TY - GEN
T1 - Visual recognition based on coding in temporal cortex
T2 - 1st International Conference on Artificial Neural Networks, ICANN 1996
AU - Perrett, D. I.
N1 - Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 1996.
PY - 1996
Y1 - 1996
N2 - A model of recognition is described that is based on cell properties in the ventral cortical stream of visual processing in the primate brain. At a critical intermediate stage in this system Elaborate feature sensitive cells respond selectively to visual features in a way that depends on size (±1 octave), orientation (±45) but does not depend on position within central vision (±5). These features are simple conjunctions of 2-D elements (e.g. a horizontal dark area above a dark smoothly convex area). Such features can arise either as elements of an objects surface pattern or as 3-D component parts of the object. By requiring a combination of several such features without regard to their position within the central region of the visual image, Pattern sensitive cells at higher levels can become selective for complex configurations that typify objects experienced in particular viewing conditions. Given that input features are specified in approximate size and orientation, initial cellular ‘representations’ of the visual appearance of object type (or object example) are also selective orientation and size. Such representations are sensitive to object view (±40-60) because visual features disappear as objects are rotated in perspective. Combined sensitivity to multiple 2-D features independent of their position establishes selectivity for configuration of object parts (from one view) because rearranged configurations yield images lacking some of features present in the normal configuration. Different neural populations appear to be tuned to particular components of the same biological object (e.g. face, eyes, hands, legs), perhaps because the independent articulation of these components gives rise to correlated activity in different sets of input visual features. Generalisation over viewing conditions for a given object can be established by hierarchically pooling outputs of view specific cells. Such pooling could depend on the continuity in experience across viewing conditions: different object parts are seen together and different views are seen in succession when the observer walks around the object. For any familiar object, more cells will be tuned to the configuration of the objects features present in the view(s) frequently experienced. Therefore, activity amongst the population of cells selective for the objects appearance will accumulate more slowly when the object is seen in an unusual orientation or view. This accounts for increased time to recognise rotated views without the need to postulate mental rotation or transformations of novel views to align with neural representations of familiar views. The model is in accordance with known physiological findings and matches the behavioural performance of the mammalian visual system which displays view, orientation and size selectivity when learning about new pattern configurations.
AB - A model of recognition is described that is based on cell properties in the ventral cortical stream of visual processing in the primate brain. At a critical intermediate stage in this system Elaborate feature sensitive cells respond selectively to visual features in a way that depends on size (±1 octave), orientation (±45) but does not depend on position within central vision (±5). These features are simple conjunctions of 2-D elements (e.g. a horizontal dark area above a dark smoothly convex area). Such features can arise either as elements of an objects surface pattern or as 3-D component parts of the object. By requiring a combination of several such features without regard to their position within the central region of the visual image, Pattern sensitive cells at higher levels can become selective for complex configurations that typify objects experienced in particular viewing conditions. Given that input features are specified in approximate size and orientation, initial cellular ‘representations’ of the visual appearance of object type (or object example) are also selective orientation and size. Such representations are sensitive to object view (±40-60) because visual features disappear as objects are rotated in perspective. Combined sensitivity to multiple 2-D features independent of their position establishes selectivity for configuration of object parts (from one view) because rearranged configurations yield images lacking some of features present in the normal configuration. Different neural populations appear to be tuned to particular components of the same biological object (e.g. face, eyes, hands, legs), perhaps because the independent articulation of these components gives rise to correlated activity in different sets of input visual features. Generalisation over viewing conditions for a given object can be established by hierarchically pooling outputs of view specific cells. Such pooling could depend on the continuity in experience across viewing conditions: different object parts are seen together and different views are seen in succession when the observer walks around the object. For any familiar object, more cells will be tuned to the configuration of the objects features present in the view(s) frequently experienced. Therefore, activity amongst the population of cells selective for the objects appearance will accumulate more slowly when the object is seen in an unusual orientation or view. This accounts for increased time to recognise rotated views without the need to postulate mental rotation or transformations of novel views to align with neural representations of familiar views. The model is in accordance with known physiological findings and matches the behavioural performance of the mammalian visual system which displays view, orientation and size selectivity when learning about new pattern configurations.
UR - http://www.scopus.com/inward/record.url?scp=84947923555&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84947923555
SN - 3540615105
SN - 3540615105
SN - 9783540615101
SN - 9783540615101
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 1
BT - Artificial Neural Networks, ICANN 1996 - 1996 International Conference, Proceedings
A2 - yon der Malsburg, Christoph
A2 - Vorbruggen, Jan C.
A2 - von Seelen, Werner
A2 - Sendhoff, Bernhard
PB - Springer-Verlag
Y2 - 16 July 1996 through 19 July 1996
ER -