MIT Libraries logoDSpace@MIT

MIT
View Item 
  • DSpace@MIT Home
  • Computer Science and Artificial Intelligence Lab (CSAIL)
  • Artificial Intelligence Lab Publications
  • AI Memos (1959 - 2004)
  • View Item
  • DSpace@MIT Home
  • Computer Science and Artificial Intelligence Lab (CSAIL)
  • Artificial Intelligence Lab Publications
  • AI Memos (1959 - 2004)
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

Ill-Posed Problems in Early Vision

Author(s)
Bertero, Mario; Poggio, Tomaso; Torre, Vincent
Thumbnail
DownloadAIM-924.ps (4.249Mb)
Additional downloads
AIM-924.pdf (3.327Mb)
Metadata
Show full item record
Abstract
The first processing stage in computational vision, also called early vision, consists in decoding 2D images in terms of properties of 3D surfaces. Early vision includes problems such as the recovery of motion and optical flow, shape from shading, surface interpolation, and edge detection. These are inverse problems, which are often ill-posed or ill-conditioned. We review here the relevant mathematical results on ill-posed and ill-conditioned problems and introduce the formal aspects of regularization theory in the linear and non-linear case. More general stochastic regularization methods are also introduced. Specific topics in early vision and their regularization are then analyzed rigorously, characterizing existence, uniqueness, and stability of solutions.
Date issued
1987-05-01
URI
http://hdl.handle.net/1721.1/5596
Other identifiers
AIM-924
Series/Report no.
AIM-924
Keywords
computational vision, regularization theory, sinverse problems, ill-posed problems

Collections
  • AI Memos (1959 - 2004)

Browse

All of DSpaceCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects

My Account

Login

Statistics

OA StatisticsStatistics by CountryStatistics by Department
MIT Libraries
PrivacyPermissionsAccessibilityContact us
MIT
Content created by the MIT Libraries, CC BY-NC unless otherwise noted. Notify us about copyright concerns.