Abstract
Recognising complex three-dimensional objects presents significant challenges to visual systems when these objects are rotated in depth. The image processing requirements for reliable individual recognition under these circumstances are computationally intensive since local features and their spatial relationships may significantly change as an object is rotated in the horizontal plane. Visual experience is known to be important in primate brains learning to recognise rotated objects, but currently it is unknown how animals with comparatively simple brains deal with the problem of reliably recognising objects when seen from different viewpoints. We show that the miniature brain of honeybees initially demonstrate a low tolerance for novel views of complex shapes (e.g. human faces), but can learn to recognise novel views of stimuli by interpolating between or ‘averaging’ views they have experienced. The finding that visual experience is also important for bees has important implications for understanding how three dimensional biologically relevant objects like flowers are recognised in complex environments, and for how machine vision might be taught to solve related visual problems.
Highlights
The ability to reliably recognise three dimensional objects is a complex problem for both biological and artificial vision systems since the viewpoint from which the object is seen may dramatically affect the spatial relationships between visible local features of the object [1,2,3,4]
Many biologically important objects like flowers for bees [5], or faces for primates [6], sheep [7] and even wasps [8,9], have to be viewed in complex natural environments from different viewpoints. This can be a difficult problem for visual systems to solve as an image of a rotated target stimulus, like a face, will often appear more dissimilar to its non-rotated appearance than to other non-rotated distractor stimuli [3]
For instance, neurons in inferior-temporal cortex can become tuned to trained views of objects [6,13,14]
Summary
The ability to reliably recognise three dimensional objects is a complex problem for both biological and artificial vision systems since the viewpoint from which the object is seen may dramatically affect the spatial relationships between visible local features of the object [1,2,3,4]. Adult humans[10,11] and other primates[6,12,13] recognise novel presentations of rotated objects through mechanisms that predominantly rely on image interpolation of a limited number of stored views.
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