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Elastic shape models for interpolations of curves in image sequences
Shantanu H Joshi1, Anuj Srivastava, Washington Mio
1Department of Electrical Engineering, Florida State University, Tallahassee, FL 32310, USA.
Information Processing in Medical Imaging : Proceedings of the ... Conference
|March 16, 2007
Summary
This study introduces a novel shape interpolation technique for image analysis, enabling continuous shape evolution between expert-defined frames. It preserves key features, enhancing temporal analysis in low-quality video sequences.
Area of Science:
- Computer Vision
- Medical Imaging
- Image Analysis
Background:
- Temporal shape evolution analysis is crucial in video sequences.
- Low-contrast and low-quality images often require human intervention for shape extraction.
- Automated methods combined with expert input offer a hybrid approach for shape analysis.
Purpose of the Study:
- To develop a technique for interpolating between expert-generated shapes in video sequences.
- To enable continuous modeling of shape evolution, overcoming limitations of coarse sampling.
- To preserve salient features during the interpolation process.
Main Methods:
- Establishing point correspondence between two end shapes.
- Constructing a geodesic flow on a shape space that maintains point correspondence.
- Applying the technique to echocardiographic and human gait image sequences.
Main Results:
- The developed technique successfully interpolates between expert-defined shapes.
- Salient features of the original shapes are preserved in the interpolated shapes.
- Continuous shape evolution can be modeled effectively.
Conclusions:
- The proposed shape interpolation method enhances the analysis of temporal shape dynamics in challenging image sequences.
- This technique facilitates more detailed and continuous modeling of shape changes compared to expert sampling alone.
- It shows promise for applications in medical imaging and motion analysis.
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