Figure 1
DefocusTracker working principle and general architecture. (a) A set of training images with known 3D particle positions are used to (b) determine the unknown 3D positions of particles through the comparison of their defocused particle images. (c) General architecture and workflow of the DefocusTracker toolbox. The toolbox uses three types of data structures (rectangles) and five main functions (round shapes, green). The toolbox is modular, allowing for addition and use of different models for the image processing and particle tracking.
Figure 2
Overview of data structures and functions in the DefocusTracker MATLAB implementation, following the general toolbox architecture shown in Figure 1.
Figure 3
Example workflow of the toolbox MATLAB implementation. The example workflow is based on part of the provided Work-Through Example 2 (WTE2) that takes the user through the processing and analysis of particle trajectories inside an evaporating droplet [17]. The programming lines illustrate the code used to create and train a model (green frame), to validate the model on the training data (blue frame), and to process the measurement images (red frame). The frames of corresponding colors illustrate the implemented pop-up GUIs and tables used to visualize and inspect the data structures.
Figure 4
Example validation of the DefocusTracker MATLAB implementation as presented in Barnkob et al. [2], where different defocus-tracking approaches and algorithms were compared when applied to synthetic image sets of different degrees of astigmatism, noise levels, and particle image overlapping. (a) Example field of views of the analyzed synthetic images. (b) Example of the synthetic particle images at different z over the measurement depth h. (c) DefocusTracker results showing the coordinate uncertainties σ and recall ϕ.