Application of Image inpainting ranges from object removal, photo restoration, scratch removal, and so on. In this thesis, we will propose a modified multi-scale method and learning-based method using artificial neural networks for image inpainting. Multi-scale inpainting method combines image segmentation, contour estimation, and exemplar-based inpainting. The main goal of image segmentation is to separate image to several homogeneous regions outside the target region. After image segmentation, we use contour estimation to estimate curves inside the target region to partition the whole image into several different regions. Then we fill those different regions inside the target region separately by exemplar-based inpainting method. The exemplar-based technique fills the target region via the texture synthesis and filling order of exemplary patches. Exemplary patches are found near target region and the filling order is determined by isophote and densities of exemplary patches. Learning-based inpainting is a novel technique. This technique combines machine learning and the concept of filling order. We use artificial neural networks to learn the structure and texture surrounding the target region. After training, we fill the target region according to the filling order. From our simulation results, very good results can be obtained for removing large-size objects by using the proposed multi-scale method, and for removing medium-size objects of gray images.

Univ., Diss. u.d.T.: Lewis, Rhodri: John Wilkins's 'Essay' (1668) and the context of seventeenth-century artificial languages in England--Oxford, 2003. / Literaturverz. S. 233 - 256.

Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2009. / Source: Dissertation Abstracts International, Volume: 70-06, Section: B, page: 3593. Adviser: Jiawei Han. Includes bibliographical references (leaves 93-99) Available on microfilm from Pro Quest Information and Learning.

Thesis (Ph. D.)--Syracuse University, 2009. / "Publication number: AAT 3381974 ."

Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2009. / Source: Dissertation Abstracts International, Volume: 70-06, Section: B, page: 3605. Adviser: Gerald DeJong. Includes bibliographical references (leaves 107-112) Available on microfilm from Pro Quest Information and Learning.

Thesis (Ph. D.)--State University of New York at Buffalo, 1995. / "December 1995." Includes bibliographical references (p. 127-142). Also available in print.

Thesis (M.S. in Astronautical Engineering)--Naval Postgraduate School, June 2004. / Thesis advisor(s): Brij Agrawal, Marcello Romano. Includes bibliographical references (p. 101-102). Also available online.

Thesis (M.S. in Electrical Engineering)--Naval Postgraduate School, June 2004. / Thesis advisor(s): Sherif N. Michael. Includes bibliographical references (p. 77-79). Also available online.

Thesis (M.S. in Software Engineering)--Naval Postgraduate School, June 2004. / Thesis advisor(s): William Ray. Includes bibliographical references (p. 265-266). Also available online.

Thesis (Ph.D.)--Brown University, 2008. / Advisor : Thomas Hofmann. Includes bibliographical references (leaves 111-118).