Research Groups - Electrical, Electronic and Computer Engineering

Project descriptions of Mr Grobler – 2010 

Research Groups: 

A1 SystemC / GHDL extension of QEMU 

A2 Altera Nios-II support for QEMU 

A3 Altera Nios-II support for LLVM 

A4 PIC16F887 VHDL core for the Altera DE1 

A5 Multi-protocol analyzer based on the Altera DE1 

A6 Ultrasonic ranging sensor module 

A7 Ultrasonic imaging system 

A8 Low cost Inertial Navigation System 

A9 Optical mouse sensor based dead-reckoning module 

A10 FPGA based encryption for robotic applications 

A11 FPGA based compression for robotic applications 

A12 Integration of autonomous vehicle 

A13 Stereo vision system for robotic vehicles 

T14 MCQ assessment system 

Research Group: 

T15 De-weathering / de-noising of video sequences 

T16 Particle flow analysis in micro-fluidic channels 

T17 Road segmentation and structure analysis for an autonomous rover 

T18 Large scale object recognition 

T19 3D modelling and chroma keying studio 

T20 Real-time object recognition using a GPU 

T21 Real-time video encoding/decoding using natural image statistics 

T22 Map-building of an office environment 

T23 Content based video/image retrieval system 

T24 High performance real-time stereo vision system 

T25 Visual odometry for an autonomous vehicle 

T26 2D-3D pose estimation 

T27 GPU algorithmic implementation 

T28 Heat shimmer modeling, characterization and correction 

T29 Background subtraction in a foliage environment

T30 Identification of stable regions in video sequences 

T31 Distributed object identification and tracking

1. Project number: A1 

2. Project title: SystemC / GHDL extension of QEMU 

3. Study leader: Mr. H Grobler 

4. Research group: Advanced Computing and Embedded Systems 

5. Focus of project: Design 

6. Eligibility 

6.1 Intended degree programme: Computer Engineering or Electronic Engineering 

6.2 Required running average: 60% 

7. Brief description of the project 

SystemC is a new IEEE standardized System Description Language similar in nature to the 

more well know Hardware Description Languages (HDLs) such as VHDL. A reference 

implementation of SystemC exists as an Open Source simulation kernel packaged as a set of 

C++ library routines and macros. The primary advantage of the free / open SystemC standard 

is that it allows hardware / software systems to be described and simulated without requiring 

any other support software. GHDL is a VHDL analyzer and simulator based on GCC. QEMU 

is an Open Source processor and peripheral simulator that supports a large number of CPU 

variants (including X86, ARM, SPARC, MIPS, PowerPC, etc.). Recently QEMU has been 

used by Google as an emulator for their Android software stack for mobile devices (next 

generation cellphones). The purpose of this project will be to add support for SystemC and 

GHDL to QEMU. The resultant Open Source system will allow the simulation of complete 

embedded systems before any hardware implementation is done. The technical challenges of 

this project include learning the three systems (SystemC, GHDL and QEMU) and subsequent 

integration of the three systems. 

8. What will be expected of the student 

The student will need to perform a detailed study of the QEMU simulator, the SystemC 

language and simulation kernel implementation, as well as the GHDL simulator. The student 

must perform the necessary integration of QEMU, GHDL and SystemC. The implementation 

must be validated by simulating a sample ARM based embedded system with non-trivial 

peripherals described in VHDL and SystemC. C/C++ and VHDL skills will be required. 

Required outcomes: Extensions to QEMU to support SystemC / GHDL integration. 

9. Resources 

Any PC with Linux OS can be used for this project. The designated work area for this project 

will be one the Linux computers in the Computer Engineering Project Lab. The Department's 

simulation clusters are available if necessary. All required software freely available on the 

Internet.


2. Project title: Altera Nios-II support for QEMU 








The Altera Nios-II soft-core processor is a 32-bit processor that is implemented (essentially) 

in VHDL. The SOPC Builder (System on a Programmable Chip Builder) that forms part of 

the Altera FPGA design suite allows this processor to be configured and synthesized for any 

suitable Altera FPGA. QEMU is an Open Source processor and peripheral simulator that 

supports a large number of CPU variants (including X86, ARM, SPARC, MIPS, PowerPC, 

etc.). Recently QEMU has been used by Google as an emulator for their Android software 

stack for mobile devices (next generation cellphones). The purpose of this project is to 

implement support for the Altera Nios-II soft-core processor in QEMU. The technical 

challenge lies in the automatic extraction of the Nios-II processor configuration from the files 

generated by the SOPC Builder, in particular aspects such as floating point support and 

custom instructions. 


The student will need to perform a detailed study of the QEMU simulator and the Altera 

Nios-II soft-core processor. The student will also need to study the intricacies of the Altera 

SOPC Builder. The student must subsequently add the necessary support to QEMU for the 

Nios-II processor. The implementation should be validated by executing sample programs 

compiled by the Nios-II GCC compiler included in the Altera Nios-II EDS. The sample 

programs must be of a sufficiently comprehensive nature. C programming skills will be 

required. Required outcomes: Extensions to QEMU to support the Altera Nios-II processor. 

9. Resources 

Any Linux based PC can be used for this project. All required software is freely available on 

the Internet. The designated work area for this project will be one the Linux computers in the 

Computer Engineering Project Lab.


2. Project title: Altera Nios-II support for LLVM 








The Altera Nios-II soft-core processor is a 32-bit processor that is implemented (essentially) 

in VHDL. The SOPC Builder (System on a Programmable Chip Builder) that forms part of 

the Altera FPGA design suite allows this processor to be configured and synthesized to any 

suitable Altera FPGA. Low Level Virtual Machine (LLVM) is a both a virtual machine with 

RISC-like instructions and a compiler infrastructure. LLVM has been used to created high 

performance compilers for various general purpose and specialized / embedded processors 

(typically FPGA based). Currently LLVM can generate static code for the following general 

purpose processors: X86, X86-64, PowerPC 32/64, ARM, Thumb, IA-64, Alpha, SPARC, 

MIPS and Cell architectures. Apple is using LLVM for various of the products, including the 

iPhone. LLVM has also been used to create a C-to-Verilog compiler (Verilog is a Hardware 

Description Language similar to VHDL). There has also been research into specialized signal 

processing language compilation using LLVM, as well as support for GPUs. Recently work 

has begun to support the Xilinx Microblaze soft-core processor and Microchip 

microcontrollers. The purpose of the project is to implement support for the Altera Nios-II 

soft-core processor in LLVM. The technical challenge lies in the detailed understanding of 

LLVM compiler system, and compilers in general, that must be gained. 


