Difference between revisions of "Template:ProjectProposalByResArea"

Methods for indoor layout reconstruction must be significantly more tolerant to missing data than their outdoor counterparts, since environments such as offices and apartments exhibit extremely high levels of clutter, which typically results in heavy occlusions of walls and other structures of interest, large-scale artifacts, noise and missing data. The proposed work will be developed in collaboration with IRALAB, the Robotics Lab of University of Milano Bicocca.

The work will be based on an existing project, Free Your Camera (http://www.ira.disco.unimib.it/research/robotic-perception-research/free-your-camera-3d-indoor-scene-understanding-from-arbitrary-camera-motion/) and will be part of a robotic framework based on with ROS and in development at IRALAB.

While browsing the Web, a user should be able to visualize the annotations relative to the page they are visiting, and to add new annotations as well.

References:

• Drivatar in Forza Motorsport (http://gar.eecs.ucf.edu/)
• TORCS (http://torcs.sourceforge.net/)
• CIG@PoliMI (http://cig.dei.polimi.it/)
• Daniele Loiacono's AI&R Lab Projects (http://home.dei.polimi.it/loiacono/uploads/Teaching/HomePage/AIRLab_AA20092010.pdf)

• increase the number of markers supported by the system (ARTag + QR codes)
• increase the accuracy of the detection and localization of the marker
• test different algorithms for the solution of the perspective from n points problem

Material:

• papers on PnP algorithms, OpenCV,
• Matlab code with three PnP algorithms implementations
• C++ libraries for marker detection (to be found and evaluated)

Expected outcome:

• C++ library to the robust localization of artificial markers
• a ROS node performing accurate ARTag localization
• a comparison of Tags and algorithms in a real world scenario
• The use of this library in a SLAM framework (Thesis)

Required skills or skills to be acquired:

• background on computer vision and image processing
• C++ programming under Linux

The purpose of this project is to develop translation tools for different social annotation systems, collect their data in a common format (expressed using an ontology), and show them through a unique user interface, able to display different annotations (i.e. geo coordinates, dates, tags, etc.) in different ways. Moreover, tests and evaluations should be performed on this aggregator to show how efficient the queries are when performed on-the-fly or from an intermediate knowledge base.

• J.R. Wolpaw et al. Brain-computer interfaces for communication and control (http://tinyurl.com/yhq27pq)
• Erich E. Sutter. The brain response interface: communication through visually-induced electrical brain responses (http://tinyurl.com/yfqvwp6)

The thesis will explore some of the aspects of the problem, eventually participating to the production of prototypes to be tested with real users. The thesis gives the possibility to develop a robot with special requirements, and to work with gesture interpretation by using devices such as Kinect or the WII Mote. The work is done in an interdisciplinary group including care givers and designers.

Picture taken from http://gar.eecs.ucf.edu/

References

• Galactic Arm Race (http://gar.eecs.ucf.edu/)
• TORCS (http://torcs.sourceforge.net/)
• CIG@PoliMI (http://cig.dei.polimi.it/)
• J. Togelius, R. De Nardi and S. Lucas. Towards automatic personalised content creation for racing games (http://julian.togelius.com/Togelius2007Towards.pdf)
• Daniele Loiacono's AI&R Lab Projects (http://home.dei.polimi.it/loiacono/uploads/Teaching/HomePage/AIRLab_AA20092010.pdf)

Material

• C++ modules for Extended Kalman Filtering
• libraries for automatic differentiation (http://www.autodiff.org/)

Expected outcome: New modules implementations based on automatic differentiation A comparison between the old stuff and new approach

Required skills or skills to be acquired:

• C++ programming under Linux

The robot first exhibition has been at Robotica 2009, within HI-Tech Expo at Fiera di Milano, on November 23-25, 2009. Here, the robot had to go around in an area delimited by a white stripe and contact verbally and with gestures people entering the area, in order to attract them to the booth.

Behaviors and gestures have still to be developed to come to an interesting and robust demo at next Robotica, or at other ehibits (e.g. at the Museo della Scienza of Milan).

• L. E. Baum and J. A. Eagon. An inequality with applications to statistical estimation for probabilistic functions of markov processes and to a model for ecology. Bull. Amer. Math. Soc, 73(73):360–363, 1967.
• P. W. Colgan. Quantitative Ethology. John Wiley & Sons, New York, 1978.
• A. Stolcke and S. M. Omohundro. Hidden markov model induction by bayesian model merging. In Stephen Jos é Hanson, Jack D. Cowan, and C. Lee Giles, editors, Advances in Neural Information Processing Systems, volume 5. Morgan Kaufmann, San Mateo, CA, 1993.
• Zoubin Ghahramani. Learning dynamic bayesian networks. Lecture Notes in Computer Science, 1387:168, 1998.
• A. Stolcke and S. M. Omohundro. Best-first model merging for hidden markov model induction. Technical Report TR-94-003, 1947 Center Street, Berkeley, CA, 1994.

Material:

• papers from major journals and conferences
• kinet SDK for the extraction of body poses

Expected outcome:

• general framework for the recognition of behaviors from time series
• toolkit for behavior segmentation and recognition from time series
• running prototype based on data coming from the Microsoft kinect sensor

Required skills or skills to be acquired:

• understanding of techniques for behavior recognition
• background on pattern recognition and stochastic models
• basic understanding of computer vision
• C++ programming under Linux or Matlab

In order to speed up the development and the maintenance of embedded applications, a way to update the firmware on a microcontroller without the need of connecting cables or programmers can be very handy. We are developing a framework for rapid prototyping of low-cost robots, with smart devices that exchange data on a CAN bus network. The CAN bus bootloader is one of the components we need for this project, enabling remote firmware upgrades of all the devices connected to the CAN network.

