Being Ironman

On the Visualisation Room at the Atlas Building in the Harwell Campus, Srikanth Nagella installed the Avizo VR Module – that allows full tracking and experimented on Tomographic Data:

  • Full immersive 3D interaction;
  • Support for single and multiple screens;
  • Support for single and multi-pipe and for graphic clusters;
  • Flexible customization for specific display geometry;
  • Head-tracking and tracked hand-held 3D input devices;
  • Control functionality via user interface; and
  • Control of 2D user interfaces with 3D devices (virtual mouse).

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Restructuring and starting a “new” Visualisation Group

The SCD within STFC is having Away Days in April and collated together with a five year plan for the complete Department. The Visualisation Group has a technology division strategy outline and operational plan but produces a list of future areas of support that include the concept of keeping the Human always within the Visuallsation loop.

Key Activities

The Visualisation Group, part of the Technology Division within SCD, was founded to support and maintain visualisation software and skills for large projects and user communities.

This has included working;

  • with the Hartree Centre hosting their visualisation centres connecting outputs from some of the largest computer in the UK making them human understandable;
  • with the Innovate UK’s Space Application Catapult and European Space Agency (ESA) producing bespoke solutions for their data analytical and command and control needs;
  • creating remote and distance data gathering and visualisation workflows to control the computational processes and
  • providing specialist local high-end equipment within the centres that are near to the main STFC image capturing x-ray, neutron and laser facilities.

Human in the Loop

The group is working to support the high-end visualisation centres within STFC, with the key objective to consider the human-in-the-loop as an integral part to pre- mid- and post-data visualisation needs from the major facilities, from archived data stores and from computational simulations. This it is believed is a key component to increasing the efficiency of the major STFC facilities allowing researchers’ work-plans to be controlled, changed and even stopped on the fly.

The group has an emphasis to work in harmony with collaborators across the STFC mission. To this end there are links to partners;

  • as well as the major imaging facilities (ISIS, Diamond Light Source, and Central Lasers Facility) and the Hartree Centre this includes the
  • Virtual Engineering Centre (University of Liverpool centre based in the Daresbury Labs.),
  • the Harwell Imaging Partnership (based in the RAL campus),
  • the Collaborative Computational Projects (CCPs based across SCD) and a
  • range of Research Council projects with other teams and groups within SCD and STFC.

An image of the initial posters shown here:

VisGrp_A0P_Poster02-15_mt2_fin

Visualisation User Needs Survey

At the end of 2014 we analysed a Visualisation Tools survey for certain HPC and computational users.

UserNeedsSurvey

The results from over 100 respondents are being edited at:
http://www.vizmatters.cs.manchester.ac.uk/

Executive summary states; for the global survey there were seven key outcome results that can be acted upon:

  • Three packages are the most-used packages by 26% of respondents. Conversely, another 31 packages are used by one or two users and account for a further 26% of respondents.
  • Producing publication quality plots is the most-used technique.
    However, the features making these packages the favourites are:
    – Software that is written specifically for their domain of interest.
    – Large datasets are handled efficiently.
    – Scripting or other ability to extend the tool is required.
  • Users second most favoured packages are general purpose visualisation tools.
  • Users were given five options for selecting their most required development. None emerged as being more needed than the others.
  • Conversely, large amounts of memory was clearly the most important requirement for high performance visualisation.
  • The main future challenges are suggested to be
    – The ability to handle large amounts of data
    – The ability to operate in a distributed environment.

A series of further surveys and follow-up questions are planned as well as afull review next October (2015).

CCPi User Survey

A series of questions asked users about their current and future needs. Some important issues were raised over the changing use of visualisation tools for Computed Tomography results.

Executive Summary:

  1. Software that is more popular now has moved to Avizo, ImageJ and Paraview
  2. Lots of reconstruction development (filtrers etc) but little segmentation
  3. CCPi core activities will focus on wrappers and development of community software for these three products.