The student will need to perform a detailed study of LLVM and the Altera Nios-II soft-core 

processor. The student will also need to study the intricacies of the Altera SOPC Builder. The 

student must subsequently add the necessary support to LLVM for the Nios-II processor. The 

implementation should be validated by compiling sample programs that are cross checked 

with those produced by the Nios-II GCC compiler included in the Altera Nios-II EDS. The 

sample programs must be of a sufficiently comprehensive nature. C programming skills will 

be required. Required outcomes: Extensions to LLVM to support the Altera Nios-II 

processor. 

9. Resources 

Any Linux based PC can be used for this project. All required software is freely available on 

the Internet. The designated work area for this project will be one the Linux computers in the 

Computer Engineering Project Lab.


2. Project title: PIC16F887 VHDL core for the Altera DE1 






6.2 Required running average: Unspecified 


This project aims to synthesize a complete PIC16F887 microcontroller as a VHDL soft-core 

processor for the Altera DE1 Development and Education board. The implemented core will 

include all peripherals on the device including timers, I/O ports, memory and the register file. 

The technical challenge of the project is that implementation must allow the PICkit 2 

Development Programmer/Debugger (used in EMK310) to perform In-Circuit Programming / 

Debugging of the processor via the Expansion Headers of the DE1 board. The technical 

challenge lies in the complexity of the VHDL that will be required. 


The student will be required to study the datasheets and revise their understanding of the 

device. The student must then decompose the processor into a structural description of lower 

level blocks and synthesize these blocks behaviorally using Altera Quartus II. The complete 

microcontroller must then be assembled from the building blocks and synthesized. Care must 

be taken to parameterize the building blocks using generics so that the building blocks can be 

used to create other variants of the PIC16Fx family of processors. The student must define 

suitable testbenches for each building block as well as the system as a whole. These must be 

used to perform verification of each building block and the processor as a whole using Altera 

ModelSim. For the demonstration the student must implement at least one of the non-trivial 

practicals of EMK310 and compare the implementation used in EMK310 to that of the softcore 

implementation. VHDL skills required. An interest in microprocessor architectures is 

recommended. Required outcomes: A full VHDL description (fully commented) of a 

PIC16F887 microcontroller, together with simulator files to show the processor running a 

program.· 

9. Resources 

A PC with the full version of Altera Quartus II and Altera ModelSim installed required. The 

designated work area for this project will be one the computers in the Computer Engineering 

Project Lab. Due to the time required for the synthesis and simulation of the complete 

processor, the Department's simulation clusters are also available (with Linux versions of the 

Quartus II and ModelSim installed).


2. Project title: Multi-protocol analyzer based on the Altera DE1 








This project aims to develop a multi-protocol analyzer based on the Altera DE1 Development 

and Education board. The system must provide standard logic analyzer functionality and 

implement protocol decoding for the following standards: I2C, I2S, SPI, Async Serial, Sync 

Serial, USB, 1-Wire, PS/2, and SMBus. The technical challenge of the project lies in the realtime 

capture and analysis of the large number of protocols. USB in particular will prove 

challenging. 


The student will be required to study the various protocols to be supported. A suitable 

interface board for the Expansion Headers of the Altera DE1 board must be designed to allow 

the various bus types to be probed. The necessary firmware / software (if a soft-core processor 

is used) must be designed and implemented. A PC based Graphical User Interface (GUI) for 

the system must be developed using Qt4. The PC software must be portable to both Linux and 

Windows. C/C++ and VHDL skills required. Required outcomes: The hardware interface 

board for the Altera DE1, firmware / software for the Altera DE1 board and Linux / Windows 

GUI software. 

9. Resources 

A PC with the full version of Altera Quartus II and Altera ModelSim installed will be 

required. The designated work area for this project will be one the computers in the Computer 

Engineering Project Lab. Due to the time required for the synthesis and simulation of the 

complete processor, the Department's simulation clusters are also available (with Linux 

versions of the Quartus II and ModelSim installed). An Altera DE1 board will be made 

available. The necessary components for the hardware interface board must be sourced.


2. Project title: Ultrasonic ranging sensor module 








For small mobile robots, determining the distance to objects is a vital sense function. This 

knowledge is typically used to perform obstacle avoidance and can also be used for more 

general environment mapping. One of the techniques that can be used for this function is 

ultrasonic echolocation. This project entails the research, design and implementation of a 

compact, power efficient and modular ultrasonic ranging sensor for use on small robots. 

Power efficiency and space constraints will be the primary challenges of this project. 


There are a number of different ultrasonic methods which are used to measure the distance to 

an object. Each method has pros and cons depending on the range to an object, the type of 

object, etc. In this project, the candidate will be required to investigate various ultrasonic 

ranging methods to select the best method for sensing ranges of 2cm - 1m and accuracy of 

1cm or better. Low power consumption and compact design are critical. The module must 

include a small microcontroller and interface to a RS485 compatible communication bus. The 

microcontroller must perform suitable control and filtering of the sensing operation to 

maximize accuracy. The completed module must be subjected to detailed testing and analysis. 

The performance of the device with regards to aspects such as accuracy, distance, sensing 

latency, power consumption and efficiency must be characterized by means of formally 

designed procedures. The performance of the module must also be compared to similar 

commercial implementations. Required outcomes: modular ultrasonic ranging sensor, module 

performance quantification data. 

9. Resources 

The designated work area for this project will be one the computers in the Computer 

Engineering Project Lab. The components necessary for the sensor module must be sourced.


