TY - GEN
T1 - Efficient methods for point matching with known camera orientation
AU - Mota, João F. C.
AU - Aguiar, Pedro M. Q.
PY - 2010
Y1 - 2010
N2 - The vast majority of methods that successfully recover 3D structure from 2D images hinge on a preliminary identification of corresponding feature points. When the images capture close views, e.g., in a video sequence, corresponding points can be found by using local pattern matching methods. However, to better constrain the 3D inference problem, the views must be far apart, leading to challenging point matching problems. In the recent past, researchers have then dealt with the combinatorial explosion that arises when searching among N! possible ways of matching N points. In this paper we overcome this search by making use of prior knowledge that is available in many situations: the orientation of the camera. This knowledge enables us to derive algorithms to compute point correspondences. We prove that our approach computes the correct solution when dealing with noiseless data and derive an heuristic that results robust to the measurement noise and the uncertainty in prior knowledge. Although we model the camera using orthography, our experiments illustrate that our method is able to deal with violations, including the perspective effects of general real images.
AB - The vast majority of methods that successfully recover 3D structure from 2D images hinge on a preliminary identification of corresponding feature points. When the images capture close views, e.g., in a video sequence, corresponding points can be found by using local pattern matching methods. However, to better constrain the 3D inference problem, the views must be far apart, leading to challenging point matching problems. In the recent past, researchers have then dealt with the combinatorial explosion that arises when searching among N! possible ways of matching N points. In this paper we overcome this search by making use of prior knowledge that is available in many situations: the orientation of the camera. This knowledge enables us to derive algorithms to compute point correspondences. We prove that our approach computes the correct solution when dealing with noiseless data and derive an heuristic that results robust to the measurement noise and the uncertainty in prior knowledge. Although we model the camera using orthography, our experiments illustrate that our method is able to deal with violations, including the perspective effects of general real images.
UR - http://www.scopus.com/inward/record.url?scp=77955452131&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-13772-3_22
DO - 10.1007/978-3-642-13772-3_22
M3 - Conference contribution
AN - SCOPUS:77955452131
SN - 9783642137716
VL - 6111
T3 - Lecture Notes in Computer Science
SP - 210
EP - 219
BT - Image Analysis and Recognition. ICIAR 2010. Lecture Notes in Computer Science, vol 6111.
A2 - Campilho, A.
A2 - Kamel, M.
T2 - 7th International Conference on Image Analysis and Recognition 2010
Y2 - 21 June 2010 through 23 June 2010
ER -