TY - GEN
T1 - Pictorial structures for object recognition and part labeling in drawings
AU - Sadovnik, Amir
AU - Chen, Tsuhan
PY - 2011
Y1 - 2011
N2 - Although the sketch recognition and computer vision communities attempt to solve similar problems in different domains, the sketch recognition community has not utilized many of the advancements made in computer vision algorithms. In this paper we propose using a pictorial structure model for object detection, and modify it to better perform in a drawing setting as opposed to photographs. By using this model we are able to detect a learned object in a general drawing, and correctly label its parts. We show our results on 4 categories.
AB - Although the sketch recognition and computer vision communities attempt to solve similar problems in different domains, the sketch recognition community has not utilized many of the advancements made in computer vision algorithms. In this paper we propose using a pictorial structure model for object detection, and modify it to better perform in a drawing setting as opposed to photographs. By using this model we are able to detect a learned object in a general drawing, and correctly label its parts. We show our results on 4 categories.
KW - object detection
KW - pictorial structures
KW - sketch recognition
UR - http://www.scopus.com/inward/record.url?scp=84863066783&partnerID=8YFLogxK
U2 - 10.1109/ICIP.2011.6116499
DO - 10.1109/ICIP.2011.6116499
M3 - Conference contribution
AN - SCOPUS:84863066783
SN - 9781457713033
T3 - Proceedings - International Conference on Image Processing, ICIP
SP - 3613
EP - 3616
BT - ICIP 2011
T2 - 2011 18th IEEE International Conference on Image Processing, ICIP 2011
Y2 - 11 September 2011 through 14 September 2011
ER -