PhD Studentship – Real-time 3D Computer Vision and Image Processing for
Automotive and Autonomous Robotics Applications
Automotive and Autonomous Robotics Applications
Applied Mathematics and Computing Group
School of Engineering, Cranfield University, MK43 0AL, UK.
Supervisor: Dr Toby Breckon Award type: PhD
Duration of award: 3.5 Years (excluding 6 month initial placement with
industrial sponsor)
Applications are invited for a fully funded PhD student to work in the
School of Engineering on the topic of developing real-time 3D computer
vision techniques within the area of autonomous robotic guidance and
automotive driver assistance systems in collaboration with TRW Conekt
(http://www.conekt.co.uk/).
School of Engineering, Cranfield University, MK43 0AL, UK.
Supervisor: Dr Toby Breckon Award type: PhD
Duration of award: 3.5 Years (excluding 6 month initial placement with
industrial sponsor)
Applications are invited for a fully funded PhD student to work in the
School of Engineering on the topic of developing real-time 3D computer
vision techniques within the area of autonomous robotic guidance and
automotive driver assistance systems in collaboration with TRW Conekt
(http://www.conekt.co.uk/).
Intelligent vehicle sensing has seen increasing interest and application
over the past decades with a wide range of applications in both driver
assistance systems and autonomous guidance systems alike. This PhD
project builds upon the ongoing theme of work within the Cranfield
research team on intelligent vehicle sensing such autonomous route
proving, road sign detection, headlight tracking, GPS navigation
augmentation and automotive stereo vision. This project specifically
looks to leverage recent advances in real-time video mosaicking and
automotive stereo vision for application to real-time on-vehicle 3D
vision for autonomous navigation and scene understanding. The project
will take the form of an industrial CASE studentship with the student
primarily based at Cranfield University whilst working at TRW Conekt
premises for placement periods throughout the PhD – including an initial
6-month placement at their industrial research base in Solihull. This
represents a very good opportunity for a candidate interested in an
applied research career, comprising both academic and industrial
placement elements within the 3.5 year study programme with an initial
pre-project 6-month industrial placement.
over the past decades with a wide range of applications in both driver
assistance systems and autonomous guidance systems alike. This PhD
project builds upon the ongoing theme of work within the Cranfield
research team on intelligent vehicle sensing such autonomous route
proving, road sign detection, headlight tracking, GPS navigation
augmentation and automotive stereo vision. This project specifically
looks to leverage recent advances in real-time video mosaicking and
automotive stereo vision for application to real-time on-vehicle 3D
vision for autonomous navigation and scene understanding. The project
will take the form of an industrial CASE studentship with the student
primarily based at Cranfield University whilst working at TRW Conekt
premises for placement periods throughout the PhD – including an initial
6-month placement at their industrial research base in Solihull. This
represents a very good opportunity for a candidate interested in an
applied research career, comprising both academic and industrial
placement elements within the 3.5 year study programme with an initial
pre-project 6-month industrial placement.
TRW Conekt is an operating business within TRW Automotive, a Tier 1
global supplier of braking and steering systems, airbags and driver
assistance systems in the automotive market. Conekt specialises in
embedded vehicle sensing and control systems, playing a leading role in
the research and development of radar, vision sensing and data fusion
solutions for future autonomous vehicles and driver assistance systems.
Active in UK and European collaborative research programmes since the
early 1990s, Conekt is currently engaged in at least six programmes with
a vision systems theme for defence, automotive and intelligent
transportation systems applications (http://www.conekt.co.uk/).
An overview of related research work within the Cranfield team is
available from http://www.cranfield.ac.uk/~toby.breckon/demos/ which
illustrates both work within the automotive vision domain and other
computer vision / image processing projects in the group.
global supplier of braking and steering systems, airbags and driver
assistance systems in the automotive market. Conekt specialises in
embedded vehicle sensing and control systems, playing a leading role in
the research and development of radar, vision sensing and data fusion
solutions for future autonomous vehicles and driver assistance systems.
Active in UK and European collaborative research programmes since the
early 1990s, Conekt is currently engaged in at least six programmes with
a vision systems theme for defence, automotive and intelligent
transportation systems applications (http://www.conekt.co.uk/).
An overview of related research work within the Cranfield team is
available from http://www.cranfield.ac.uk/~toby.breckon/demos/ which
illustrates both work within the automotive vision domain and other
computer vision / image processing projects in the group.
The Applied Mathematics and Computing Group (AMAC) is part of the School
of Engineering at Cranfield University. It specialises in the
application of mathematical and computational techniques to engineering
problems – including the domain of applied image and signal processing.
AMAC has been involved in image processing activities for ~20 years.
