Bio-inspired emergent construction

Feltell, David; Bai, Li; Soar, Rupert

doi:10.1109/sis.2005.1501596

Cited by 4 publications

(4 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Given this non-linearity, a tiny change in a parameter can result in a drastically different solution, but (if the system is well formed) one that still conforms to a valid set of stable solutions -that is, the system exhibits multistability. We have demonstrated this engineering paradigm previously in modeling the building behaviour of Macrotermes termites (Feltell and Bai, 2004;Feltell et al, 2005). The use of an attractive cement pheromone to coordinate soil clustering behaviour has provided the inspiration for the swarm-intelligent clustering mechanism used here.…”

Section: Multi-agent Swarm Based Algorithmsmentioning

confidence: 89%

LEVEL SET BRIAN SEGMENTATION WITH AGENT CLUSTERING FOR INITIALISATION - Fast Level Set Based MRI Tissue Segmentation with Termite-Like Agent Clustering for Parameter Initialization

Feltell¹,

Bai

2008

Proceedings of the First International Conference on Bio-Inspired Systems and Signal Processing

View full text Add to dashboard Cite

This paper presents a novel 3D brain segmentation method based on level sets and bio-inspired methodologies. Level set segmentation methods, although highly promising, require manual selection of seed positions and thereshold parameters, along with manual reinitialisation to a new level set surface for each candidate region. Here, the use of swarm intelligent mechanisms is used to provide all the statistical data and sample points required, allowing automatic initialisation of multiple level set solvers. This is shown by segmentation of white matter, grey matter and cerebro-spinal fluid in a simulated T1 MRI scan, followed by direct comparison between a commercial level application-FMRIB's FAST-and the ground truth anatomical model.

show abstract

Section: Multi-agent Swarm Based Algorithmsmentioning

confidence: 89%

LEVEL SET BRIAN SEGMENTATION WITH AGENT CLUSTERING FOR INITIALISATION - Fast Level Set Based MRI Tissue Segmentation with Termite-Like Agent Clustering for Parameter Initialization

Feltell¹,

Bai

2008

Proceedings of the First International Conference on Bio-Inspired Systems and Signal Processing

View full text Add to dashboard Cite

show abstract

“…In most models of natural swarm intelligence (for example, previous work on modelling termites [6], [5]), a random number generator is used in order to represent unseen external stochasticity. However, nature itself is entirely deterministic.…”

Section: Swarm Intelligencementioning

confidence: 99%

3D level set image segmentation refined by intelligent agent swarm

Feltell

2010

IEEE Congress on Evolutionary Computation

View full text Add to dashboard Cite

The level set method of surface representation and deformation has found many applications in image processing, especially with regard to segmentation. Naive numerical solutions have long since given way to much more efficient narrow band methods, where updates to the scalar field are performed only within a number of layers either side of the surface. This paper presents our implementation of automated segmentation via the sparse field narrow band approach. We use k-means clustering of regularly sampled points to provide initialisation of seed locations and mean voxel values, removing the need for manual seed and parameter selection. We demonstrate this method by segmenting grey matter and white matter from a simulated T1 MRI scan.Anisotropy can easily cause this and other segmentation methods to misclassify voxels, however. Often it is clear in a 3D view of the resulting surface which regions require refinement. With available extensions to the sparse field and rendering algorithms we can now efficiently perform purely localised modifications and re-rendering of the surface. These extensions, plus properties inherent in the level set method, allow us to easily situate swarms of agents on the surface, intelligently inhabiting and modifying the zero-level set. By a simple weighting of movement direction, we can direct the swarm to any user-specified point on the surface in order to affect repairs and refinement.978-1-4244-8126-2/10/$26.00 ©2010 IEEE

show abstract

“…Here it is possible that inspiration can be drawn from structures such as the mound of termites (Macrotermitinae). The study of these has show that they are complex selfregulating structures and the way in which they are constructed and operate is being analysed [23].…”

Section: Structurementioning

confidence: 99%

Material requirements for bio-inspired sensing systems

Biggins

Lloyd

Salmond

et al. 2008

Optical Materials in Defence Systems Technology V

View full text Add to dashboard Cite

The aim of developing bio-inspired sensing systems is to try and emulate the amazing sensitivity and specificity observed in the natural world. These capabilities have evolved, often for specific tasks, which provide the organism with an advantage in its fight to survive and prosper. Capabilities cover a wide range of sensing functions including vision, temperature, hearing, touch, taste and smell. For some functions, the capabilities of natural systems are still greater than that achieved by traditional engineering solutions; a good example being a dog's sense of smell. Furthermore, attempting to emulate aspects of biological optics, processing and guidance may lead to more simple and effective devices. A bio-inspired sensing system is much more than the sensory mechanism. A system will need to collect samples, especially if pathogens or chemicals are of interest. Other functions could include the provision of power, surfaces and receptors, structure, locomotion and control. In fact it is possible to conceive of a complete bio-inspired system concept which is likely to be radically different from more conventional approaches. This concept will be described and individual component technologies considered.

show abstract

Bio-inspired emergent construction

Cited by 4 publications

References 8 publications

LEVEL SET BRIAN SEGMENTATION WITH AGENT CLUSTERING FOR INITIALISATION - Fast Level Set Based MRI Tissue Segmentation with Termite-Like Agent Clustering for Parameter Initialization

LEVEL SET BRIAN SEGMENTATION WITH AGENT CLUSTERING FOR INITIALISATION - Fast Level Set Based MRI Tissue Segmentation with Termite-Like Agent Clustering for Parameter Initialization

3D level set image segmentation refined by intelligent agent swarm

Material requirements for bio-inspired sensing systems

Contact Info

Product

Resources

About