 

Slide1 Slide2 Slide3 Slide4 Slide5 Slide6 Slide7 Slide8

At the CCPi Working Group meeting there was a short debate on the results which resolved to focus effort from VolView to Avizo and follow the direction from the user base. This was followed up at a Developers’ Workshop help in Nottingham University (23 July 2014).

CCPi Working Group June 2014. Included discussion on survey results.

CCPi Working Group June 2014. Included discussion on survey results.

SciVis4All a consortium for the future from EuroVis 2014

During EuroVis 2014 in June at the University of Swansea we had post dinner and then extended discussion meeting regarding the possibility of considering Visualisation as a CCP in itself or as a cross-service role to other CCPs.

1. The conference hosted about 250 people so there is a vibrant and large community which would be active in supporting. The UK component is a good size and this year it was reported that there was a significant increase in interest from a range of UK universities.

EuroVis Conference Dinner - June 2014 held at Swansea

EuroVis Conference Dinner – June 2014 held at Swansea

2. There is alterntaive networks within the UK including the Eurographics UK Chapter: who could support and assist in further network development. They also represent 10 or so people across the two main communities of Computer Graphics and Visualisation. A recent survey has involved trying to define the distinction between these two main groups.

Question is what is the key software deliverables;

  1. information visualisation or scientific visualisation – or both.
  2. specific API toolkit or major product including interface etc.
  3. web-based portal type programming or stand-alone application
  4. how localised a network of developers vs users

 

Swansea City is meant to be the wettest city in the UK- and we had to suffer the 30 min walk across the wilderness from the hotel to the university; there and back every day.

Gorgous sunshine on beach walk to University of Swansea from hotel during EuroVis 2014.

Gorgeous sunshine on the beach walk to the University of Swansea from our hotel during EuroVis 2014.

 

CCPi (Tomographic Imaging) Case Study

Tomographic Imaging of a Nokia 702 Mobile Phone and other Items

The Challenge

Spot anomalies using the software available. As part of an assignment a set of objects were scanned using an X-Tech 320 kV CT scanner. This was followed by an exploratory stage using the multiple methods available from visualisation systems.

Key items to be discovered included the following:

  • Understand how different materials and components can be separated.
  • Extract some understanding of the objects to gain insight in to the object’s function
  • Extract the shape of a visible or unknown component.
  • Analyse the density of the materials used and identify defects.

The Solution

Presented a range of 2D and 3D views that could be manipulated to see features. An ancient mobile phone, the Nokia 702 amongst other items were investigated. About 1500 2kx2k X-ray images were captured over evenly spaced rotation angles of the mobile phone. These were reconstructed to create a 3D volume approx 1500 x 1000 x 1000.

Example 1: Mobile Phone Nokia 702

The speaker mechanism (bottom) can be clearly seen, as well as loose components (right) that were not screwed down properly.

Left: Speaker componets clearly visable idefining 3D shape and measurable size and density. Right: Loose componets (extra screw) that has fallen into the phone during assembly is visable and measurable.

Left: Speaker components clearly visible identifying 3D shape and measurable size and density.
Right: Loose components (extra screw) that has fallen into the phone during assembly is visible and measurable.

The obvious aerial is actually an aesthetic feature, as it is not connected, with a mesh being used instead (below).

Two density transfer functions highlighting the mesh aerial.

Two density transfer functions highlighting the mesh aerial.

Very quick confirmation of discoveries has occurred but the full visualisation component can be seen in the following screenshot that describes and views the raw data, image projections, 3D reconstruction slices and final 3D volume visualisation (left to right).

Mobile Phone being viewed as raw data, image projections, 3D reconstruction and final 3D volume visualisation (Drishti)

Mobile Phone being viewed as raw data, image projections, 3D reconstruction and final 3D volume visualisation (Drishti)

Example 2: Golf Balls.

