Linear combination of transformations
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Visualization of Combined Motions in Human Joints
IEEE Computer Graphics and Applications
On Manifold Structure of Cardiac MRI Data: Application to Segmentation
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Image distance functions for manifold learning
Image and Vision Computing
State of the Art: Coordinated & Multiple Views in Exploratory Visualization
CMV '07 Proceedings of the Fifth International Conference on Coordinated and Multiple Views in Exploratory Visualization
Effectiveness of Animation in Trend Visualization
IEEE Transactions on Visualization and Computer Graphics
Interactive Coordinated Multiple-View Visualization of Biomechanical Motion Data
IEEE Transactions on Visualization and Computer Graphics
Science of analytical reasoning
Information Visualization
Challienges for visual analytics
Information Visualization
Motion-based video retrieval by trajectory matching
IEEE Transactions on Circuits and Systems for Video Technology
Exploratory visualization of animal kinematics using instantaneous helical axes
EuroVis'08 Proceedings of the 10th Joint Eurographics / IEEE - VGTC conference on Visualization
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We present a framework for combining automated and interactive visual analysis techniques for use on high-resolution biomechanical data. Analyzing the complex 3D motion of, e.g., pigs chewing or bats flying, can be enhanced by providing investigators with a multiview interface that allows interaction across multiple modalities and representations. In this paper, we employ nonlinear dimensionality reduction to automatically learn a low-dimensional representation of the data and hierarchical clustering to learn patterns inherent within the motion segments. Our multi-view framework allows investigators to simultaneously view a low-dimensional embedding, motion segment clustering, and 3D visual representation of the data side-by-side.We describe an application to a dataset containing thousands of frames of high-speed, 3D motion data collected over multiple experimental trials.