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A Hybrid Detection and Classification System for Human Motion Analysis
(Springer Nature, 2002)
Omni-directional motion: pedestrian shape classification using neural networks and active contour models
(2001)
This paper describes a hybrid vision system which, following initial user interaction, can detect and track objects in the visual field, and classify them as human and non-human. The system incorporates an active contour ...
The Architecture and Performance of a Stochastic Competitive Evolutionary Neural Tree Network
(2000)
A new dynamic tree structured network - the Stochastic Competitive Evolutionary Neural Tree (SCENT) is introduced. The network is able to provide a hierarchical classification of unlabelled data sets. The main advantage ...
Analysis of human motion using snakes and neural networks
(2000)
A novel technique is described for analysing human movement in outdoor scenes. Following initial detection of the humans using active contour models, the contours are then re-represented as normalised axis crossover vectors. ...
The analysis of animate object motion using neural networks and snakes
(2000)
This paper presents a mechanism for analysing the deformable shape of an object as it moves across the visual field. An object’s outline is detected using active contour models, and is then re-represented as shape, location ...
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 ...
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 ...