A Fitleist 2 (High-quality) versus a Srixon 4 (standard quality) golf balls; were both scanned at 2kx2k and then investigated using identical networks to discover material characteristics non-destructively. The high-quality golf ball has a very well-defined liquid core with an injection point, multiple tight winding impressions, as well as, far more complex layers of materials all visible and can be quantified in terms of shapen and size.

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Example 3: Low Quality Padlock.

A very low quality padlock – with simple mechanism. Although the padlock uses a key the shape has been shown to be irrelevant to opening the lock. All that is needed is for the clip (far right) to be pulled apart so virtually any flat shape will work.

Visualisation network for the low-quality padlock. Although simple can still with interactive exploration discover new features within the volume data set.

Visualisation network for the low-quality padlock. Although simple can still with interactive exploration discover new features within the volume data set.

Example 4: High Quality Padlock.

A very high quality spherical security padlock – with complex mechanism. Requires a complex key structure, but it is still possible to retrieve the main components of the key shape.

Various views and colourmaps highlighting components within the high-quality padlock; as well as marked up components showing a possible 2D key structure to passist in picking the lock.

Various views and colourmaps highlighting components within the high-quality padlock; as well as marked up components showing a possible 2D key structure to assist in picking the lock.

Example 5 Homeland Security combination padlock

Homeland Security combination padlock. Understand how both combination and key mechanisms work; 1. Extract the correct combination code for unlocking the padlock, 2. Extract the shape of the key, and 3. Analyse the density of the materials used within the padlock manufacture and identify defects. Visualization filters can extract specific components – in this case recover non-destructively the combination lock values for the padlock. Alternative visualization filters allow analysis and measurement of defects introduced during the manufacturing process.

 

How to extract the combinations from a padlock. Step-0by-step process extracting and examining the components.

How to extract the combinations from a padlock. Step-0by-step process extracting and examining the components.

Density measurements illustrating poor and good quality manufacture wiithin the lock. Network is complete network for the extraction of the combinations.

Density measurements illustrating poor and good quality manufacture within the lock. Network is complete network for the extraction of the combinations.

 

The Benefits

The benefits of using visualisation in this case was to gain Insight as the human was able to see anomalies that an automated computer system could not. The question is when would an automated system – say for defect detection – be sufficient and used and then visualisation is not needed just data analysis and mining.

Credit to the students and researchers in the past who have created these visualisations and carried out the analysis.

Impact of Visualisation – a formula

Can visualisation methods be evaluated and should they.

A proposal presented at EuroVis 2014 held in the University of Swansea, Wales.

V = -T + I + E + C

This is the Value of a Visualisation is equal to the Time taken to understand the visualisation from the user plus the Insight the user gains from this plus the Essence gained for parts of this plus the Clarity defining the overall acceptance of the global data set by the user.

So let’s try this in action on a simple 3D example with users.

Example 1:

LiDAR visualisation for the geology community - can show rock escarpments and cave structures at myltiple sclaes with augmented meta-data and markers.

LiDAR visualisation for the geology community – can show rock escarpments and cave structures at myltiple sclaes with augmented meta-data and markers.

The previous image shows a geological structure in Egypt, Mount Sanai in stereoscopic 3D projection mode, with added markers showing way-points, landmarks, measurement fields as well as allowing ares of curvature for example to be highlighted and measured.

The time taken to do a demonstration is very low, and the number of Insight points (sometimes called Impact or Wow elements) is reasonably high as the user can discover anomalies, for example smooth areas of curvature in the rock face showing different strata. The Essence or global structure and understanding is also high as this is an intuitive 3D structure, and from this Confidence or Clarity in the data can be achieved. There are a few outliers and anomalies from the LiDAR data but the visualisation is very clean.

 Example 2:

CCP4software demonstration visualisation image - showing switchable components from proteins viewed in stereosc

CCP4software demonstration visualisation image – showing switchable components from proteins viewed in stereoscopic mode to an audience.