnegativity of the unknowns to derive a provably convergent minimization algorithm. In the third group, we cast the problem in the context of partial differential equations. We formulate the problem of inferring shape from blurred images as that of inferring the diffusion coefficient of a parabolic differential equation. These solutions are based on the common assumption that the scene is made of a single surface (a depth map). This assumption is often violated in real images, especially when we are in the presence of occlusions between different objects in the scene. We address this issue in the fourth group.
590 $a School code: 0252.
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650 4 $a Artificial Intelligence. $3 769149
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