2. Project title: Ultrasonic imaging system 








The use of ultrasound for imaging purposes was originally proposed in the late 1920s for the 

detection of flaws in metals. One of the dominant current uses is medical ultrasound scanners. 

Research has shown the viability of using similar techniques for short range general imaging 

in air. The purpose of this project is to develop an ultrasonic scanning system that will allow 

3-D maps of the environment to be generated. The technical challenge of this project lies in 

the complexity of precision sensing with ultrasonic waves. 


The student will need to research ultrasonic imaging approaches, specifically those suitable 

for imaging in air. Based on the most feasible technique, the student must propose a design 

for a scanning system. The system should focus primarily on the ultrasonic transducers and 

imaging aspects. The necessary processing should be done on a PC. The necessary interfacing 

must therefore also be developed. Required outcomes: modular ultrasonic imaging system, 


9. Resources 


Engineering Project Lab. The necessary components for the imaging module must be sourced.


2. Project title: Low cost Inertial Navigation System 








Inertial navigation has been used in aviation and space exploration for decades. Inertial 

navigation systems (INS) have also been applied to robotic applications. INS systems are 

generally very expensive and large in size because of the high precision and reliability 

typically required in the intended applications. In contrast, for mobile robotic applications 

compact low cost implementations are required. Automotive applications have resulted in 

significant progress in the development of position and acceleration measurement devices, 

largely based on Micro-Electro-Mechanical Systems (MEMS) technology. This project entails 

the construction and characterization of a low cost INS system that can be used on small scale 

robots. The technical challenge of the project lies in the minimal budget and low power 

constraints imposed. 


The student will need to research available sensors and select suitable components based on 

formally defined specifications. A system based on the sensors must be designed and 

implementation. The completed module must be subjected to detailed testing and analysis. 

The performance of the device with regards to aspects such as accuracy, distance, sensing 

latency, power consumption and efficiency must be characterized by means of formally 

designed test procedures. Required outcomes: low cost INS module, performance 

quantification data. 

9. Resources 


Engineering Project Lab. The necessary components for the sensor module must be sourced.


2. Project title: Optical mouse sensor based dead-reckoning module 








Optical mice have progressively replaced older mechanical mice, primarily due to their 

increased sensitivity and reliability. So called “Laser mice” have switched to using infra-red 

laser diodes to further increase sensitivity. One of the problems with these approaches is that 

the mice exhibit reduced functionality on smooth uniform surfaces such as glass. This 

problem has in turn been address by a relatively new type of mice that use a technique called 

“dark field microscopy”, which allows the mice to be used on almost any surface. The 

purpose of this project is to develop an accurate localization system for mobile robots. The 

aim is to construct a dead-reckoning system based on optical mouse sensors which track 

movement of the robot across a surface. 


The student will need to research the principles of dead-reckoning, as well as optical mice 

technology. Based on this research the student must specify and procure suitable sensors. The 

student must design and implement the necessary interface and processing subsystem for the 

dead-reckoning module. The completed module must be subjected to detailed testing and 

analysis. The performance of the device with regards to aspects such as accuracy, distance, 

sensing latency, power consumption and efficiency must be characterized by means of 

formally designed qualification test procedures. Required outcomes: optical mouse sensor 

based dead-reckoning module, performance quantification data. 

9. Resources 


Engineering Project Lab. The necessary components for the sensor module must be sourced.


2. Project title: FPGA based encryption for robotic applications 








Robotic systems are often linked to a base station by means of a wireless link. For certain 

applications, this wireless link may represent a security risk. Encryption of the data transfer 

across such a link is therefore desirable. Power and processing capacity constrains place limits 

on the type of encryption possible. The aim and technical challenge of this project is to 

identify and implement the optimum encryption algorithm that can be implemented in a 

FPGA. 


The student will need to perform extensive research on encryption algorithms, particularly 

those suitable for hardware implementation. Using a suitable FPGA development board, such 

as the Altera DE1 board, the student must implement the three most promising candidates. 

The three implementations must be subjected to detailed testing and analysis. The 

performance of the implementations with regards to aspects such as power consumption, 

resource utilization and maximum bandwidth must be characterized by means of formally 

designed test procedures. Required outcomes: VHDL implementation of encryption module, 


9. Resources 






available.


2. Project title: FPGA based compression for robotic applications 








Robotic systems are often linked to a base station by means of a wireless link. For certain 

applications a video stream needs to be transferred. In such cases, a compression algorithm 

implemented in a FPGA is desirable. Power and processing capacity constrains place limits 

on the type of compression possible. The aim and technical challenge of this project is to 

identify and prototype the optimum compression algorithm that can be implemented in an 

FPGA. 


The student will need to perform extensive research on compression algorithms, particularly 

those suitable for hardware implementation. Using a suitable FPGA development board, such 

as the Altera DE1 board, the student must implement the three most promising candidates. 

The three implementations must be subjected to detailed testing and analysis. The 

performance of the implementations with regards to aspects such as power consumption, 

resource utilization and maximum bandwidth must be characterized by means of formally 

designed qualification test procedures. Required outcomes: VHDL implementation of 

compression module, performance quantification data. 

9. Resources 






available.


2. Project title: Integration of autonomous vehicle 








The US Government sponsored DARPA Grand Challenge (http://www.darpa.org) aims to 

stimulate the development of mobile intelligent autonomous systems. In 2008 the South 

African branch of National Instruments initiated a similar competition, albeit on a smaller 

scale. During 2008 and 2009 final year projects entailed the development of components of 

two robotic platforms to meet the requirements of the 2008 and 2009 competitions. This year 

a new competition will be held, with more open specifications. This project will refine the 

previously developed components and integrate them into an autonomous vehicle according 

to the rules of the new competition. 


The student will be required to perform a detailed analysis of the subsystems that have been 

previously developed. Similarly, the specification of the new competition must be analyzed. 

Based on this analysis the student must identify the subsystem refinements required and 

addition modifications for integration. In particular the navigation and path planning system 

must be ported from a Matlab based system to a suitable embedded processor. Required 

outcomes: A fully functional autonomous vehicle verified to achieve the goals of the 

competition. 