This project aims to develop a CAN bus bootloader for STM32 ARM Cortex-M3 microcontrollers, and eventually for other architectures.

No special skills are required, except basic c and object oriented programming.

analysis of different algorithms for the optimization of pseudo-Boolean functions, i.e., functions defined over binary variables with values in R. These functions have been studied a lot in the mathematical programming literature, and different algorithms have been proposed (1). More recently, the same problems have been faced in evolutionary computations, with the use of genetic algorithms, and in particular estimation of distribution algorithms (2,3). Estimation of distribution algorithms are a recent meta-heuristic, where classical crossover and mutation operators used in genetic algorithms are replaced with operators that come from statistics, such as sampling and estimation.

The focus will be on the implementation of a new algorithm able to combine different approaches (estimation and sampling, from one side, and exploitation of prior knowledge about the structure of problem, on the other), together with the comparison of the results with existing techniques that historically appear in different (and often separated) communities. Good coding (C/C++) abilities are required. Since the approach will be based on statistical models, the student is supposed to be comfortable with notions that come from probability and statistics courses. The project could require some extra effort in order to build and consolidate some background in math, especially in Bayesian statistics and MCMC techniques, such as Gibbs and Metropolis samplers (4).

The project can be extended to master thesis, according to interesting and novel directions of research that will emerge in the first part of the work. Possible ideas may concern the proposal of new algorithms able to learn existing dependencies among the variables in the function to be optimized, and exploit them in order to increase the probability to converge to the global optimum.

Picture taken from http://www.ra.cs.uni-tuebingen.de/

Bibliography

1. Boros, Endre and Boros, Endre and Hammer, Peter L. (2002) Pseudo-boolean optimization. Discrete Applied Mathematics.
2. Pelikan, Martin; Goldberg, David; Lobo, Fernando (1999), A Survey of Optimization by Building and Using Probabilistic Models, Illinois: Illinois Genetic Algorithms Laboratory (IlliGAL), University of Illinois at Urbana-Champaign.
3. Larrañga, Pedro; & Lozano, Jose A. (Eds.). Estimation of distribution algorithms: A new tool for evolutionary computation. Kluwer Academic Publishers, Boston, 2002.
4. Image Analysis, Random Fields Markov Chain Monte Carlo Methods

estimation.

The focus will be on the implementation of new hybrid algorithms able to combine estimation of distribution algorithms with different approaches available in the evolutionary computation literature, such as genetic algorithms and evolutionary strategies, together with other local search techniques. Good coding (C/C++) abilities are required. Some background in combinatorial optimization form the "Fondamenti di Ricerca Operativa" is desirable. The project could require some effort in order to build and consolidate some background in MCMC techniques, such as Gibbs and Metropolis samplers (4). The project could be extended to master thesis, according to interesting and novel directions of research that will emerge in the first part of the work.

Computer vision provides a large number of optimization problems, such as new-view synthesis, image segmentation, panorama stitching and texture restoration, among the others, (6). One common approach in this context is based on the use of binary Markov Random Fields and on the formalization of the optimization problem as the minimum of an energy function expressed as a square-free polynomial, (5). We are interested in the proposal, comparison and evaluation of different Estimation of Distribution Algorithms for solving real world problems that appear in computer vision.

Pictures taken from http://www.genetic-programming.org and (6)

Bibliography

1. Pelikan, Martin; Goldberg, David; Lobo, Fernando (1999), A Survey of Optimization by Building and Using Probabilistic Models, Illinois: Illinois Genetic Algorithms Laboratory (IlliGAL), University of Illinois at Urbana-Champaign.
2. Larrañga, Pedro; & Lozano, Jose A. (Eds.). Estimation of distribution algorithms: A new tool for evolutionary computation. Kluwer Academic Publishers, Boston, 2002.
3. Lozano, J. A.; Larrañga, P.; Inza, I.; & Bengoetxea, E. (Eds.). Towards a new evolutionary computation. Advances in estimation of distribution algorithms. Springer, 2006.
4. Pelikan, Martin; Sastry, Kumara; & Cantu-Paz, Erick (Eds.). Scalable optimization via probabilistic modeling: From algorithms to applications. Springer, 2006.
5. Image Analysis, Random Fields Markov Chain Monte Carlo Methods
6. Carsten Rother, Vladimir Kolmogorov, Victor Lempitsky, Martin Szummer. Optimizing Binary MRFs via Extended Roof Duality, CVPR 2007

Material

• a huge corpus of literature on the topic
• libraries on visual odometry (http://www.cvlibs.net/datasets/kitti/index.php)
• C++ library for image processing and computer vision (OpenCV)

Expected outcome:

• a set of running algorithms performing visual odometry

Required skills or skills to be acquired:

• computer vision and 3D reconstruction
• C++ programming under Linux

• J.R. Wolpaw et al. Brain-computer interfaces for communication and control (http://tinyurl.com/yhq27pq)

Several theses and projects are available on this topic and involve different games: TORCS, Unreal Tournament and Quake Live. Please contact us for additional information.

References

• TORCS (http://torcs.sourceforge.net/)
• QuakeLive (http://www.quakelive.com)
• Daniele Loiacono's AI&R Lab Projects (http://home.dei.polimi.it/loiacono/uploads/Teaching/HomePage/AIRLab_AA20092010.pdf)