Prior projects include real-time night-vision systems for major
automotive manufacturers, pan-European driver monitoring/awareness
projects together with a range of medical vision and security
surveillance work. Recent work includes 3D computer vision for object
recognition and inspection, automated ground robot and aerial target
detection (MOD Grand Challenge Winners 2008, Stellar Team รข URL:
http://www.science.mod.uk/engagement/grand_challenge/grand_challenge.aspx),
airport baggage security screening and on-going automotive computer
vision using low-cost on-board sensors. In general we are concerned with
the novel application of image processing and computer vision approaches
for the effective extraction of visual information from images.
of Engineering at Cranfield University. It specialises in the
application of mathematical and computational techniques to engineering
problems – including the domain of applied image and signal processing.
AMAC has been involved in image processing activities for ~20 years.
Prior projects include real-time night-vision systems for major
automotive manufacturers, pan-European driver monitoring/awareness
projects together with a range of medical vision and security
surveillance work. Recent work includes 3D computer vision for object
recognition and inspection, automated ground robot and aerial target
detection (MOD Grand Challenge Winners 2008, Stellar Team รข URL:
http://www.science.mod.uk/engagement/grand_challenge/grand_challenge.aspx),
airport baggage security screening and on-going automotive computer
vision using low-cost on-board sensors. In general we are concerned with
the novel application of image processing and computer vision approaches
for the effective extraction of visual information from images.
Entry Requirements: Applicants should have good background in any of
computer science, artificial intelligence, electrical engineering or a
related discipline with a strong programming ability in a high level
language (preferably C/C++, Java or Matlab). Candidates should hold at
least a 2:1 honours degree or equivalent in Computer Science, Electrical
Engineering or a related discipline (Masters degree a plus). In
addition applicants would ideally have a good knowledge of mathematics
(especially algebra, statistics and geometry) and a strong motivation
towards PhD study. Prior experience in computer vision, image processing
and/or machine learning is a plus although not essential.
computer science, artificial intelligence, electrical engineering or a
related discipline with a strong programming ability in a high level
language (preferably C/C++, Java or Matlab). Candidates should hold at
least a 2:1 honours degree or equivalent in Computer Science, Electrical
Engineering or a related discipline (Masters degree a plus). In
addition applicants would ideally have a good knowledge of mathematics
(especially algebra, statistics and geometry) and a strong motivation
towards PhD study. Prior experience in computer vision, image processing
and/or machine learning is a plus although not essential.
Funding: This studentship is only available to UK/EU nationals.
Studentship awards are available to cover both tuition fees and a
stipend in the form of a tax-free subsistence bursary for UK applicants
(in line with EPSRC
recommendations,http://www.epsrc.ac.uk/funding/students/Pages/minimumpay.aspx).
UK applicants would be eligible for both tuition fees and stipend
maintenance support.
Studentship awards are available to cover both tuition fees and a
stipend in the form of a tax-free subsistence bursary for UK applicants
(in line with EPSRC
recommendations,http://www.epsrc.ac.uk/funding/students/Pages/minimumpay.aspx).
UK applicants would be eligible for both tuition fees and stipend
maintenance support.
Scholarship awards are also available to cover tuition fees only for
EU-applicants. EU-applicants are eligible for fees only support unless
they can demonstrate a relevant connection to the UK, in which case they
should qualify for full tuition fees and maintenance support.
Unfortunately non-EU applicants are not eligible for this award unless
they can demonstrate a relevant connection to the UK.
EU-applicants. EU-applicants are eligible for fees only support unless
they can demonstrate a relevant connection to the UK, in which case they
should qualify for full tuition fees and maintenance support.
Unfortunately non-EU applicants are not eligible for this award unless
they can demonstrate a relevant connection to the UK.
Further details regarding eligibility can be found on EPSRC’s web pages
http://www.epsrc.ac.uk/funding/students/Pages/eligibility.aspx
http://www.epsrc.ac.uk/funding/students/Pages/eligibility.aspx
Whilst the above funded position is only open to UK/EU students the
department does additionally recruit international PhD students on a
self-funding basis for related projects (and maybe able to identify
funding sources to which you can apply).
department does additionally recruit international PhD students on a
self-funding basis for related projects (and maybe able to identify
funding sources to which you can apply).
How to apply: Applicants can make initial informal enquiries with Dr.
Toby Breckon http://www.cranfield.ac.uk/~toby.breckon/
Toby Breckon http://www.cranfield.ac.uk/~toby.breckon/
If you meet the eligibility criteria please make an application via the
university applications page at:
http://www.cranfield.ac.uk/students/applications/index.html Please note
this is the only way we can accept applications and applications are not
accepted by email. (Please specify project title: Real-time 3D Computer
Vision for Automotive Applications, supervisor: Dr. Toby Breckon,
Cranfield Campus)
university applications page at:
http://www.cranfield.ac.uk/students/applications/index.html Please note
this is the only way we can accept applications and applications are not
accepted by email. (Please specify project title: Real-time 3D Computer
Vision for Automotive Applications, supervisor: Dr. Toby Breckon,
Cranfield Campus)
