Last week I attended the International Computer Vision Summer School in Sicily, Italy. The main topics were Reconstruction and Recognition. I think the quality of the lectures, organization and location were all quite good, therefore I would recommend it to other PhD students.
Here is a short summary of some of the things we heard about:
Andrew Zisserman (Oxford, UK) – gave an overview of object recognition and image classification, with focus on methods that use “bag of visual words” models. Quite nice for newcomers like me!
Silvio Savarese (UIUC, USA) – talked about 3D representations for object recognition. There is actually a Special Issue of the “Computer Vision and Image Understanding” on the topic at
Luc Van Gool (ETH Zurich, Switzerland) – Lots of cool and fancy demos about 3D reconstruction. They are starting to use some recognition to help reconstruction (opposite direction of S. Savarese).
Stefano Soatto (UCLA, USA) – gave an “opinion talk” on the foundations of Computer Vision and how it can be distinguished from Machine Learning. I would have to read his papers to understand better, but he seems to claim that the existence of non-invertible operations such as
occlusions would support the need for image analysis instead of just “brute-force machine learning”.
We also had Bill Triggs (CNRS) talking about human detection, Jan Koendrick (Utrecht, Netherlands) on “shape-from-shade” and a few tutorials touching stuff as diverse as: SIFT, object tracking, multi-view stereo and photometric methods for 3D reconstruction or
randomized decision forests.
To summarize, I think the message was:
- Traditionally, recognition uses lots of Machine Learning but models keep few 3D information about objects;
- Traditionally, reconstruction uses ideas from geometry, optics and optimization but not learning;
- The future trend is to merge them: use 3D reconstruction to help in recognition tasks and use recognition to help in 3D reconstruction.