9. Resources 

To be determined as part of the project. The designated work area for this project is the CAEC 

lab.


2. Project title: Stereo vision system for robotic vehicles 


4. Research group: Intelligent Systems 



6.1 Intended degree programme: Electronic Engineering 



The US Government sponsored DARPA Grand Challenge (http://www.darpa.org) aims to 

stimulate the development of mobile intelligent autonomous systems. In 2008 the South 

African branch of National Instruments initiated a similar competition, albeit on a smaller 

scale. During 2008 and 2009 final year projects entailed the development of components of 

two robotic platforms to meet the requirements of the 2008 and 2009 competitions. This year 

a new competition will be held, with more open specifications. During 2009 a Matlab based 

stereo vision system was developed for the vehicle. This project will refine the previously 

developed implementation and create a low-power stereo vision processing system. 


The student will be required to perform a detailed analysis of the subsystems that have been 

previously developed. Based on this analysis the student must identify a suitable platform for 

the implementation of the stereo vision algorithms (for example FPGA, DSP, etc.). Required 

outcomes: A stereo vision module which produces a distance map as output. 

9. Resources 

To be determined as part of the project. The designated work area for this project is the CAEC 

lab.

1. Project number: T14 

2. Project title: MCQ assessment system 





6.1 Intended degree programme: Computer Engineering 



Growth in student numbers has increase the workload of lecturers with respect to assessments. 

Multiple Choice Questions (MCQ) can be used to reduce the time spend on marking. The aim 

of this project is to develop a computer based MCQ assessment system that can be used in 

two modes of operation: a) online web-based assessments and b) offline paper based 

assessments. For the online assessments, the system must allow predefined MCQ assessments 

to be performed in a computer laboratory context. For the offline assessments, the system 

must generate the appropriate MCQ answer sheets. Once the answer sheets have been 

completed, the sheets are to be scan to a multi-page Portable Document Format (PDF) using a 

digital multifunction copier/scanner/printer (such as the Ricoh Aficio MP 6500). The system 

must accept such a PDF via Simple Message Transfer Protocol (SMTP) and perform Optical 

Mark Recognition (OMR) to extract the answers. A SQL database system should be used for 

the storage of marks and other data. For both assessment approaches, the system must 

perform the necessary mark evaluation, statistical analysis and report generation. The system 

must support all required question weight assignment profiles and must allow the extracted 

answers to be exported in various formats (for example: raw and CSV). The primary 

challenge of this project lies in the development of a robust (zero error) OMR subsystem than 

can handle both standard and generated OMR sheets. The secondary challenge is the 

integration of all the subsystems into a highly reliable system usable by non-technical users. 


The student must perform a literature study and research into existing implementations of 

OMR systems. In order to develop the specified system from first principles, the student will 

need to research the digital image processing and pattern recognition potentially suitable for 

OMR. The student must create a prototype OMR implementation and use TIFF based scans 

to test the functionality. Subsequently the student must incorporate a PDF based page 

extraction mechanism in order to handle multi-page PDF documents. The OMR and PDF 

subsystems must be integrated with a SMTP mail transfer agent (MTA) subsystem to create a 

stand-alone server daemon. The system must store received PDF in a properly designed 

hierarchical filesystem structure. The document meta-data and processed results must be 

stored in a SQL database. A WWW based front-end must be created (in PHP or Ruby) for the 

system which allows non-technical users to operate the system. 

9. Resources 

To complete this project the student will need a Linux based computer. The dual-boot 

computers in the Computer Engineering Project Labs should be used. The compilers (GCC), 

libraries, database systems (PostgreSQL), WWW servers and script engines (PHP/Ruby) 

which come standard with Debian / Ubuntu are sufficient for this project. Sample PDF 

documents can be generated with minimal effort using the department's multifunctional 

copier/scanner/printer.


2. Project title: De-weathering / de-noising of video sequences 



5. Focus of project: Investigative 





Surveillance videos are often distorted by environmental effects such as rain and varying light 

conditions. Recently research has produced algorithms and techniques to compensate for 

specific types of (environmental) noise in images. These include approaches based on 

physical models and purely signal processing based methods. For this project the signal 

processing based methods must be researched and implemented to create a dynamic deweathering 

/ de-noising video sequence pre-processor. Amongst other, Discrete Fourier 

Transform (DFT) and Discrete Wavelet Transform (DWT) based methods must be research 

and implemented. Recently the Discrete Curvelet Transform (DCuT) has shown considerable 

promise with regards to de-weathering / de-noising and the DCuT must therefore also be 

researched and implemented. 


The student is required to design a dynamic de-weathering / de-noising video sequence preprocessor. 

Strong Mathematical, Signal Processing and C++ programming skills will be 

required. The student will be expected to follow a formal engineering procedure 

(requirements analysis, specification, architecture, design, implementation, testing and 

configuration management). Required outcomes: A fully functional system with associated 

technical and user documentation. 

9. Resources 

The Open Source OpenCV image processing toolkit will be used as foundation. The Qt4 

library will be used for GUI aspects. The designated work area for this project will be one the 

Linux computers in the Computer Engineering Project Lab.


2. Project title: Particle flow analysis in micro-fluidic channels 

3. Study leader: Mr. H Grobler, in collaboration with CSIR (MSM) 







The Mechatronics and Micro-Manufacturing group at the CSIR is involved in research 

towards the application of Micro-fluidics in manufacturing and medicine. This is achieved by 

exploiting the unique physical properties of fluids and particles at the micro scale. Micro 

channels are cut within a polymer substrate and fluid and particles are allowed to flow 

through. As part of the research it is important to measure various parameters of the flow. 

Currently, high-speed videos of the channels are captured and manually analyzed offline. It is 

desirable to automate the analysis. Therefore the aim of the project is to construct a software 

system that will read the video data and use image processing and computer vision techniques 

to make various quantitative measurements about the fluid flow and the particles. Some 

examples of the kind of measurements are: Measure the flow rate of the fluid by identifying 

and tracking known particles that are injected into the fluid. Classify/segment amongst the 

different particles in the channel. Measure the velocity, trajectory, size, identity and path of 

all particles and annotate the video feed with this information as per user requirement. 


