Dabiri, DanaLiu, Yi-Lin2021-10-292021-10-292021Liu_washington_0250O_23395.pdfhttp://hdl.handle.net/1773/47904Thesis (Master's)--University of Washington, 2021This thesis will investigate particle identification in the 2D noiseless image. The modified cascade cross-correlation method (MCCM) is first introduced. The idea of it is to perform cross-correlation on images and apply a nonlinear least-square solver to give sub-pixel accuracy. Next is the support set method, which uses a morphological way to segment images. An iterative scheme is applied to each segment to acquire particle location. Finally, the technique developed from 3D tomographic PIV which is projected to 2D then utilizes nonnegative least-square (NNLS) to retrieve particle location. This method is superior in the ability to quantitatively find particles and the accuracy in constant particle size images. For images that the particle size varied, we proposed a method that required MCCM to determine the size to apply in NNLS and the result from NNLS to reconstruction image. After getting the reconstructed image, the residual can be calculated. The particle position corresponded to the lowest residual is selected to be the result that is shown better than other methods.application/pdfen-USnoneAerospace engineeringAeronautics and astronauticsThesis