• a competitive study of the pseudoflow algorithm for the minimum s–t cut problem in vision applications

    جزئیات بیشتر مقاله
    • تاریخ ارائه: 1392/07/24
    • تاریخ انتشار در تی پی بین: 1392/07/24
    • تعداد بازدید: 916
    • تعداد پرسش و پاسخ ها: 0
    • شماره تماس دبیرخانه رویداد: -
     rapid advances in image acquisition and storage technology underline the need for real-time algorithms that are capable of solving large-scale image processing and computer-vision problems. the minimum st cut problem, which is a classical combinatorial optimization problem, is a prominent building block in many vision and imaging algorithms such as video segmentation, co-segmentation, stereo vision, multi-view reconstruction, and surface fitting to name a few. that is why finding a real-time algorithm which optimally solves this problem is of great importance. in this paper, we introduce to computer vision the hochbaum’s pseudoflow (hpf) algorithm, which optimally solves the minimum st cut problem. we compare the performance of hpf, in terms of execution times and memory utilization, with three leading published algorithms: (1) goldberg’s and tarjan’s push-relabel; (2) boykov’s and kolmogorov’s augmenting paths; and (3) goldberg’s partial augment-relabel. while the common practice in computer-vision is to use either bk or prf algorithms for solving the problem, our results demonstrate that, in general, hpf algorithm is more efficient and utilizes less memory than these three algorithms. this strongly suggests that hpf is a great option for many real-time computer-vision problems that require solving the minimum st cut problem.

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