The desired output is a software implementation of all the necessary algorithms to perform the 

measurements and a user friendly interface that allows access to their functionality. The 

system must allow the user to view and extract the information about the particle and/or fluid 

flow as required. Detailed user requirements should be obtained from the Mechatronics and 

Micro-Manufacturing research group. 

9. Resources 


Engineering Project Lab. The Open Source OpenCV image processing toolkit will be used as 

foundation and the Qt4 library used for GUI aspects. High-speed video data will be supplied 

by the Mechatronics and Micro-Manufacturing research group.


2. Project title: Road segmentation and structure analysis for an 

autonomous rover 

3. Study leader: Mr. H Grobler, in collaboration with CSIR (MIAS) 







Autonomous unmanned ground vehicles (AUGVs, also known as smart cars) are autonomous 

robots that operate on ground surfaces. To safely navigate under normal traffic conditions, it 

is necessary for such vehicles to build a dense map of the road surface. Using a video feed 

provided by cameras on board the vehicle, the system should identify the road surface present 

in the video feed and augment its three-dimensional model of the road surface. The threedimensional 

model will be used to for navigating the vehicle. In addition, important properties 

of the road surface is to be identified, such as traffic lanes, the road shoulder, curbs, the 

presence of humps, potholes, etc. Currently only tar road surfaces are considered. 


The student is required to design and implement a software system that uses advanced image 

processing and computer vision techniques to create a three-dimensional model of the road 

surface across the entire length of road that the vehicle traverses, and to augment the model 

with important road surface information such as traffic lanes, curbs, humps, potholes, etc. 

Furthermore the three-dimensional model with augmented data is to be visualized as part of 

the software. Strong C++ programming skills will be required. The student will be expected 

to follow a formal engineering procedure (requirements analysis, specification, architecture, 

design, implementation, testing and configuration management). Required outcomes: A fully 

functional system (Open Source, ANSI C++) with associated technical and user 

documentation. 

9. Resources 


library will be used for GUI aspects. Data will be supplied by the Mobile Intelligent 

Autonomous Systems research group in the CSIR. The designated work area for this project 

will be one the Linux computers in the Computer Engineering Project Lab. For specialised 

experiments the facilities at the Computer Vision Laboratory (CSIR) will be available.


2. Project title: Large scale object recognition 








With the phenomenal growth of the Internet over the large decade, it is estimated that there 

are billions of images available on the Internet. These images cover just about any 

conceivable topic. Recent studies indicate that by incorporating such vast amounts of 

information in an object recognition system, the results can be vastly improved. In this 

project, the student is required to develop a system that can query for and download a large 

number labeled (context based) images from the web (using search engines such as Google or 

Yahoo, and sharing sites such as MySpace, Facebook or Flickr). These images are to cover 

the complete spectrum of perceivable objects (for such purposes, a tool such as WordNet can 

be used). Once such a database is established, computer vision and machine learning 

techniques are to be applied (for example using SIFT features) to associate the relevant parts 

in the images with category labels (content based) and to reject labels where no strong 

correspondence between images can be found. Using the improved database, a system is to 

be designed that uses a standard object recognition technique to classify all object present in 

an image presented to it. 


The student is required to design a software system that extracts a vast number of images 

from the Internet and uses image processing and computer vision techniques to make 

associations between the images. The system should use these associations to recognize 

objects when presented an image. A user interface that displays the image, and highlights 

recognized objects in the image should be developed. Strong C++ programming skills will be 

required. The student will be expected to follow a formal engineering procedure 

(requirements analysis, specification, architecture, design, implementation, testing and 

configuration management). Required outcomes: A fully functional system (Open Source, 

ANSI C++) and with associated technical and user documentation. 

9. Resources 


library will be used for GUI aspects. Image data is to be acquired as part of the project. The 

designated work area for this project will be one the Linux computers in the Computer 

Engineering Project Lab. For specialized experiments the facilities at the Computer Vision 

Laboratory (CSIR) will be available.


2. Project title: 3D modelling and chroma keying studio 








In this project, the student is required to build a workbench that can be used to create 3D 

models of small objects. 3D models are often used in computer graphics in applications such 

as movies and games. It is also useful in the domain of robotics, to give machines the ability 

to recognise objects and plan tasks based on their pose. 


In the first part of the project, a small workbench for 3D modeling needs to be set up. The 

workbench should allow an object to be placed in such a way that it is positioned against a 

uniform background. The system should allow images of the object to be taken from multiple 

viewpoints, by rotating the object on a small turntable and taking images from a stationary 

mounted camera. Adequate lighting should be provided as part of the system. In the second 

part of the project, algorithms for chroma keying (greenscreening) need to be developed. 

These algorithms should make it possible to segment an object from a uniform background 

and then to insert the segmented object on another background. In the third part of the project, 

algorithms need to be developed to extract feature points from objects across multiple views, 

and to use that information to construct 3D models of the objects. The 3D models need to be 

validated against ground truth (there is a Riegl Laser Scanner at the CSIR that could be used 

for this purpose). A GUI need to be developed to visualise the 3D objects (supporting 

different modes, such point-clouds, wire-frames and textured models) and to project them 

onto a different background. 

9. Resources 

A set of small objects will be provided to demonstrate the system, ranging from simple to 

complex geometric shapes. A web camera could be made available upon request. The CSIR’s 

Riegl laser scanner will be made available to acquire ground truth measurements. A turntable 

will be required and should be sourced as part of an old LP player or similar equipment. 

Additional funding will be available if necessary.


2. Project title: Real-time object recognition using a GPU 








The availability of relatively cheap Graphical Processing Units (GPUs), having the ability to 

parallelise execution of certain algorithms, have made teraflop computing accessible to a 

larger audience. The availability of such computational power has made many new 

applications possible. One such example is in the field of computer vision, where large 

amounts of visual data have to be processed. In this project, the objective is to develop a 

system that can recognise objects in real-time, by implementing common feature extraction 

and description algorithms on a GPU. Such algorithms include Scale Invariant Feature 

Transform (SIFT), Speeded Up Robust Features (SURF), Gradient Location and Orientation 

Histogram (GLOH), etc. The primary focus and technical challenge of this project is the 

development of a real-time GPU based object recognition system. 


