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In this study, the authors have proposed a new solution for the problem of pose estimation from a set of matched 3D model and 2D image lines. Traditional line-based pose estimation methods utilising the finite information of the observations are based on the assumption that the noises for the two endpoints of the image line segment are statistically independent. However, in this study, the authors...
This study suggests an application of human–robot interaction based on three-dimensional real-time monocular head pose tracker in which active appearance models (AAMs) are utilised to extract facial features. In order to improve texture model, two probabilistic approaches are proposed for principal component analysis in the presence of missing values. It is observed that using the suggested Bayesian...
This paper presents the challenging issue of target detecting and pose estimation in Forward Looking Infrared (FLIR) sequences. Target detecting and pose estimation are formulated as one process. Specifically, a new mixture of Gaussians model is proposed to detect target appearances. A probability based method is used to estimate the target position and size, where the detected appearance plays an...
This paper proposes a novel human motion capture method that locates human body joint position and reconstructs the human pose in 3D space from monocular images. We propose a two-stage framework including 2D and 3D probabilistic graphical models which can solve the occlusion problem for the estimation of human joint positions. The 2D and 3D models adopt directed acyclic structure to avoid error propagation...
The ability to control the movements of an object or person in a video sequence has applications in the movie and animation industries, and in HCI. In this paper, we introduce a new algorithm for real-time motion control and demonstrate its application to pre-recorded video clips and HCI. Firstly, a dataset of video frames are projected into a lower dimension space. A k-mediod clustering algorithm...
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