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Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging
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Distributed Seams for Gigapixel Panoramas.

Sujin Philip, Brian Summa, Julien Tierny

    IEEE Transactions on Visualization and Computer Graphics
    |September 11, 2015
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    Summary

    We developed a fast, parallel technique for computing seams in gigapixel panoramas. This method significantly reduces processing time from hours to minutes, improving image blending quality.

    Area of Science:

    • Computer Vision
    • Image Processing
    • Parallel Computing

    Background:

    • Gigapixel panoramas are created from mosaics of smaller images, often resulting in lighting and exposure inconsistencies.
    • Seamless blending of these images requires minimizing discontinuities along boundaries, a process known as seam computation.
    • Existing multi-labeling Graph Cuts methods are computationally expensive and memory-intensive for large-scale panoramas.

    Purpose of the Study:

    • To present a novel parallel, out-of-core seam computing technique for gigapixel panoramas.
    • To develop a method that is fast, memory-efficient, and scalable across various parallel systems.
    • To improve the efficiency of seam computation without compromising boundary quality.

    Main Methods:

    • A parallel, out-of-core algorithm for seam computation.

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  • Configurable memory usage with a cache to reduce disk I/O and redundant computations.
  • Testing on symmetric multiprocessing, cluster, and distributed shared memory systems.

    • Main Results:

    • The technique significantly reduces seam computation time for gigapixel imagery from hours/days to minutes.
    • Achieves near-perfect scaling on symmetric multiprocessing systems and good scaling on clusters.
    • Maintains boundary energy comparable to slower Graph Cuts methods.
    • Demonstrates efficient performance on diverse parallel architectures.

    Conclusions:

    • The proposed technique offers a substantial speedup for seam computation in gigapixel panoramas.
    • It provides a practical and efficient solution for creating seamless large-scale digital images.
    • The method is scalable and adaptable to different parallel computing environments.