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Architecture-Driven Level Set Optimization: From Clustering to Subpixel Image Segmentation.

Souleymane Balla-Arabe, Xinbo Gao, Dominique Ginhac

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    |December 15, 2015
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel hybrid CPU-GPU Level Set Method (LSM) for accelerated image segmentation. The new model achieves higher accuracy and speed compared to existing methods, improving efficiency for computer vision tasks.

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    Area of Science:

    • Computer Vision
    • Image Processing
    • Computational Geometry

    Background:

    • Active Contour Models (ACMs) are valuable for computer vision tasks.
    • Level Set Methods (LSMs) offer subpixel accuracy and automatic topological change handling.
    • LSMs are computationally intensive, limiting their practical application.

    Purpose of the Study:

    • To develop an architecture-aware hybrid Central Processing Unit (CPU)-Graphics Processing Unit (GPU) LSM for image segmentation.
    • To improve the trade-off between speed, accuracy, efficiency, and effectiveness in LSMs.
    • To address the computational cost of LSMs through hardware acceleration.

    Main Methods:

    • Designed a hybrid CPU-GPU LSM architecture.
    • Utilized k-means algorithm for fast CPU-based initialization.
    • Implemented the evolution equation on GPU for parallel acceleration.
    • Incorporated local statistics into the level set evolution process.

    Main Results:

    • The hybrid LSM demonstrates superior speed and accuracy compared to state-of-the-art methods.
    • The model effectively detects boundaries missed by initial clustering algorithms.
    • Achieved robust, subpixel accurate segmentation with smooth, closed contours.
    • Reduced susceptibility to local minima.

    Conclusions:

    • The proposed hybrid CPU-GPU LSM offers a significant advancement in image segmentation.
    • It effectively balances computational efficiency with high accuracy.
    • The framework is suitable for automatic systems and enhances two-phase clustering algorithms.