'Edge strength' diaporamas de présentation

Phase: An Important Low-Level Image Invariant Peter Kovesi

Phase: An Important Low-Level Image Invariant Peter Kovesi School of Computer Science & Software Engineering The University of Western Australia. Canny. Canny. Much of computer vision depends on the ability to correctly detect, localize and match local features.

989 views • 89 slides

Phase: An Important Low-Level Image Invariant Peter Kovesi

Phase: An Important Low-Level Image Invariant Peter Kovesi School of Computer Science & Software Engineering The University of Western Australia. Canny. Canny. Much of computer vision depends on the ability to correctly detect, localize and match local features.

905 views • 89 slides

Recap from Friday

Recap from Friday. linear Filtering convolution differential filters filter types boundary conditions. The Frequency Domain. Somewhere in Cinque Terre, May 2005. CS195g: Computational Photography James Hays, Brown, Spring 2010. Slides from Steve Seitz and Alexei Efros. Salvador Dali

836 views • 67 slides

Edge Detection

374 views • 17 slides

Supervised Random Walks: Predicting and Recommending Links in Social Networks

Supervised Random Walks: Predicting and Recommending Links in Social Networks. Lars Backstrom (Facebook) & Jure Leskovec (Stanford) Proc. of WSDM 2011 Present by Mo Mingzhen. Problem. Friendship is important on social networks How to predict the future interaction

352 views • 13 slides

Convolution

Convolution. A convolution operation is a cross-correlation where the filter is flipped both horizontally and vertically before being applied to the image: It is written: Suppose H is a Gaussian or mean kernel. How does convolution differ from cross-correlation?. Continuous filtering.

418 views • 12 slides