'Database images' presentation slideshows

Content-based Image Retrieval (CBIR)

Content-based Image Retrieval (CBIR). Searching a large database for images that match a query: What kinds of databases? What kinds of queries? What constitutes a match? How do we make such searches efficient?. Applications. Art Collections e.g. Fine Arts Museum of San Francisco

809 views • 73 slides

Indexing local features

Indexing local features. Wed March 30 Prof. Kristen Grauman UT-Austin. Matching local features. Kristen Grauman. Matching local features. ?. Image 2. Image 1. To generate candidate matches , find patches that have the most similar appearance (e.g., lowest SSD)

570 views • 43 slides

Amir Hosein Omidvarnia Spring 2007

Principles of 3D Face Recognition. Amir Hosein Omidvarnia Spring 2007. Outline. Fundamentals 3D face processing stages Illumination Cone method Structured-light pattern method Elastic bunch graph method Open issues and challenges Conclusion. 3D FRT vs. 2D FRT.

563 views • 40 slides

3D Visual Phrases for Landmark Recognition

3D Visual Phrases for Landmark Recognition. Qiang Hao , Rui Cai , Zhiwei Li, Lei Zhang, Yanwei Pang, Feng Wu Tianjin University, Tianjin 300072, P.R. China Microsoft Research Asia, Beijing 100080, P.R. China. Outline. Introduction 3D Visual Phrase Discovery

384 views • 18 slides

Image Retrieval Using Haar Discrete Wavelet Transform

Image Retrieval Using Haar Discrete Wavelet Transform. Advisors : Chi-Hong Lin, Yung-Kuan Chan Speaker: Chin-Hong Lin ( ??? ) Date: 2005-05-04. Outline. Introduction Feature of image Image Retrieval Experiments Conclusion. Introduction. Haar Discrete Wavelet Transform. LL : ??.

409 views • 17 slides

321 views • 15 slides