Abstract
Original language  English 

Qualification  Doctor Degree 
Awarding Institution 

Supervisors/Advisors 

Award date  18 Dec 1992 
Place of Publication  Espoo 
Publisher  
Print ISBNs  9513842401 
Publication status  Published  1992 
MoE publication type  G4 Doctoral dissertation (monograph) 
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Keywords
 3d analysis
 calculation
 parallel computing
 modelling
Cite this
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Calculation of 3D pose of a known object in a single perspective view : Dissertation. / Pehkonen, Kari.
Espoo : VTT Technical Research Centre of Finland, 1992. 107 p.Research output: Thesis › Dissertation
TY  THES
T1  Calculation of 3D pose of a known object in a single perspective view
T2  Dissertation
AU  Pehkonen, Kari
N1  Project code: TKO2107
PY  1992
Y1  1992
N2  This thesis describes an algorithm for 3D pose calculation of known objects from a single perspective view. The pose calculation algorithm consists of four main stages: pose candidate generation, candidate verification, optimization and reliability analysis. In the first stage, pose candidates are generated by matching model and image triangles. An important subproblem, the socalled 3point perspective pose estimation problem, is solved numerically. Pose candidates are verified by matching model and image features. The first accepted candidate is then optimized. Three optimization methods are considered: NewtonRaphson, simulated annealing and robust Mestimate using Lorenzian distribution. The reliability estimates are based on covariance analysis. Uncertainties due to both model and image noise are taken into account. A structure optimization method to reduce modeling noise is also presented. The algorithm was implemented on two parallel computers, a Connection Machine2 (CM2) and a Hathi2/16. The Connection Machine is an SIMD machine whereas the Hathi2/16 is based on the MIMD architecture. To evaluate the speed and accuracy of the algorithm, several experiments were performed using both synthetic and real images. In simulations, the accuracy of the calculated pose, the validity of uncertainty estimates and the effect of dynamic modeling were studied using a polyhedral test object whose maximum distance between any two vertices was 175 mm. Simulations showed that for the distance range from 0.5 m to 2.75 m, the standard deviation of the rotation error ranged from 0.5 degrees to 3.5 degrees when the standard deviation of image and model noise was 1.5 pixels and 1.5 mm, respectively. For the xand ydirections the errors ranged from less than 2 mm up to 10 mm, and for the zdirection, from a few millimeters up to 75 mm. Much better results were achieved at the lower noise levels. The processing times on both the machines were approximately the same. On the Hathi2/16, when all the 16 processors were used, the pose calculation time ranged from 0.376 s up to 0.814 s. For ease of programming the Hathi2/16 was better than the CM2, and it was also considerably cheaper.
AB  This thesis describes an algorithm for 3D pose calculation of known objects from a single perspective view. The pose calculation algorithm consists of four main stages: pose candidate generation, candidate verification, optimization and reliability analysis. In the first stage, pose candidates are generated by matching model and image triangles. An important subproblem, the socalled 3point perspective pose estimation problem, is solved numerically. Pose candidates are verified by matching model and image features. The first accepted candidate is then optimized. Three optimization methods are considered: NewtonRaphson, simulated annealing and robust Mestimate using Lorenzian distribution. The reliability estimates are based on covariance analysis. Uncertainties due to both model and image noise are taken into account. A structure optimization method to reduce modeling noise is also presented. The algorithm was implemented on two parallel computers, a Connection Machine2 (CM2) and a Hathi2/16. The Connection Machine is an SIMD machine whereas the Hathi2/16 is based on the MIMD architecture. To evaluate the speed and accuracy of the algorithm, several experiments were performed using both synthetic and real images. In simulations, the accuracy of the calculated pose, the validity of uncertainty estimates and the effect of dynamic modeling were studied using a polyhedral test object whose maximum distance between any two vertices was 175 mm. Simulations showed that for the distance range from 0.5 m to 2.75 m, the standard deviation of the rotation error ranged from 0.5 degrees to 3.5 degrees when the standard deviation of image and model noise was 1.5 pixels and 1.5 mm, respectively. For the xand ydirections the errors ranged from less than 2 mm up to 10 mm, and for the zdirection, from a few millimeters up to 75 mm. Much better results were achieved at the lower noise levels. The processing times on both the machines were approximately the same. On the Hathi2/16, when all the 16 processors were used, the pose calculation time ranged from 0.376 s up to 0.814 s. For ease of programming the Hathi2/16 was better than the CM2, and it was also considerably cheaper.
KW  3d analysis
KW  calculation
KW  parallel computing
KW  modelling
M3  Dissertation
SN  9513842401
T3  VTT Publications
PB  VTT Technical Research Centre of Finland
CY  Espoo
ER 