Template:ProjectProposalByResArea

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Wiki Page: 3D Scene Understanding
FreeCamera.png
Title: 3D Indoor scene understanding and layout reconstruction for a mobile robot in collaboration with UnimiB
Description: The proposed project aims to reconstruct the 3D structural layout of an indoor environment perceived by a mobile robot. From the sensorial data, the robot should be able to reconstruct a geometrical structure of an indoor environment (e.g., an office).

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.

Tutor: [[FrancescoAmigoni | ]] (, , , , , , , , , , , , , , , , , , … further resultswarning.png
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Start: 1 February 2015
Students: 1 - 2
CFU: 10 - 20
Research Area: Robotics
Research Topic: Robotics
Wiki Page: A firefox extension for semantic annotations
Title: A Firefox extension for semantic annotations
Description: Aim of this project is to develop a Firefox extension, to allow a community of users to annotate resources on the Web using a shared RDF vocabulary.

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.

Tutor: [[DavidLaniado | ]] (, , , , , , , , , , , , , , , , , , … further resultswarning.png
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Start:
Students: 1 - 2
CFU: 5 - 20
Research Area: Social Software and Semantic Web
Research Topic: Semantic Annotations
Wiki Page: AI in Racing Games
TORCS2.jpg
Title: AI in Racing Games
Description: This project is focused on TORCS, a state-of-the-art open source racing simulator. From one hand, TORCS represents an ideal bechmark to study Computational Intelligence techniques. On the other hand, Computational Intelligence techniques could be used to improve the game experience in this kind of games. Several projects and theses are available on this topic, please contact us for additional information.

References:

Tutor: [[DanieleLoiacono | ]] (, , , , , , , , , , , , , , , , , , … further resultswarning.png
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Start: 1 October 2009
Students: 1 - 2
CFU: 5 - 20
Research Area: Computational Intelligence and Games
Research Topic: Computational Intelligence and Games
Wiki Page: Accurate AR Marker Location
ARTag.jpg
Title: C++ Library for accurate marker location based on subsequent pnp refinements
Description: ARTags, QR codes, Data Matrix, are visual landmark used for augmented reality, but they could be used for robotics as well. A thesis has already been done on using data matrix for robot localization and mapping, but improvements are required in terms generality, accuracy and robustness of the solution. The goal is thuss to:
  • 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
Tutor: [[MatteoMatteucci | ]] (, , , , , , , , , , , , , , , , , , … further resultswarning.png
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Start: 1 January 2015
Students: 1 - 2
CFU: 5 - 10
Research Area: Computer Vision and Image Analysis
Research Topic: None
Wiki Page: Annotation aggregators from social applications
Title: Annotation aggregators from social applications
Description: Annotations are metadata published about a resource, such as tags in del.icio.us, comments on stumbleupon.com, or twines on Twine.com. One of the main problems of these annotations is that they are not expressed in a standard format: thus, any tool trying to aggregate information from these sources should be able to access each one of them in a different way.

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.

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Start:
Students: 1 - 2
CFU: 5 - 20
Research Area: Social Software and Semantic Web
Research Topic: Semantic Annotations
Wiki Page: Aperiodic visual stimulation in a VEP-based BCI
Bci arch.png
Title: Aperiodic visual stimulation in a VEP-based BCI
Description: Visual-evoked potentials (VEPs) are a possible way to drive the a Brain-Computer Interface (BCI). This projects aims at maximizing the discrimination between different stimuli by using numerical codes derived from techniques of digital telecommunications.
Tutor: [[MatteoMatteucci | ]] (, , , , , , , , , , , , , , , , , , … further resultswarning.png
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Start: 1 October 2009
Students: 1
CFU: 5
Research Area: BioSignal Analysis
Research Topic: Brain-Computer Interface
Wiki Page: Autistic Children Play Robot
SkypeWII3Small.JPG
Title: Autistic Children Play Robot
Description: Within a Polisocial project we will have to implement a robot suitable to play autonomously with autistic children that will interact with it and a game system by gestures.

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.

Tutor: [[AndreaBonarini | ]] (, , , , , , , , , , , , , , , , , , … further resultswarning.png
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Start: 20 January 2015
Students: 1 - 2
CFU: 20 - 20
Research Area: Robotics
Research Topic: Robogames
Wiki Page: Automatic Content Generation in Computer Games
Gar.jpg
Title: Automatic Content Generation in Computer Games
Description: The generation of customized game content for each player is an attractive direction to improve the game experience in the next-generation computer games. In this scenario, Machine Learning could play an important role to provide automatically such customized game content. Several projects and theses are available on this topic, please contact us for additional information.