In the first part of the project, algorithms for feature point extraction, feature description and 

feature matching will be implemented on the GPU, using a framework such as CUDA. A 

variety of popular algorithms need to be implemented. In the second part of the project, a 

database of features of common objects needs to be created. For this purpose, popular image 

datasets such as PASCAL VOC, MSRC and Caltech should be used. A method need to be 

developed to represent such a database on a GPU, such that it could easily be referenced and 

match scores calculated. In the third part of the project, a GUI needs to be developed. The 

GUI should allow a user to select an image from disk, and then determine the objects present 

in the image by extracting features from it and matching it to the database. The image and 

associated objects should be displayed in the GUI. The recognition capability of the system 

should be characterized. C++ programming skills will be required. Required outcomes: 

properly designed / implemented and fully functional real-time object recognition system, 

detailed experimental analysis of various recognition algorithms implemented. 

9. Resources 

The Open Source OpenCV image processing toolkit will be used as foundation and the Qt4 

library for GUI aspects. A PC with NVidia GTX 295 will be available for this project. The 

required software (such as CUDA) can be downloaded from the Internet, but will also be 

made available on the department's FTP site. Image datasets such as PASCAL VOC, MSRC 

and Caltech will be made available.


2. Project title: Real-time video encoding/decoding using natural 

image statistics 








Video is becoming a very important resource in online media and Internet culture. On online 

sharing sites such as YouTube, it is estimated that there are hundreds of millions of videos, 

with hundreds of thousands being added daily. Due to the large amount of visual data, 

efficient encoding and decoding of such videos is required due to limitations in bandwidth. 

Classically, encoding / decoding algorithms use only the content of the video itself. 

However, due to the large amount of visual data that is currently available, it may be possible 

to construct a database containing natural image statistics that is shared and referenced 

between the encoder and decoder. By creating indices into the shared data, the video could be 

encoded and compressed efficiently using only such indices. The technical challenge of this 

project is to create a clever indexing structure that could be used to encode / decode video in 

real-time with minimal loss in visual appearance and maximum compression. 


The student will be required to study the principles of image and video compression. In the 

first part of the project, a database of natural image statistics is to be constructed. Datasets 

available online, such as PASCAL, MSRC or Caltech could be used, or videos that is 

currently available on YouTube could be assembled. Statistics based on image patches, 

frequency components, etc. could be gathered from such data. Similar patterns could be 

clustered to reduce the size of the database. In the second part of the project, a distance 

measure needs to be developed to select a sample from the database that is closest in visual 

appearance to a target sample. This would require an analysis of humans perceive visual 

information. In the third part of the project, an efficient encoder / decoder is to be developed, 

based on the database and distance measure. The encoder / decoder should be able to operate 

in real time. A GUI should be developed for viewing and managing the encoding/decoding of 

videos and images. Models need to be developed describing the “loss of visual information” 

in the encoded visual data. The student is required to design software to construct the 

database, distance measure and encoding / decoding scheme and a GUI to manage the 

process. C++ programming skills will be required. Required outcomes: properly designed / 

implemented and fully functional real-time video encoding/decoding system, detailed 

experimental analysis of system performance. 

9. Resources 


library for GUI aspects. The designated work area for this project will be one the computers 

in the Computer Engineering Project Lab. Datasets with visual information are to be acquired 

from the Internet. The datasets need to be representative of a large number of visual 

circumstances that can occur. A set of videos will be provided that are to be encoded/decoded.


2. Project title: Map-building of an office environment 








Accurate map-building is an important task for mobile robots. It enables robots to be able to 

plan routes and move about in complex human environments, lessening the danger it poses to 

itself or its environment. The objective is to build a map of office environment, using only a 

stereo vision camera mounted on a small robot. The map should be geometric rather than 

topological in nature. The focus and technical challenge of this project is the development of 

stereo vision system where accuracy is the primary consideration (at the cost of high memory 

usage and computational requirements). 


The student is required to develop algorithms to perform stereo vision to construct an accurate 

map of an office environment. In the first part of the project, algorithms need to be developed 

to rectify images, do distortion correction to get rid of lens effects and perform feature 

matching across pairs of images (i.e. perform stereo vision). Using disparity information, 

distances to objects in the images should be calculated. In the second part of the project, 

information from pairs of images in an entire video sequence should be used to construct a 

map of the environment depicted in the video. In the third part of the project, a GUI should be 

developed to visualise the constructed map. The accuracy of the constructed map against 

ground truth should be calculated. The stereo datasets and ground truth must be captured 

according to formally designed procedures. C++ programming skills will be required. 

Required outcomes: properly designed / implemented and fully functional stereo vision 

system, detailed experimental analysis of system performance, high quality stereo vision 

datasets. 

9. Resources 



in the Computer Engineering Project Lab. A high resolution stereo vision camera (CSIR) will 

be used to capture videos of an office environment. The student will have to do the 

experimental setup to capture all relevant data, such as calibration information, etc.


2. Project title: Content based video/image retrieval system 








The availability of cheap digital cameras and the emergence and strong support of social 

networking websites, have created a situation where billions of images and videos are 

available on the Internet, with millions being added daily. These images and videos cover 

just about any conceivable topic. Most current search engines don’t index these images and 

videos based on their content, but rather based on text that is found near these images / videos 

on web pages. Although such search engines generally perform well for simple queries such 

as “face”, “horse” or “boat”, it fails for more complex queries such as “red boat sailing into 

the sunset” or “lady in pink dress walking on the beach”. The aim of this project is to develop 

a search engine capable of indexing images / videos based on their content, rather than textual 

descriptions found on web pages. Analysis of image and video data is also vital in activities 

such as strategy determination, tactics development, disaster recovery or mission debriefing. 

The system should therefore also be able to search images and videos for a specific object. 


