June 2026 Monthly Summary: Successfully delivered a major performance optimization for OpenCV's Earth Mover's Distance (EMD) solver within the imgproc module, resulting in substantial runtime reductions and improved CPU efficiency for image matching tasks. The work centered on refactoring the dual-variable calculation to a linear-time BFS tree traversal and replacing pointer-heavy linked lists with stack-allocated arrays to boost cache locality. The change was implemented in the opencv/opencv repository as part of a large PR (Merge PR #29194) with commit ffa38e1b74654a78c17f65a992adeb60953b1d53 and corresponding description detailing the O((N+M)^2) to O(N+M) optimization. The feature was validated with comprehensive benchmarks and existing tests: a series of dims tests (N, M = 64/3/100/500/1000/2000) demonstrated speedups up to ~1.60x for various scales, with performance consistently improving over the original implementation. All opencv_test_imgproc tests, including EMD, pass without regressions. This work delivers clear business value by accelerating core image processing workloads, enabling higher throughput in pipelines that rely on EMD computations, and reducing overall compute time. It also showcases deep technical competencies in algorithms, memory-layout optimization, and performance benchmarking, as well as strong contribution practices (PR readiness, documentation, and adherence to Apache 2.0 licensing). Key outcomes include improved runtime performance, enhanced cache locality, and maintainable, linear-time algorithm design, reinforcing OpenCV’s role in speed-critical vision workloads.