2009 Fourth International Conference on Innovative Computing, Information and Control (ICICIC) 2009
DOI: 10.1109/icicic.2009.55
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A Study of the Different Uses of Colour Channels for Traffic Sign Recognition on Hierarchical Temporal Memory

Abstract: When designing intelligence for a car many different tasks can be performed. Some of these tasks cannot easily be performed by conventional algorithms in comparison with the human brain. Recently, such intelligence has often been reached by using probability based systems. In this paper, Hierarchical Temporal Memory (HTM) is used to implement one of these tasks, namely traffic sign recognition. In implementing this traffic sign recognition task, it is noticed that the use of colour is of particular importance,… Show more

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Cited by 10 publications
(2 citation statements)
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“…The permanence vector, ρ j * , is a binary vector to indicate the status of each potential synapse, where '1' refers to connected synapse and '0' unconnected synapse. The columns computed overlap ( α) then get evaluated by comparing it to a threshold, known as minOverlap (O th ), as given by (4). The resulted vector is an indicator vector representing the nominated column with high overlap.…”
Section: A Spatial Pooler Modelmentioning
confidence: 99%
“…The permanence vector, ρ j * , is a binary vector to indicate the status of each potential synapse, where '1' refers to connected synapse and '0' unconnected synapse. The columns computed overlap ( α) then get evaluated by comparing it to a threshold, known as minOverlap (O th ), as given by (4). The resulted vector is an indicator vector representing the nominated column with high overlap.…”
Section: A Spatial Pooler Modelmentioning
confidence: 99%
“…A two-level HTM was implemented taking multiview orientations of different hand shapes as input. Recently, [159] implemented a HTM for a traffic sign recognition task, proving that the color information is of particular importance in object recognition. A rather more complicated algorithm was introduced by [160], relying on the combination of Visual Feature Array model (VFA) with a HTM network; able to distinguish objects presented in different scales.…”
Section: Introductionmentioning
confidence: 99%