In the first part of the project an image and video web crawler should be developed. It should 

create an index with common image / video properties, such as URL, dimensions, etc. The 

crawler should be run to assemble a large collection of images and videos. The crawler may 

be structured to use additional information, such as the structure of Google Image Search 

queries, Facebook image naming conventions, etc. to help in the construction of such a 

dataset. In the second part of the project, algorithms need to be developed to analyse these 

images / videos based on their content. To limit the scope of the project, the analysis should 

be restricted to the assignment of class categories, and simple features such as the colour 

content. In the third part of the project, a web interface needs to be developed to illustrate the 

content-based search engine. The user should be able to enter a search term, upon which the 

user interface provides an overview of the images and videos in the database that match the 

search term based on content. 

9. Resources 


Engineering Project Lab. The Open Source OpenCV image processing toolkit will be used as 

foundation. Web development packages such as WAMP or LAMP can be downloaded from 

the Internet (they are also available on the department's FTP server). Datasets with visual 

information are to be acquired from the Internet. The datasets need to be representative of a 

large number of visual circumstances that can occur.


2. Project title: High performance real-time stereo vision system 








The goal of computer vision is to infer information about the real world from images. One of 

the major applications in computer vision is three-dimensional reconstruction. This has been a 

widely researched area and there are a number of algorithms that exist such as stereopsis, 

structure from motion, volumetric graph cuts, etc. Most of these algorithms are computational 

intensive and extremely slow. Recently progress has been made in speeding up stereo vision 

algorithms. Stereo vision requires two images to be taken of the same scene but separated by 

a certain distance. By calculating the difference in distance between the same pixels present in 

both images, one can calculate the depth information, known as the disparity. Using the 

disparity information, 3D reconstruction of the scene can be done. The primary focus and 

technical challenge of this project is the development of a real-time stereo vision system 

based on disparity information. To maximise the frame rate at which the system operates, not 

only must improved algorithms be implemented, various techniques such as multi-core 

operation and GPU off-loading must be explored. 


The student is required to design and implement a software system that uses image processing 

and computer vision techniques to produce a stereo vision system that can calculate the depth 

information and produce a visualisation of the scene in real-time. The student will therefore 

have to research the principles of stereo vision and in particular the methods relating to 

disparity. A reference implementation must be done and validated. Subsequently the student 

must study various performance profiling tools / techniques and use these to analyse the 

performance of the implementation. Based on the results, the student must investigate various 

techniques such as multi-core based parallel processing and GPU off-loading to maximise the 

frame rate achieved by the system. To achieve the latter, the student will be required to study 

multi-core programming approaches such as OpenMP and the Intel Threading Building 

Blocks (TBB), as well as GPU programming API such as CUDA. C++ programming skills 

will be required. Required outcomes: properly designed / implemented and fully functional 

real-time stereo vision system, detailed experimental analysis of various acceleration 

techniques implemented. 

9. Resources 



in the Computer Engineering Project Lab. For GPU related work, a PC with a NVidia GTX 

295 will be available. Stereo images datasets will be made available (such as the Middlebury 

Stereo Datasets) and additional datasets should be captured at the Computer Vision 

Laboratory (CSIR).


2. Project title: Visual odometry for an autonomous vehicle 








Navigating in an unknown environment is a key task for an autonomous vehicle. Cameras 

mounted on the vehicle offer low cost, high information content sensors that are eminently 

suitable for human environments. The camera captures images which can be used to 

determine the visual odometry of the vehicle. The main goal of visual odometry is to recover 

the camera motion and the 3D structure of the world concurrently by exploiting the projective 

geometry relating multiple views. Structure from Motion (SfM) is one of the algorithms used 

to determine the visual odometry. SfM refers to the process of finding the three-dimensional 

structure by analysing the motion of an object over time. Different features such as corners, 

edges, etc. are tracked in sequential images to find the correspondences between them. The 

feature trajectories over time are then used to reconstruct their 3D positions as well as the 

camera motion. The primary focus and technical challenge of this project is the development 

of a real-time visual odometry system. To maximise the frame rate at which the system 

operates, various optimisations must be explored. 


The student is required to design and implement a software system that implements Structure 

from Motion to accurately determine the visual odometry of an autonomous vehicle as well as 

produce a 3D visualization of the path traveled. The student will therefore be required to 

study the topic Structure from Motion, as well as various techniques from the field of digital 

image processing. A reference implementation must be done and validated. Subsequently the 

student must study various performance profiling tools / techniques and use these to analyse 

the performance of the implementation. Based on the results, the student must investigate 

various algorithmic and implementation optimisations to maximise the frame rate achieved by 

the system. C++ programming skills will be required. Required outcomes: properly designed 

/ implemented and fully functional real-time visual odometry system, detailed experimental 

analysis of various optimisations implemented. 

9. Resources 



in the Computer Engineering Project Lab. The student may use facilities available at the 

Computer Vision Laboratory (CSIR) to capture suitable data.


2. Project title: 2D-3D pose estimation 








Pose estimation of an object refers to the object’s position and orientation relative to some coordinate 

system. Determining the pose of an object is vital to many computer and robot vision 

tasks such as object grasping, manipulation and recognition or self-localization of mobile 

robots. The 2D-3D pose estimation problem requires the fitting of 2D sensor data (an image 

of an object) with a 3D object model. The aim is to estimate a rigid motion (containing both 

3D rotation and 3D translation) which minimises an error measure (which needs to be 

defined) between the image and object data. The primary focus and technical challenge of this 

project is the development of an accurate pose estimation system. 


The student is required to design and implement a software system that addresses the 2D-3D 

pose estimation problem and minimises the error measure. The student will therefore be 

required to study the topics of pose estimation, structure from motion and model based vision. 

A reference implementation must be done and validated. A detailed analysis of the 

performance of system must be done. The results must be compared to published results. 

Potential improvements must be identified, implemented and analysed. C++ programming 

skills will be required. Required outcomes: properly designed / implemented and fully 

functional pose estimation system, detailed experimental analysis of the performance of the 

system as improvements are integrated. 

9. Resources 


library for GUI aspects. The designated work area for this project will be one the Linux 

computers in the Computer Engineering Project Lab. The student may use facilities available 

at the Computer Vision Laboratory (CSIR) to capture suitable data.


