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

Visual Tracking in a General Context via Tracker Combination and Low-Level Cues

Visual Tracking in a General Context via Tracker Combination and Low-Level CuesBy: Ido Leichter

The Computer Vision research area is concerned with the extraction of information about the content of a scene from one or more images. The extracted information may, for example, be the three-dimensional structure of the scene or objects contained in it (e.g., stereo: three-dimensional descriptions from two or more views), the identity of the objects (e.g., face recognition), the class the imaged objects are classified into (e.g., character recognition) or the the class of the image itself (e.g, deciding whether an input image contains an urban or a natural scene), a meaningful partitioning of the image into image regions (segmentation), and the locations of specific kinds of targets in the image (e.g., face detection).


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Understanding Events in Video

Understanding Events in VideoBy: Gal Lavee

Video events are those high-level semantic concepts that humans perceive when observing a video sequence. Understanding these concepts is the highest level task in computer vision. It relies on sufficient solutions to many lower-level tasks such as edge detection, optical flow estimation, object recognition, object classification and tracking. The maturity of many solutions to these low-level problems has spurred additional interest in utilizing them for higher level tasks such as video event understanding.


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Robust Epipolar Geometry Estimation Using Noisy Pose Priors

Robust Epipolar Geometry Estimation Using Noisy Pose PriorsBy: Yehonatan Goldman

Epipolar geometry estimation is fundamental to many computer vision algorithms. It has therefore attracted a lot of interest in recent years, yielding high quality estimation algorithms for wide baseline image pairs. Currently many types of cameras (e.g., in smartphones and robot navigation systems) produce geo-tagged images containing pose and internal calibration data. Exploiting this information as part of an epipolar geometry estimation algorithm may be useful but not trivial, since the pose measurement may be quite noisy. We introduce SOREPP, a novel estimation algorithm designed to exploit pose priors naturally. It sparsely samples the pose space around the measured pose and for a few promising candidates applies a robust optimization procedure. It uses all the putative correspondences simultaneously, even though many of them are outliers, yielding a very efficient algorithm whose runtime is independent of the inlier fractions. SOREPP was extensively tested on synthetic data and on hundreds of real image pairs taken by a smartphone. Its ability to handle challenging scenarios with extremely low inlier fractions of less than 10% was demonstrated as was its ability to handle images taken by close cameras. It outperforms current state-of-the-art algorithms that do not use pose priors as well as other algorithms that do.


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Quadrotor Simulation

Quadrotor SimulationBy: Michael Chojnacki

This report presents a Quad-rotor simulation built for the Intelligent Systems Lab at the Computer Science Faculty at the Technion IIT.
Different projects at the lab use quadrotors as platforms for research, mainly in the computer vision aided navigation field. A tailor made quadrotor simulation responds to the need to fully understand the system dynamics, the control laws and sensors modeling, in order to assess the algorithms developed in the frame of the research prior to flight testing. 


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Processing and Interpretation of Biological Microscopical Images

Processing and Interpretation of Biological Microscopical ImagesBy: Grigory Begelman

This research thesis addresses the problems of microscopic pathology analysis. It presents a general framework for computer-aided diagnostics. The aim of the framework is unification and improvement of the pathological examination routine. The framework combines telepathology with computer-aided diagnostics algorithms. The framework targets image acquisition and interpretation stages. The image acquisition subsystem solves problems related to microscopical slide digitization such as biomed-ical image registration, data representation, and processing. The interpretation sub-system uses a support vector machine classifier together with a feature selection for computer-aided diagnostics. The histopathological and cytopathological systems for computer-aided diagnostics are implemented using the presented framework. 


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