CLASS, or Cognitive-Level Annotation using latent Statistical Structure, is a research initiative focussed on technologies to recognise three dimensional, physical objects such as a car, or lamp post, or a house, irrespective of its orientation, or the percentage of it visible to a machine vision system.
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The CLASS project, whilst academic in nature, succeeded in creating significant technological improvements to machine vision systems which have since been utilised in multiple commercial products for machine vision, from several different firms.
Chief amongst the advancements was a image splitting system that breaks any physical object up into a number of three dimensional patches. These patches are then compared against the object in question, both to see if the patch fits, and then to see if a selection of other patches also fit, without damaging the spatial relationship between the patches. Thus, partially occluded, or even damaged objects can still be recognised by the AI.
In addition, CLASS created special mechanisms - known as efficient approximate neighbourhood searches - for the comparison of an image or an object with huge numbers of reference images.
1st of January 2006 to 31st of December 2008
Total funding: 2.9 million Euros.
3 year 6th Framework Specific Targeted Research Project, European Union