Inverse kinematics positioning using nonlinear programming for highly articulated figures
ACM Transactions on Graphics (TOG)
Real-time inverse kinematics techniques for anthropomorphic limbs
Graphical Models and Image Processing
An inverse kinematics architecture enforcing an arbitrary number of strict priority levels
The Visual Computer: International Journal of Computer Graphics - Special section on implicit surfaces
A survey of advances in vision-based human motion capture and analysis
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
Toward natural interaction through visual recognition of body gestures in real-time
Interacting with Computers
An efficient euclidean distance transform
IWCIA'04 Proceedings of the 10th international conference on Combinatorial Image Analysis
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In order to study human motion in biomechanical applications, a critical component is to accurately obtain the 3D joint positions of the user's body. Computer vision and inverse kinematics are used to achieve this objective without markers or special devices attached to the body. The problem of these systems is that the inverse kinematics is "blinded" with respect to the projection of body segments into the images used by the computer vision algorithms. In this paper, we present how to add image constraints to inverse kinematics in order to estimate human motion. Specifically, we explain how to define a criterion to use images in order to guide the posture reconstruction of the articulated chain. Tests with synthetic images show how the scheme performs well in an ideal situation. In order to test its potential in real situations, more experiments with task specific image sequences are also presented. By means of a quantitative study of different sequences, the results obtained show how this approach improves the performance of inverse kinematics in this application.