Pose estimation for augmented reality applications using genetic algorithm

• Published in: IEEE transactions on systems, man and cybernetics. Part B, Cybernetics Vol. 35; no. 6; pp. 1295 - 1301
• Main Authors: Ying Kin Yu, Kin Hong Wong, Chang, M.M.Y.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.12.2005
• Subjects: Algorithms; Animals; Application software; Artificial Intelligence; Augmented reality; Biological cells; Cameras; Computer vision; Cybernetics; Filtering; Genetic algorithms; Humans; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Imaging, Three-Dimensional - methods; Information Storage and Retrieval - methods; Iterative algorithms; Joints - anatomy & histology; Joints - physiology; Layout; Models, Biological; Models, Genetic; Motion estimation; Pattern Recognition, Automated - methods; pose estimation; Posture - physiology; Searching; Strategy; Translations; User-Computer Interface
• ISSN: 1083-4419
• DOI: 10.1109/TSMCB.2005.850164

This paper describes a genetic algorithm that tackles the pose-estimation problem in computer vision. Our genetic algorithm can find the rotation and translation of an object accurately when the three-dimensional structure of the object is given. In our implementation, each chromosome encodes both the pose and the indexes to the selected point features of the object. Instead of only searching for the pose as in the existing work, our algorithm, at the same time, searches for a set containing the most reliable feature points in the process. This mismatch filtering strategy successfully makes the algorithm more robust under the presence of point mismatches and outliers in the images. Our algorithm has been tested with both synthetic and real data with good results. The accuracy of the recovered pose is compared to the existing algorithms. Our approach outperformed the Lowe's method and the other two genetic algorithms under the presence of point mismatches and outliers. In addition, it has been used to estimate the pose of a real object. It is shown that the proposed method is applicable to augmented reality applications.