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A stroke is an acute neurological event caused by the sudden disruption of cerebral blood flow, leading to rapid loss of neuronal function. Neurons depend on continuous oxygen and glucose supply, so even brief interruptions can cause irreversible injury within minutes. Strokes are classified into ischemic and hemorrhagic types.Ischemic StrokeIschemic strokes are most common and occur due to arterial occlusion, depriving brain tissue of oxygen and nutrients. This leads to energy failure, ionic...
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DPFrag: trainable stroke fragmentation based on dynamic programming.

R Sinan Tümen, T Metin Sezgin

    IEEE Computer Graphics and Applications
    |May 9, 2014
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces DPFrag, a new method for fragmenting freehand curves into geometric primitives. DPFrag offers an efficient and globally optimal solution for sketch recognition, outperforming existing techniques.

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    Area of Science:

    • Computer Graphics
    • Computational Geometry
    • Machine Learning

    Background:

    • Freehand curve fragmentation is crucial for applications like sketch recognition.
    • Existing methods often require manual tuning, are computationally expensive, or yield suboptimal results.
    • There is a need for efficient and accurate curve fragmentation techniques.

    Purpose of the Study:

    • To develop an efficient, globally optimal method for fragmenting freehand curves into geometric primitives.
    • To address the limitations of current fragmentation techniques in terms of manual tuning and computational cost.
    • To improve the accuracy and practicality of sketch recognition and similar computer graphics applications.

    Main Methods:

    • Introduced DPFrag, a novel fragmentation method.
    • Employed a dynamic-programming framework to combine primitive recognizers.
    • Learned segmentation parameters directly from data.

    Main Results:

    • DPFrag achieves efficient and globally optimal curve fragmentation.
    • The method eliminates the need for laborious parameter tuning.
    • Experimental results demonstrate superior performance compared to state-of-the-art methods on standard databases, even with limited labeled data.

    Conclusions:

    • DPFrag provides a fast, accurate, and data-driven solution for freehand curve fragmentation.
    • The method significantly advances sketch recognition and related computer graphics tasks.
    • DPFrag offers a practical alternative to existing, less efficient fragmentation approaches.