Computing

Implementing Loopy Belief Propagation as a more general optimization approach for MRF, supporting non-grid graphs and non-binary cases, with image restoration experiment results.

Deep dive into what energy functions can be minimized via graph-cut, with the necessary and sufficient condition f(0,0)+f(1,1)≤f(0,1)+f(1,0) and application to image segmentation.

Introducing how to formulate image restoration as a Markov Random Field optimization problem and solve it using graph-cut algorithm to achieve global optimal results efficiently.

No single language fits all scientific computing needs. Following UNIX philosophy—combining C#, MATLAB, and Python through files and system calls—achieves both development and runtime efficiency.