^February March – 2009 ^April

Modern cameras and scanners technologies are capable of generating quality images for models. These images are usually stored as a collection of pictures or points in 3D space. Several approaches are available to process these collections into parametric 3D surfaces that geometrically represent the models. These approaches are usually automatic or semi-automatic, and are supported by algorithms and theories from many research areasin computer science (probability, graph theory, numerical analysis).

In this presentation, I will present several selected papers in computer graphics that attempt to solve the mentioned reconstruction problem. The papers were presented in top rated conferences related to computer graphics (SIGGRAPH2007, SIGGRAPH2008, EUROGRAPHICS2009). Some elementary backgroundis required in the following research areas: random algorithms, probability, numerical analysis, graphs algorithms, and linear algebra, however all the algorithms will be represented clearly (first degree background in related courses should be sufficient).

Topics and approaches that will be presented as time and interest allow: The efficient RANSAC approach, TheGRAPH-CUT as an optimization problem, The "4 is less than 3" approach for random validation and scoring algorithms, Advanced approach for cleaningnoisy datasets.

Many of us are familiar with the concept of an express line (say, 1-10 products) in the supermarket. While traveling in Argentina I saw a supermarket with two express lanes, 0ne for 1-10 items and a second for 11-20 items. Imagine that we manage all the lanes in this way, each one responsible for a different range of items. In the context of computer systems, this has first been suggested and studied by Mor Harchol-Balter and her collaborators in the last 10 years. However, basic questions remained unresolved. How to determine the best range for each lane, should the first lane handle 1-8 items, 1-12 items, etc.? What will the resulting performance be and how does it compare with other methods? How does the distribution of the number of items customers purchase change the behavior of the system? We provide some answers to these and several other issues and explore future directions of research. Joint work with Hagit Sarfati.

Collaboration of multiple intelligent agents on a shared task is a complex research issue, made particularly difficult when communication is limited or impossible. A common solution in multi-agent systems is to commit a team of collaborating agents to a joint plan. Since any deviation from the plan by an agent is hazardous, the common treatment of potential unplanned opportunities is either by ignoring them (even when ``opportunistic'' actions increase the expected utility of the team), or by ad-hoc rules determining whether to accept such opportunities. Neither of these solutions is desirable. In our framework, Abstract World for Opportunistic Local decisions ? AWOL for shortfootnote{Unlike the military term AWOL (Absent WithOut Leave), here the agent defaults only in order to {em increase} (expected) team utility.}, we attempt a disciplined treatment of opportunistic actions, in the context of an existing joint plan. The idea is to model the (stochastic) tradeoff of such opportunistic actions vs. continued commitment to the joint plan, while abstracting away as much as possible from the state of the world. The abstract model is evaluated using strict decision-theoretic criteria, with the goal of applying the optimal decision on whether to accept an opportunistic action in the original domain.