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--- projects [2011/09/03 21:14] – dymond
+++ projects [2013/09/03 23:01] – pd
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-====== Currently offered Projects, Fall 2011 (updated September 3, 2011)  ======
+====== Proposed Projects for Fall 2013 ======
-(Listed in order received.)
-====== Building an autonomous motorboat ======
+\\
+====== Attentive Sensing for Better Two-Way Communication in Remote Learning Environments ======
-**Supervisor**: Michael Jenkin
+**Supervisor**: James Elder
-**Required Background**: General CSE408x prerequisites
+**Required Background**: General CSE408x prerequisites, good programming skills,
+good math skills, knowledge of C and MATLAB programming languages
-**Recommended Background**: Robotics
+One of the challenges in remote learning is to allow students to communicate effectively with the lecturer.  For example, when a student asks a question, communication will be more effective if the instructor has a zoomed view of the student’s face, so that s/he can interpret expressions etc.
+The goal of this project is to apply attentive sensing technology (www.elderlab.yorku.ca) to this problem.  This technology is able to monitor a large environment such as a classroom and direct a high-resolution ‘attentive’ sensor to events of interest.
+In particular, working with a senior graduate student or postdoctoral fellow, the  successful applicant will:
+  - Study the problem of detecting hand-raises in the preattentive sensor stream
+  - Implement algorithms for detecting hand-raises based upon this investigation
+  - Evaluate these algorithms in a real-classroom setting, using proprietary attentive sensing technology
-__Description__
-An opportunity exists for a small number of students to build an autonomous motorboat using a RC motorboat as a base and integrating computation and control in the form of a Beagleboard. Students will participate in lectures and labs associated with CSE6324 (Part I). Interested students should attend the first lecture of CSE6324. See the departmental schedule for time and place.
+====== Attentive Sensing for Sport Video Recording Markets ======
+**Supervisor**: James Elder
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+**Required Background**: Good programming skills; Good math skills; Knowledge of C and MATLAB programming languages
-:
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-====== Athenians Data Project ======
-**Supervisor**: Nick Cercone
-**Required Background**: General CSE408x prerequisites
-**Recommended Background**: Data Mining
-__Description__
-The Athenians Project is a multi-year, ongoing project of compiling, computerizing and studying data about the persons of ancient Athens.
-Possible project ideas for this term span from simpler ones such as
-how to present data in the best possible way, add spatial characteristics to existing data,
-add multimedia data, improve text searching, etc. to more complex ideas such as filling
-missing parts for the "broken" words on the existing inscriptions. Filling text for the broken
-words has been done in the past using expert knowledge. Those experts have establish
-certain rules/guidelines that may be possible to extrapolate in some kind of expert system
-when talking in IT terminology. Furthermore, any hypotheses on word completion enters
-the database with some likelihood. Associating probabilities with hypotheses introduces
-another opportunity for research projects.
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-:
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-====== Three-Dimensional Context from Linear Perspective for Video Surveillance Systems ======
-**Supervisor**:  James Elder
-**Requirements**:  Good facility with applied mathematics
-__Description__
-To provide visual surveillance over a large environment, many surveillance cameras are typically deployed at widely dispersed locations.  Making sense of activities within the monitored space requires security personnel to map multiple events observed on two-dimensional security monitors to the three-dimensional scene under surveillance.  The cognitive load entailed rises quickly as the number of cameras, complexity of the scene and amount of traffic increases.
-This problem can be addressed by automatically pre-mapping two-dimensional surveillance video data into three-dimensional coordinates.  Rendering the data directly in three dimensions can potentially lighten the cognitive load of security personnel and make human activities more immediately interpretable.
-Mapping surveillance video to three-dimensional coordinates requires construction of a virtual model of the three-dimensional scene.  Such a model could be obtained by survey (e.g., using LIDAR), but the cost and time required for each site would severely limit deployment.  Wide-baseline uncalibrated stereo methods are developing and have potential utility, but require careful sensor placement, and the difficulty of the correspondence problem limits reliability.
-This project will investigate a monocular method for inferring three-dimensional context for video surveillance.  The method will make use of the fact that most urban scenes obey the so-called “Manhattan-world” assumption, viz., a large proportion of the major surfaces in the scene are rectangles aligned with a three-dimensional Cartesian grid (Coughlan & Yuille, 2003).  This regularity provides strong linear perspective cues that can potentially be used to automatically infer three-dimensional models of the major surfaces in the scene (up to a scale factor).  These models can then be used to construct a virtual environment in which to render models of human activities in the scene.
