Figure 10.

Accuracy of GPU-based motion tracking algorithms applied to synthetic voltage-sensitive optical mapping data. The synthetic data consists of video image pairs showing the tissue in two different deformed states (I₀ and I₁), and dense 2D displacements (hsv-color-code depicts orientation and magnitude, refer to Figure 11) describing the deformation between the two images. In addition, the synthetic video images include action potential wave patterns, which cause a local decrease in fluorescence (−ΔF/F). The ground truth displacements and tracking outcomes are shown next to each other. (A) Tracking accuracy with original video with 0, –3, and –10% fluorescence signal strength, respectively. The increasing fluorescent signal causes tracking artifacts, particularly with the TV-L1 and Lucas-Kanade algorithms. (B) Tracking accuracy with contrast-enhanced video images with –3% fluorescence signal strength. The contrast-enhancement reduces tracking artifacts with all algorithms. However, with the TV-L1 and Brox algorithms, the contrast-enhancement generates noisy tracking results (the contrast-enhancement also amplifies noise). (C) Tracking accuracy with original video images with 3% noise and –3% fluorescence signal strength. All tracking algorithms are very sensitive to noise: the tracking accuracy deteriorates quickly with noise, refer to Figure 12A.