Copyright © 2014 by Intelligent Systems Laboratory, Computer Science Department, Technion - Israel Institute of Technology, Haifa 3200003, Israel. All rights reserved

Large Databases For Object Recognition

Large Databases For Object RecognitionStudents: Cirill Aizenberg, Dima Altshuler
Instructor:  Erez Berkovich

With the advent of the Internet it is now possible to collect hundreds of millions of images. These images come with varying degrees of label information. Semi-supervised learning can be used for combining these different label sources. However, it scales polynomially with the number of images. In this work we utilize recent results in machine learning to obtain highly efficient approximations for semi-supervised learning that are linear in the number of images. This enables us to apply semi-supervised learning to a large database of images gathered from the Internet.



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Navigation Using Color And Depth

Navigation Using Color And DepthStudents: Assaf Cohen and Hila Sastiel
Instructor:  Amir Geva

For any mobile device, the ability to navigate in its environment is one of the most important capabilities of all. Robot navigation means its ability to determine its own position in its frame of reference and then to plan a path towards some goal location. In order to navigate in its environment, the robot or any other mobility device requires representation i.e. a map of the environment and the ability to interpret that representation.


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An Algorithm For Multiple Target Tracking I

An Algorithm For Multiple Target Tracking IStudents: Naseem Mahajna and Muhammad Zoabi 
Instructor: Ishay Kamon

In this project the students have implemented an algorithm for multiple target tracking. Graph algorithms are used for efficient computation of association and tracking problems.



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STC Simulation

STC SimulationStudents:Avi Stiefel
Instructors: Yotam Elor

A memoryless implementation of the on-line STC floor-coverage algorithm, in the cisbots Simulator environment.  The robot builds a spanning tree to cover the surface as it moves about the workspace, and then returns to the original location.  The robot leaves markers as it covers the floor, to signify which portions of the workspace have been covered, and which portions remain to be covered. 



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Quad-Rotor Control

Quad-Rotor ControlStudents: Eran Nissim and Asaf Tal
Instructors: Leonid Mirkin

A quad-rotor type X-3DBL UFO helicopter is used in our project. The project goal is to learn the quad-rotor dynamics and create a control algorithm that will stabilize its orientation. The quad-rotor is initially equipped with a 3DOF gyro . In addition to the gyro, an Xsens IMU added. 


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