Model-based tracking using motion estimation of a static vehicle in airborne image sequences

2006 ◽  
Author(s):  
Gregory J. Power ◽  
Craig R. Roush
Keyword(s):  
Motion Estimation ◽  
Image Sequences ◽  
Model Based

3-D model-based segmentation of videoconference image sequences

1998 ◽  
Vol 8 (5) ◽  
pp. 547-561 ◽  
Author(s):  
I. Kompatsiaris ◽  
D. Tzovaras ◽  
M.G. Strintzis
Keyword(s):  
Image Sequences ◽  
Model Based ◽  
D Model

Motion estimation from tagged MR image sequences

1992 ◽  
Vol 11 (2) ◽  
pp. 238-249 ◽  
Author(s):  
J.L. Prince ◽  
E.R. McVeigh
Keyword(s):  
Motion Estimation ◽  
Image Sequences ◽  
Mr Image

scholarly journals On the Usage of GPUs for Efficient Motion Estimation in Medical Image Sequences

2011 ◽  
Vol 2011 ◽  
pp. 1-15 ◽  
Author(s):  
Jeyarajan Thiyagalingam ◽  
Daniel Goodman ◽  
Julia A. Schnabel ◽  
Anne Trefethen ◽  
Vicente Grau
Keyword(s):  
Motion Estimation ◽  
Real Time ◽  
Three Dimensional ◽  
Basic Research ◽  
Estimation Algorithm ◽  
Image Sequences ◽  
Multicore Architectures ◽  
Gpu Architectures ◽  
Access Patterns ◽  
Performance Gains

Images are ubiquitous in biomedical applications from basic research to clinical practice. With the rapid increase in resolution, dimensionality of the images and the need for real-time performance in many applications, computational requirements demand proper exploitation of multicore architectures. Towards this, GPU-specific implementations of image analysis algorithms are particularly promising. In this paper, we investigate the mapping of an enhanced motion estimation algorithm to novel GPU-specific architectures, the resulting challenges and benefits therein. Using a database of three-dimensional image sequences, we show that the mapping leads to substantial performance gains, up to a factor of 60, and can provide near-real-time experience. We also show how architectural peculiarities of these devices can be best exploited in the benefit of algorithms, most specifically for addressing the challenges related to their access patterns and different memory configurations. Finally, we evaluate the performance of the algorithm on three different GPU architectures and perform a comprehensive analysis of the results.

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