Dynamic PoDynamic Point-Cloud Analysis: with int-Cloud Analysis: with applications in Thoracic Surface Reconstruction (TSR) and Autonomous Navigation
Lead Research Organisation:
University of Cambridge
Department Name: Engineering
Abstract
The key aims of the project and novel methodology are summarised below. The
research fits in to the computer vision, biomedical engineering and
digital-build fields.
1. Key Objectives/aims of research:
This research will firstly address the problem of fitting objects/surfaces
to dynamic point cloud data (such data consists of 3D spatial coordinates
of random points on objects over time). This data can be obtained by
inexpensive, commercially available time of flight cameras, or from more
costly lidar systems.
In the case of time of flight cameras, automatically fitting a surface to
the thoracic (chest and abdomen) region of a breathing patient, will be one
of our applications. A regional analysis of these breathing patterns (and
parameters extracted, such as volume and flow) will enable us to
automatically classify respiratory disorders (the aim is to look at motor
neurone disease and childhood asthma -- with clinical collaborators).
In the case of lidar, the point clouds are generally of a stationary object
(normally a building) taken with a moving 'camera'. Here we aim to use the
motion information to automatically and accurately segment building
primitives and to match them to a model, if that exists. This research will
tie in with current CUED work in the Cambridge Centre for Digital Build
Britain.
2. Novel physical sciences/engineering methodology which will be carried
out during the course of the project:
In order to carry out the research described above we anticipate that we
will need to develop a number of novel solutions. Firstly, we will look
into novel methods of fitting both geometric primitives (planes etc) and
non-uniform surfaces (such as the thoracic region) to dynamic point cloud
data. This will require integrating computer vision and computer graphics
techniques with statistical optimisation. For the aspects which involve
classification we hope to have a new (moving surface over time) dataset, to
which we can apply machine learning methods. The conventional CNN
(convolutional neural network) methods will require extensions for this
data, and the fusion of related data (other known information) could also
be important.
research fits in to the computer vision, biomedical engineering and
digital-build fields.
1. Key Objectives/aims of research:
This research will firstly address the problem of fitting objects/surfaces
to dynamic point cloud data (such data consists of 3D spatial coordinates
of random points on objects over time). This data can be obtained by
inexpensive, commercially available time of flight cameras, or from more
costly lidar systems.
In the case of time of flight cameras, automatically fitting a surface to
the thoracic (chest and abdomen) region of a breathing patient, will be one
of our applications. A regional analysis of these breathing patterns (and
parameters extracted, such as volume and flow) will enable us to
automatically classify respiratory disorders (the aim is to look at motor
neurone disease and childhood asthma -- with clinical collaborators).
In the case of lidar, the point clouds are generally of a stationary object
(normally a building) taken with a moving 'camera'. Here we aim to use the
motion information to automatically and accurately segment building
primitives and to match them to a model, if that exists. This research will
tie in with current CUED work in the Cambridge Centre for Digital Build
Britain.
2. Novel physical sciences/engineering methodology which will be carried
out during the course of the project:
In order to carry out the research described above we anticipate that we
will need to develop a number of novel solutions. Firstly, we will look
into novel methods of fitting both geometric primitives (planes etc) and
non-uniform surfaces (such as the thoracic region) to dynamic point cloud
data. This will require integrating computer vision and computer graphics
techniques with statistical optimisation. For the aspects which involve
classification we hope to have a new (moving surface over time) dataset, to
which we can apply machine learning methods. The conventional CNN
(convolutional neural network) methods will require extensions for this
data, and the fusion of related data (other known information) could also
be important.
Organisations
People |
ORCID iD |
| Joan Lasenby (Primary Supervisor) | |
| Alexander Grafton (Student) |