-Although the Manhattan world assumption provides powerful constraints, there are many technical challenges that must be overcome before a working prototype can be demonstrated.  The prototype requires six stages of processing:    1)The major lines in each video frame are detected.  2)  These lines are grouped into quadrilaterals projecting from the major surface rectangles of the scene.  3)  The geometry of linear perspective and the Manhattan world constraint are exploited to estimate the three-dimensional attitude of the rectangles from which these quadrilaterals project.  4)  Trihedral junctions are used to infer three-dimensional surface contact and ordinal depth relationships between these surfaces.  5)  The estimated surfaces are rendered in three-dimensions.  6)  Human activities are tracked and rendered within this virtual three-dimensional world.
-The student will work closely with graduate students and postdoctoral fellows at York University, as well as researchers at other institutions involved in the project.  The student will develop skills in using MATLAB, a very useful mathematical programming environment, and develop an understanding of basic topics in image processing and vision.
-For more information on the laboratory: [[http://www.elderlab.yorku.ca]]
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-:
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-====== Estimating Pedestrian and Vehicle Flows from Surveillance Video ======
-**Supervisor**:  James Elder
-**Requirements**:  Good facility with applied mathematics
-__Description__
-Facilities planning at both city (e.g., Toronto) and institutional (e.g., York University) scales requires accurate data on the flow of people and vehicles throughout the environment.  Acquiring these data can require the costly deployment of specialized equipment and people, and this effort must be renewed at regular intervals for the data to be relevant.
-The density of permanent urban video surveillance camera installations has increased dramatically over the last several years.  These systems provide a potential source of low-cost data from which flows can be estimated for planning purposes.
-This project will explore the use of computer vision algorithms for the automatic estimation of pedestrian and vehicle flows from video surveillance data.  The ultimate goal is to provide planners with accurate, continuous, up-to-date information on facility usage to help guide planning.
-The student will work closely with graduate students and postdoctoral fellows at York University, as well as researchers at other institutions involved in the project.  The student will develop skills in using MATLAB, a very useful mathematical programming environment, and develop an understanding of basic topics in image processing and vision.
-For more information on the laboratory: [[http://www.elderlab.yorku.ca]]
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+The goal of this project is to modify York University’s patented attentive sensor technology to the sport video recording market.  Specific application domains under investigation include skiing, indoor BMX parks, and horse tracks.
-:
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+The general problem is to use attentive sensing technology (www.elderlab.yorku.ca) to visually detect and track multiple moving agents (e.g., skiers, riders, horses) and to select specific agents for active high-resolution smooth pursuit.
-====== Tandem repeat detection using spectral methods ======
+The student will work with senior graduate students, postdoctoral fellows and research scientists to help modify the attentive sensing technology to operate in these domains.   Specific tasks include:
-**Supervisor**: Suprakash Datta
+.     Ground-truth available datasets
+.     Evaluate current attentive algorithms on these datasets
-**Required Background**: The student should have completed undergraduate courses in Algorithms and Signals and Systems.
+.     Modify these algorithms to improve performance on these datasets
-**Recommended Background**: Some background in Statistics is desirable but not essential.
+------------
-__Description__
-DNA sequences of organisms have many repeated substrings. These are called repeats in Biology, and include both exact as well as approximate repeats. Repeats are of two main types: interspersed repeats (which are spread across a genome) and tandem repeats, which occur next to each other. Tandem repeats play important roles in gene regulation and are also used as markers that have several important uses, including human identity testing.
-Finding tandem repeats is an important problem in Computational Biology. The techniques that have been proposed for it fall into two classes: string matching algorithms and signal processing techniques. In this project, we will explore fast, accurate algorithms for detecting tandem repeats and evaluate the outputs of the algorithms studied by comparing their outputs with those of available packages, including mreps (http://bioinfo.lifl.fr/mreps/), SRF (http://www.imtech.res.in/raghava/srf/) and TRF (http://tandem.bu.edu/trf/trf.html).
-The student will implement existing spectral algorithms based on Fourier Transforms and on an autoregressive model. He will then make changes suggested by the supervisor, and evaluate the effect of the modifications. Throughout the course, the student is required to maintain a course Web site to report any progress and details about the project.
+\\
+====== Hunting for Bugs in Logging: applying JPF to log4j ======
+**Supervisor:** Franck van Breugel
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+Description:
-:
+Java PathFinder (JPF) is a tool that can detect bugs in Java code.
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+The Java library Apache log4j allows developers to control which log
+statements are output.  In the past, Dickey et al. [1] have attempted
+to detect bugs in log4j by means of JPF with very limited succes.
