Visual Nearest Neighbor Search

Template Matching finds the best match in an image to a given template and this is used in a variety of computer vision applications. I will discuss several extensions to Template Matching. First, dealing with the case where we have millions of templates that we must match at once, second dealing with the case of RGBD images, where depth information is available and finally, presenting a fast algorithm for template matching under 2D affine transformations with global approximation guarantees. Joint work with Simon Korman, Yaron Eshet, Eyal Ofek, Gilad Tsur and Daniel Reichman.

Speaker Details

Shai Avidan is an Associate Professor at the School of Electrical Engineering at Tel-Aviv University, Israel. He earned his PhD at the Hebrew University, Jerusalem, Israel, in 1999. Later, he was a Postdoctoral Researcher at Microsoft Research, a Project Leader at MobilEye, a startup company developing camera based driver assisted systems, a Research Scientist at Mitsubishi Electric Research Labs (MERL), and a Senior Researcher at Adobe. He published extensively in the fields of object tracking in video and 3-D object modeling from images. Recently, he has been working on Computational Photography. Dr. Avidan is an Associate Editor of PAMI and was on the program committee of multiple conferences and workshops in the fields of Computer Vision and Computer Graphics.

Date:
Speakers:
Shai Avidan
Affiliation:
Tel Aviv University