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

References
Tutor: [[DanieleLoiacono | ]] (, , , , , , , , , , , , , , , , , , … further resultswarning.png
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Start: 1 October 2009
Students: 1 - 2
CFU: 5 - 20
Research Area: Computational Intelligence and Games
Research Topic: Computational Intelligence and Games
Wiki Page: Automatic Differentiation Techniques for Real Time Kalman Filtering
Autodiff.png
Title: Evaluation of Automatic Differentiation Techniques for Gauss-Newton based Simultaneous Localization and Mapping
Description: In Gauss-Newton non linear optimization one of the most tedious part is computing Jacobians. At the AIRLab we have developed a framework for non linear Simultaneous Localization and Mapping suitable for different motion models and measurement equations, but any time you need to change something you need to recompute the required Jacobian. Automatic differentiation is a tool for the automatic differentiation of source code either at compiling time or at runtime; we are interested in testing these techniques in the software we have developed and compare their performance with respect to (cumbersome) optimized computation.

Material

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
Tutor: [[MatteoMatteucci | ]] (, , , , , , , , , , , , , , , , , , … further resultswarning.png
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Start: 1 January 2015
Students: 1 - 2
CFU: 10 - 20
Research Area: Robotics
Research Topic: None
Wiki Page: Automatic generation of domain ontologies
Title: Automatic generation of domain ontologies
Description: This thesis to be developed together with Noustat S.r.l. (see http://www.noustat.it), who are developing research activities directed toward the optimization of knowledge management services, in collaboration with another company operating in this field. This project is aimed at removing the ontology building bottleneck, long and expensive activity that usually requires the direct collaboration of a domain expert. The possibility of automatic building the ontology, starting from a set of textual documents related to a specific domain, is expected to improve the ability to provide the knowledge management service, both by reducing the time-to-application, and by increasing the number of domains that can be covered. For this project, unsupervised learning methods will be applied in sequence, exploiting the topological properties of the ultra-metric spaces that emerge from the taxonomic structure of the concepts present in the texts, and associative methods will extend the concept network to lateral, non-hierarchical relationships.
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Start:
Students: 1 - 2
CFU: 20 - 20
Research Area: Machine Learning
Wiki Page: Barking Robots
E2LateralHeadCutSmall.JPG
Title: Barking Robots
Description: Aim of this project is the development of a robot that can operate autonomously at exhibitions and malls to attract people to a given location, by showing interesting behaviors and interacting with people.

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).

Tutor: [[AndreaBonarini | ]] (, , , , , , , , , , , , , , , , , , … further resultswarning.png
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Start: 25 February 2012
Students: 1 - 2
CFU: 5 - 20
Research Area: Robotics
Research Topic: Robot development
Wiki Page: Behavior recognition from visual data
Gesturelib.jpg
Title: Behavior recognition from visual data
Description: In the literature several approaches have been used to model observed behaviors and these date back to early approaches in animal behavior analysis (Baum and Eagon, 1967)(Colgan, 1978). Nowadays several techniques are used and they can be roughly classified as: State space models, Automata (e.g., Finite State Machines, Agents, etc.), Grammars (e.g., strings, T-Patterns, etc.), Bayeasian models (e.g., Hidden Markov Models), and Dynamic State Variables. The work will leverage on a huge corpus of techniques to devise the most suitable for behavior recognition from visual data. We exclude from the very beginning any deterministic approach being the phenomenon under observation complex and affected by noisy observations. The focus will be mainly of the use of dynamic graphical models (Ghahramani, 1998) and the application of bottom up learning techniques (Stolcke and Omohundro, 1993)(Stolcke and Omohundro, 1994) for model induction.
  • 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
Tutor: [[MatteoMatteucci | ]] (, , , , , , , , , , , , , , , , , , … further resultswarning.png
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Start: 1 April 2012
Students: 1 - 2
CFU: 20 - 20
Research Area: Machine Learning
Research Topic: None
Wiki Page: BringMeHome
E2LateralHeadCutSmall.JPG
Title: BringMeHome
Description:
Tutor: [[AndreaBonarini | ]] (, , , , , , , , , , , , , , , , , , … further resultswarning.png
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Start: 30 March 2013
Students: 1 - 2
CFU: 2 - 20
Research Area: Robotics
Research Topic: E-2? - A robot for exhibitions
Wiki Page: CAN Bus bootloader for STM32 microcontrollers
STM32-H103-1.jpg
Title: CAN Bus bootloader for STM32 microcontrollers
Description: JOINT PROJECT with the Embedded Systems group (contact: Patrick Bellasi http://home.dei.polimi.it/bellasi/)

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.