2. Project title: GPU algorithmic implementation 

3. Study leader: Mr. H Grobler, in collaboration with CSIR (OSS) 







Many algorithms have been ported to run on Graphics Processing Units (GPUs) to take 

advantage of its parallel processing performance benefits. The aim of this project is to create a 

GPU accelerated suite of image processing algorithms. It is expected that the student will 

implement the algorithms listed below both in CPU code and GPU code and then compare the 

processing speeds obtained on at least three different CPUs and four different GPUs. Note 

that two types of GPU programs exist, vertex shaders, and fragment shaders, unless otherwise 

stated both should be implemented unless sound justification is provided. The suite should 

include at least the following algorithms implemented from first principles: 

• Local Histogram: Many algorithms require knowledge of the distribution of 

intensities around a given pixel position. 

• Intensity Statistics: The average and standard deviation of the pixels in a specified 

neighbourhood are frequently required by high level algorithms. 

• FFT: Translating a picture to the frequency domain allows many types of processing 

(such as convolution filtering) to be performed more rapidly. 

• RGB-HLS: Converting from the traditional colour triplet to Hue-Light-Saturation 

representation is useful for many segmentation applications. 

• HLS-RGB Similar to the above, being able to convert back to the traditional colour 

triplet is required. 


The student will be required to study the various algorithms to be implemented. A C/C++ 

implementation of each algorithm must be developed and thoroughly tested. Using a 

framework such as CUDA, the student must create GPU implementations of each algorithm. 

Using a formal experimental design, the performance of each implementation on various 

CPUs and GPUs must be characterized and analysed. C/C++ programming skills will be 

required. Required outcomes: GPU implementation of various image processing algorithms 

and reference implementation in C/C++. 

9. Resources 


library for GUI aspects. A PC with NVidia GTX 295 will be available for this project. The 

required software (such as CUDA) can be downloaded from the Internet, but will also be 

made available on the department's FTP site.


2. Project title: Heat shimmer modeling, characterization and 

correction 








In images obtained from a camera some distance away from observed objects, warm days can 

induce a mirage like effect which distorts the images. Similar effects can be observed both 

over land and over open water and for distances as short as only a few hundred meters. One 

technique that has been used to produce clear images is super-resolution imaging. Superresolution 

(SR) imaging entails the combination of images with slight offset to produce 

images of higher resolution. The aim of this project is to create a image processing module 

that uses SR techniques to correct for heat shimmer effects. 


The student will be expected to study general image processing and specifically SR 

techniques. The student will be required to determine what differences, if any, exist between 

the distortion over land and water. Using SR techniques, an algorithm which creates a more 

focused image in the presence of such noise must be developed. The performance of the 

implementation must be characterization on suitable datasets. Required outcomes: 

Implementation of head shimmer correction module. 

9. Resources 



in the Computer Engineering Project Lab. Video datasets will be supplied.


2. Project title: Background subtraction in a foliage environment 








The problem of 24 hour surveillance in an uncontrolled environment is pervasive problem. 

Due to the random movement of foliage in images captured in savanna environments, 

extracting the foreground is a very difficult problem. It is required that the student determine 

which of the current segmentation algorithms would be the most effective in a savanna 

environment where the dynamic environment consists primarily of low foliage moving due to 

the wind. 


The student will be expected to study general image processing and background subtraction 

techniques. As background subtraction is required in almost all digital image processing, a 

substantial amount of research exists. The student will be required to systematically explore 

the various techniques to determine which of the techniques produces best results for the 

problem of background subtraction in the presence of foliage. The student must implement 

the selected method as a comprehensive OpenCV extension. The performance of the 

implementation must be characterization on suitable datasets. Required outcomes: OpenCV 

implementation of background subtraction module. 

9. Resources 



in the Computer Engineering Project Lab. Video datasets will be supplied and additional 

datasets can be captured.


2. Project title: Identification of stable regions in video sequences 








It is just as important to know which parts of an image do not change as to know which parts 

do. Ideally these two sets are mutually exclusive yet constitute the entire image. In practice it 

might be more feasible to add a third category where the image might be moving. The stable 

parts of the image are useful both for image compression purposes as well as for stabilization 

of the input image. It is proposed that a student researches, develops/refines and tests/verifies 

a suitable algorithm to quickly determine which parts of a video sequence are likely to be 

stable for the next few frames. Once this has been done the motion of the camera over time, 

relative to the scene being viewed, is to be determined not necessarily by making use of the 

identified stable regions. 


The student will be expected to study general image processing and scene analysis. The 

identification of stable regions in video sequences has been researched and techniques such as 

Efficient Maximally Stable Extremal Region (MSER) tracking have been developed. The 

student must study this and other techniques and determine which of the techniques produces 

best results. The student must implement the selected method as a comprehensive OpenCV 

extension. The performance of the implementation must be characterization on suitable 

datasets. Required outcomes: OpenCV implementation of stable region tracking module. 

9. Resources 



in the Computer Engineering Project Lab. Video datasets will be supplied.


2. Project title: Distributed object identification and tracking 








It is unlikely that a single camera would be able to cover the entire required field of view with 

sufficient resolution to detect all possible targets. Thus it is probable that multiple cameras 

will be used in most surveillance applications. It is required to be able to combine the outputs 

of these cameras into a distributed surveillance system. Objects in these multiple feeds need 

to be identified and tracked across the various feeds. The technical challenge of this project 

will be to represent the identified objects in a non-pixel based reference system that is 

synchronised across multiple video streams. 


The student will be expected to study general image processing and scene analysis. Object 

recognition and tracking has been researched. The student must study the research and 

identify techniques that can be adapted to operation on multiple video streams. The student 

must implement the selected method as a comprehensive OpenCV extension. The 

performance of the implementation must be characterization on suitable datasets. Required 

outcomes: OpenCV implementation of distributed object identification and tracking module. 

9. Resources 



in the Computer Engineering Project Lab. Video datasets will need to be captured using, for 

example, web cameras.

Research Groups - Electrical, Electronic and Computer Engineering

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