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Human shape recognition from snakes using neural networks
(Institute of Electrical and Electronics Engineers (IEEE), 1999)
This paper documents experiments which have been carried out with several neural network systems designed to categorise pedestrian shapes from non-pedestrian shapes. Active Contour models (‘Snakes’) [1] have been used to ...
Detecting partial occlusion of humans using snakes and neural networks
(1999)
This paper summarises the development of a computer system designed to detect moving humans in an image or series of images. The system combines the use of active contour models, ‘snakes’, which detect human objects in an ...
Hierarchical Classification with a Competitive Evolutionary Neural Tree
(1999)
A new, dynamic, tree structured network, the Competitive Evolutionary Neural Tree (CENT) is introduced. The network is able to provide a hierarchical classification of unlabelled data sets. The main advantage that the CENT ...
Analysing Hierarchical Data Using a Stochastic Evolutionary Neural Tree
(1998)
SCENT is simple competitive neural network model that evolves a tree structured set of nodes in response to being presented with an unlabelled data set. The resulting set of weight vectors and their relationship can be ...
An investigation into the performance and representation of a stochastic evolutionary neural tree
(Springer Nature, 1997)
The Stochastic Competitive Evolutionary Neural Tree (SCENT) is a new unsupervised neural net that dynamically evolves a representational structure in response to its training data. Uniquely SCENT requires no initial parameter ...