-====== Touch- and Gesture-based Text Entry With Automatic Error Correction ======
+Recently, in collaboration with Shafiei (NASA) we have developed
+an extension of JPF called jpf-nhandler.  The aim of this project
+is to apply this extension to log4j.
-**Supervisor**: Scott Mackenzie
+[1] David A. Dickey, B. Sinem Dorter, J. Michael German, Benjamin D. Madore, Mark W. Piper, Gabriel L. Zenarosa. "Evaluating Java PathFinder on Log4J."  2011.
-**Required Background**:
+**Required Background:** General CSE408x prerequisites
-CSE3461 (or equivalent), CSE3311 (or equivalent), CSE4441 (or equivalent)
+\\
-A student wishing to do this project must be well versed in Java, Eclipse, and developing java code for the Android operating system.
+------------
+\\
+======Hybrid 2D/3D User Interfaces for 3D Rotation ======
+**Supervisor:** Wolfgang Stuerzlinger
-**Recommended Background**:
+**Required Background:** General 408X prerequisites, 3D Computer Graphics (3431) completed or equivalent, C/C++ coding experience or (if using Unity 4) Javascript C# coding experience
-Possession of an Android touch-based phone or tablet would be an asset, but is not essential.
-__Description__
+This project implements and evaluates a new method for 3D Rotation where the user uses both a 2D and 3D user interface to complete the task. The fundamental idea is to use the 3D interface for large-scale manipulation, but the 2D interface for precise adjustments. The project will use a Leap Motion or similar technology for 3D tracking.
-This project involves extending a touch-based text entry method to include automatic error correction.  The method, as is, uses Graffiti strokes entered via a finger on a touch-based Android tablet.  The stroke recognizer works fine, but it is not perfect.  Some strokes are mis-recognized while others are un-recognized.  The fault is sometimes attributable to the recognizer, but, often, the fault is simply that the user's input was sloppy.  The work involves developing, integrating, and testing software.  The core software is already written, but automatic error correction is lacking. The primary task of the added software is to receive a sequence of characters representing a word and matching the sequence with words in a dictionary.  If a match is found, all is well (presumably).  If a match is not found, the search is extended to find a set of candidate words that are "close" to the inputted sequence.  "Close", here, involves using a minimum string distance algorithm (provided).  The user interface must be modified to present the user with alternative words in the event an error occurred.  The user selects the desired word by tapping on a word in the list.  The project will involve testing the new input method in a small user study and writing up a report describing the work and presenting the results of the user study.
+\\
+------------
+\\
+====== Immersive Virtual Reality Kitchen Planner ======
+**Supervisor:** Wolfgang Stuerzlinger
+**Required Background:** General 408X prerequisites, 3D Computer Graphics (3431) completed or equivalent, 4431 desired, Javascript or C# coding experience
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+This project implements a kitchen planner application for an immersive virtual reality system. The implementation will be based on Unity 4.
-:
+\\
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+------------
-====== Early Breast Cancer Detection based on MRI’s. ======
+\\
+======3D Interaction in Immersive Virtual Reality======
-**Supervisor**: Amir Asif
+**Supervisor:** Wolfgang Stuerzlinger
-**Required Background**: General CSE408x prerequisites
+**Required Background:** General 408X prerequisites, 3D Computer Graphics (3431) completed or equivalent, 4431 desired, Javascript or C# coding experience
-**Recommended background**: Signal processing
+This project implements and tests various 3D Navigation and 3D Interaction methods in an immersive virtual reality system. The target is to enable the user to roam freely in a large environment while still being able to interact with the environment. The implementation will be based on Unity 4.
+\\
+------------
+\\
+------------------
+\\
+Additional current possible  projects will be listed here by start of fall term.
+\\
+------------------
+\\
+\\
-Project Description: This research will develop advanced computer-aided, signal
-processing techniques for early detection of breast cancer using the available
-modalities. In particular, we propose to develop time reversal beamforming imager,
-based on our earlier work in time reversal signal processing, for detecting early stage
-breast cancer tumours from MRI data.
-Our preliminary work has illustrated the type of
-results that are possible for breast cancer detection by applying time reversal signal
-processing on MRI breast data. In this research, we propose to extend these results to
-provide a quantitative understanding of the practical gains provided by time reversal
-in MRI based breast cancer detection and its limitations. This will be accomplished
-a local hospital, and running our algorithms on these datasets. The first step is
-important to check the validity of our algorithms. The next step is to compare the
-estimated locations of the tumours (as derived with our algorithms) to their precise
-locations as identified by the pathologists. The second step will quantify the accuracy
-of our estimation algorithms.
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-:
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