TY - GEN
T1 - Acquiring robust representations for recognition from image sequences
AU - Wallraven, Christian
AU - Bulthoff, Heinrich
PY - 2001
Y1 - 2001
N2 - We present an object recognition system which is capable of on-line learning of representations of scenes and objects from natural image sequences. Local appearance features are used in a tracking framework to find ‘key-frames’ of the input sequence during learning. In addition, the same basic framework is used for both learning andre cognition. The system creates sparse representations and shows good recognition performance in a variety of viewing conditions for a database of natural image sequences.
AB - We present an object recognition system which is capable of on-line learning of representations of scenes and objects from natural image sequences. Local appearance features are used in a tracking framework to find ‘key-frames’ of the input sequence during learning. In addition, the same basic framework is used for both learning andre cognition. The system creates sparse representations and shows good recognition performance in a variety of viewing conditions for a database of natural image sequences.
KW - Appearance-based learning
KW - Model acquisition
KW - Object recognition
UR - http://www.scopus.com/inward/record.url?scp=84945289683&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84945289683&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84945289683
SN - 3540425969
VL - 2191
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 216
EP - 222
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PB - Springer Verlag
T2 - 23rd German Association for Pattern Recognition Symposium, DAGM 2001
Y2 - 12 September 2001 through 14 September 2001
ER -