Image Processing and Computer Vision

13:00 29-05-2014
The Faculty of Science and the Department of Applied Mathematics Invite faculty and students to the Workshop:

Image Processing & Computer Vision


29.5.14 | 13:15 | Building 3, Conference Room


The program:
 
Registration and refreshments 13:15
Opening 13:45
Prof. R. Coifman, Department of Mathematics, Yale University
Learning Dual contextual/conceptual geometries to achieve signal processing of Databases/Matrices.
14:00
Randomized LU Decomposition for Low Rank Approximation with Applications to Image Processing.
14:50
Break 15:40
Prof. S. Avidan, Faculty of Engineering, Tel-Aviv University
Organizing Visual Data
16:00
SceneNet using the power of crowd sourcing for 3D Reconstruction of videos
16:50

 

   

Learning Dual contextual/conceptual geometries to achieve signal processing of Databases/Matrices.
Prof. R. Coifman, Department of Mathematics, Yale University.
We provide an overview of recent developments in signal processing methodologies for organization of empirical data.
We focus on data provided as an array or matrix, where we view the rows and columns of the matrix as "informing each other”, generating joint row/column organizational geometries, enabling automated analytic organizations of Matrices or Databases as well as joint "signal processing”.

In particular we introduce methodologies, enabling functional regression, prediction, denoising, compression, fast numerics, and so on.

We illustrate these ideas to organize and map out in an automatic and purely data driven fashion, images, text documents, psychological questionnaires, medical profiles, physical sensor data, financial data.
 

 
 
Randomized LU Decomposition for Low Rank Approximation with Applications to Image Processing.
Gil Shabat, School of Electrical Engineering, Tel Aviv University.

Recently, there is an on-going interest in randomized algorithms for solving numerical linear algebra problems. Randomized algorithms are used as a trade-off between speed and accuracy, enabling us to accelerate the computations significantly.

In this talk, we present a novel algorithm for low rank LU decomposition that uses random projections type techniques and can be used to efficiently compute a low rank approximation of large matrices. The randomized LU algorithm can be parallelized and efficiently run on GPUs, making it suitable for image processing algorithms.
 
Moreover, it can utilize sparsity to accelerate the computations even farther. Several error bounds for the algorithm's approximations are proved using random Gaussian and random sparse matrices .

As an application, the algorithm can be used for fast dictionary learning that can be used for compression and classification.
Joint work with A. Averbuch and Y. Shmueli
 
 
 

 

 
Organizing Visual Data
Prof. S. Avidan, Faculty of Engineering, Tel-Aviv University

What is an image? There is a growing trend in the last decade or so to treat images as a bag of patches. This can be seen in texture synthesis, object and action recognition and even image denoising. This approach has great success but it comes at a price.
 
All geometric information is lost. In this talk I argue that much geometric information is implicitly encoded in the bag of patches, demonstrate how to recover it and show a number of potential image editing applications. Then, I will consider the problem of organizing images in the correct temporal order. This problem occurs naturally in group photography where a group of people take multiple images of a dynamic event.
 
Since the cameras are not necessarily synchronized we need to organize the images in their correct temporal order, a problem we term Photo Sequencing. Common to both cases is the need to establish what is the correct way to organize the visual data and then devising ways to achieve this order.

Joint work with Tali Dekel, Yael Moses, Taeg-Sang Cho, Moshe Butman and Bill Freeman.
 

 

 
 
SceneNet using the power of crowd sourcing for 3D Reconstruction of videos
Dr. Chen Sagiv, SagivTech and SceneNet coordinator

The aim of SceneNet is to use the power of crowd sourcing, in the form of multiple mobile phone users, to create a higher quality 3D video scene experience that can be shared via social networks. A typical SceneNet scenario can be a rock concert, a sports event, a private or a public ceremony, breaking news events and any other multiple mobile users' crowded event.

The SceneNet pipeline starts at the mobile device where the video streams are acquired, pre-processed and transmitted along with a tagging identity to the server. At the server, the various video streams are registered and synchronized and then submitted to 3D reconstruction to generate a multi-view video scene that can be edited and shared by the users.

In this talk we will give an overview of the SceneNet project and the technologies we develop in order to facilitate it, from mobile computing to 3D reconstruction and GPU computing.

This project is done in collaboration between SagivTech, Ecole Polytechnique de Lausanne (EPFL) and the University of Bremen.
This project is funded by the European Union under the 7th Research Framework, programme FET-Open SME, Grant agreement no. 309169.