This joint with Alan Sussman's high-performance computing lab team effort aims at building a high performance system for efficient multi-perspective image analysis on (very) large image datasets, implemented as customized extensions of the object-oriented frameworks:
The Multi-perspective Vision Studio emphasizes separation from data acquisition facilities and portability across high performance computing platforms while providing a flexible framework for handling multi-view video sequences.
Initial performance results show that using an effective data distribution strategy based on the Hilbert space filling curve is critical for good workload balance and system scaling.
The subsequent research reveals that the system's performance is also greatly affected by the chosen cube projection algorithm and frame grouping strategy. The advanced implementation of Multi-perspective Vision Studio introduces additional query specifications including object surface mesh creation and mesh texture coloring.