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Multiple Target Tracking From An Airborne Camera

Multiple Target Tracking From An Airborne CameraStudents: Yoni Faradian, Dikla Eisinger
Instructor:  Ishay Kamon

In recent years there is a growing interest in detection of moving objects from aerial camera. For example, in Wide Area Airborn Surveillance (WAAS) huge images taken from unmanned aerial vehicle (UAV) are used to detect and track cars over a large area, i.e. entire city.

In this project we will implement algorithms for object detection is such scenarios. 

 

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Surgery Simulation Using Image Processing

Surgery Simulation Using Image ProcessingStudents: David Fattakhov, Peleg Harel
Instructor:  David Peles

With the high precision that is required during a surgery it may be almost impossible for a human eye to reach high enough precision based on an image taken of the organ or a specific area. The Surgery Simulation was built to overcome this obstacle using image proccessing techniques. Given two 2-dimensional images the application will allow us to mark points on the images and after analyzing the input the robot will move towards the exact points on the organ/area marked on the images and proceed with the needed procedure. 

 

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Computer Assisted Classification Of Adenocarcinoma

Computer Assisted Classification Of AdenocarcinomaStudents: Ofer Givoli
Instructors:  Dr. Erez Berkovich

The project's goal was developing an algorithm that classifies images into classes (normal tissue, adenoma, adenocarcinoma). We've decided on a set of features to extract from each image in order to train and use a classifier (using the machine learning approach). 

 

 

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Fast Scale Space Ridge Detection Using Integral Image

Fast Scale Space Ridge Detection Using Integral ImageStudents: Tal Amir, Boris Cherevatsky
Instructors:  Dr. Renen Adar

We would like to detect roads in aerial photos by using ridge detection. Due to different road widths, we would like to detect ridges in different scales. In [1] T. Lindeberg (1996) proposed a scale-space ridge detection with automatic scale selection by using Gaussian derivatives. We propose a faster approach that approximates Lindeberg’s solution, plus some additions. 

 

 

 

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Change Detection In Video Streams Using One Class SVMs

Change Detection In Video Streams Using One Class SVMsStudents: Alon Brifman
Instructors:  Assaf Glazer and Prof. Michael Lindenbaum

Change-detection methods in video streams have varies applications in health monitoring, video surveillance, and quality control systems. These methods’ goal is to find objects of interest in an image sequence. For this purpose, it is common to detect objects by comparing new images with a representation of the background scene. In order to do so, one has to model the background first. Using machine learning algorithms it is possible to try and learn the background model from a sample of the scene. One of these learning methods is the "One Class SVM."

 

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