Tutor: [[AndreaBonarini | ]] (, , , , , , , , , , , , , , , , , , … further resultswarning.png
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Start: 1 March 2012
Students: 1 - 2
CFU: 2 - 5
Research Area: Robotics
Research Topic: Robot development
Wiki Page: Cognitive SLAM
C SLAM Recognition2.png
Title: Cognitive SLAM
Description: We have developed a system that is able to detect, recognize and track objects in an image taken from a low cost robot equipped with a IMU and a low cost camera. The system is capable to detect and recognize objects using a user defined fuzzy tree classifier. However the system performance is heavily dependent on high level feature extraction, such as geometric features. The problem is non trivial due to noisy low cost camera and changes in the light conditions. The aim of this project is to improve the feature extraction and description process, both in performance and quality, possible adding a more complete description or others type of features. The long term aim of the research is to have an autonomuos robot capable to create a semantic map of the envirorment, localize himself , make inference on the map, navigate into the envirorment using the objects as landmarks.

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

Tutor: [[AndreaBonarini | ]] (, , , , , , , , , , , , , , , , , , … further resultswarning.png
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Start: 1 January 2015
Students: 1 - 2
CFU: 5 - 20
Research Area: Robotics
Research Topic: SLAM, Feature Extraction
Wiki Page: Combinatorial optimization based on stochastic relaxation
Stochastic.jpg
Title: Combinatorial optimization based on stochastic relaxation
Description: The project will focus on the study, implementation, comparison and

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
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Start: 1 October 2009
Students: 1 - 2
CFU: 5 - 20
Research Area: Machine Learning
Research Topic: Information Geometry, Stochastic Optimization
Wiki Page: Combining Estimation of Distribution Algorithms and other Evolutionary techniques for combinatorial optimization
Evolve1at300dpi.gif
Title: Combining Estimation of Distribution Algorithms and other Evolutionary techniques for combinatorial optimization
Description: The project will focus on the study, implementation, comparison and analysis of different algorithms for combinatorial optimization using techniques and algorithms proposed in Evolutionary Computation. In particular we are interested in the study of Estimation of Distribution Algorithms (1,2,3,4), a recent meta-heuristic, often presented as an evolution of Genetic Algorithms, 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 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
Tutor: [[MatteoMatteucci | ]] (, , , , , , , , , , , , , , , , , , … further resultswarning.png
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), [[LuigiMalago | ]] (, , , , , , , , , , , , , , , , , , … further resultswarning.png
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)
Start: 1 October 2009
Students: 1 - 2
CFU: 5 - 10
Research Area: Machine Learning
Research Topic: Evolutionary Computation, Stochastic Optimization
Wiki Page: Comparison of State of the Art Visual Odometry Systems
VisualOdometry.jpg
Title: A Comparison of State of the Art Visual Odometry Systems (Monocular and Stereo)
Description: Visual odometry is the estimation of camera(s) movement from a sequence of images. In case we deal with a single camera system we have Monocular Visual Odometry; in case we have more cameras we have a Stero Visual Odometry. The goal of the thesis is to review the state of the art on in visual odometry, classify existing approaches and compare their implementations (many of the algorithms have online source code available).

Material

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
Tutor: [[MatteoMatteucci | ]] (, , , , , , , , , , , , , , , , , , … further resultswarning.png
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Start: 1 January 2015
Students: 1 - 2
CFU: 10 - 20
Research Area: Computer Vision and Image Analysis
Research Topic: None
Wiki Page: Creation of new EEG training by introduction of noise
Bci arch.png
Title: Creation of new EEG training by introduction of noise
Description: A Brain-Computer Interface (BCI) must be trained on the individual user in order to be effective. This training phase require recording data in long sessions, which is time consuming and boring for the user. The aim of this project is to develop algorithm to create new training EEG (electroencephalography) data from existing ones, so as to speed up the training phase.
Tutor: [[MatteoMatteucci | ]] (, , , , , , , , , , , , , , , , , , … further resultswarning.png
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Start: 1 October 2009
Students: 1 - 2
CFU: 5 - 20
Research Area: BioSignal Analysis
Research Topic: Brain-Computer Interface
Wiki Page: Data Mining in Computer Games
QLive.png
Title: Data Mining in Computer Games
Description: Today a lot of data can be extracted from popular games. The analysis of such data allow to discover a lot of interesting information about players, the game and the interaction between the game and different type of players.

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
Tutor: [[DanieleLoiacono | ]] (, , , , , , , , , , , , , , , , , , … further resultswarning.png
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Start: 1 October 2009
Students: 1 - 2
CFU: 5 - 20
Research Area: Computational Intelligence and Games
Research Topic: Computational Intelligence and Games
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