國立虎尾科技大學 |

Computing Intrinsic Images (Artificial Intelligence).

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Computing Intrinsic Images (Artificial Intelligence)./
作者:	Aloimonos, John.
面頁冊數:	1 online resource (261 pages)
附註:	Source: Dissertations Abstracts International, Volume: 48-07, Section: B.
Contained By:	Dissertations Abstracts International48-07B.
標題:	Computer science. -
電子資源:	click for full text (PQDT)
ISBN:	9798641522067

Computing Intrinsic Images (Artificial Intelligence).
Aloimonos, John.

Computing Intrinsic Images (Artificial Intelligence). - 1 online resource (261 pages)

Source: Dissertations Abstracts International, Volume: 48-07, Section: B.

Thesis (Ph.D.)--University of Rochester, 1987.

Includes bibliographical references

Low-level modern computer vision is not domain dependent, but concentrates on problems that correspond to identifiable modules in the human visual system. Several theories have been proposed in the literature for the computation of shape from shading, shape from texture, retinal motion from spatiotemporal derivatives of the image intensity function, and the like. The problems with the existing approach are basically the following: (1) The employed assumptions are very strong (they are not present in a large subset of real images), and so most of the algorithms fail when applied to real images. (2) Usually the constraints from the geometry and the physics of the problem are not enough to guarantee uniqueness of the computed parameters. In this case, strong additional assumptions about the world are used, in order to restrict the space of all solutions to a unique value. (3) Even if no assumptions at all are used and the physical constraints are enough to guarantee uniqueness of the computed parameters, then in most cases the resulting algorithms are not robust, in the sense that if there is a slight error in the input (i.e. small amount of noise in the image), this results in a catastrophic error in the output (computed parameters). It turns out that if several available cues are combined, then the above-mentioned problems disappear; the resulting algorithms compute uniquely and robustly the intrinsic parameters (shape, depth, motion, etc.). In this thesis the problem of machine vision is explored from its basics. A low level mathematical theory is presented for the unique and robust computation of intrinsic parameters. The computational aspect of the theory envisages a cooperative highly parallel implementation, bringing in information from five different sources (shading, texture, motion, contour and stereo), to resolve ambiguities and ensure uniqueness and stability of the intrinsic parameters. The problems of shape from texture, shape from shading and motion, visual motion analysis and shape and motion from contour are analyzed in detail.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798641522067Subjects--Topical Terms:

573171
Computer science.
Index Terms--Genre/Form:

554714
Electronic books.

Computing Intrinsic Images (Artificial Intelligence).
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502 $a Thesis (Ph.D.)--University of Rochester, 1987.
504 $a Includes bibliographical references
520 $a Low-level modern computer vision is not domain dependent, but concentrates on problems that correspond to identifiable modules in the human visual system. Several theories have been proposed in the literature for the computation of shape from shading, shape from texture, retinal motion from spatiotemporal derivatives of the image intensity function, and the like. The problems with the existing approach are basically the following: (1) The employed assumptions are very strong (they are not present in a large subset of real images), and so most of the algorithms fail when applied to real images. (2) Usually the constraints from the geometry and the physics of the problem are not enough to guarantee uniqueness of the computed parameters. In this case, strong additional assumptions about the world are used, in order to restrict the space of all solutions to a unique value. (3) Even if no assumptions at all are used and the physical constraints are enough to guarantee uniqueness of the computed parameters, then in most cases the resulting algorithms are not robust, in the sense that if there is a slight error in the input (i.e. small amount of noise in the image), this results in a catastrophic error in the output (computed parameters). It turns out that if several available cues are combined, then the above-mentioned problems disappear; the resulting algorithms compute uniquely and robustly the intrinsic parameters (shape, depth, motion, etc.). In this thesis the problem of machine vision is explored from its basics. A low level mathematical theory is presented for the unique and robust computation of intrinsic parameters. The computational aspect of the theory envisages a cooperative highly parallel implementation, bringing in information from five different sources (shading, texture, motion, contour and stereo), to resolve ambiguities and ensure uniqueness and stability of the intrinsic parameters. The problems of shape from texture, shape from shading and motion, visual motion analysis and shape and motion from contour are analyzed in detail.
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