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Association areas are regions of the cerebral cortex that do not have a specific sensory or motor function. Instead, they integrate and interpret information from various sources to enable higher cognitive processes such as memory, learning, and decision-making. Some key association areas include the following:
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High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques
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FASHE: A FrActal Based Strategy for Head Pose Estimation.

Carmen Bisogni, Michele Nappi, Chiara Pero

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |February 22, 2021
    PubMed
    Summary

    FASHE, a novel approach using fractal-based methods, achieves state-of-the-art head pose estimation without deep learning. This method offers a robust alternative for accurately determining head rotations from single images.

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    Area of Science:

    • Computer Vision
    • Image Processing
    • Machine Learning

    Background:

    • Head Pose Estimation (HPE) is crucial for various applications, including best-frame selection.
    • Deep learning methods dominate HPE but require extensive training.
    • Alternative, non-deep learning approaches may offer competitive performance.

    Purpose of the Study:

    • To introduce FASHE, a novel head pose estimation method.
    • To demonstrate the efficacy of Partitioned Iterated Function Systems (PIFS) for HPE.
    • To provide an accurate and robust HPE solution without extensive training.

    Main Methods:

    • FASHE utilizes PIFS to represent facial auto-similarities.
    • A single frontal reference image is used to extract domain blocks.
    • Pose estimation is performed by matching fractal codes using Hamming distance.

    Main Results:

    • FASHE surpasses state-of-the-art results on Biwi and Ponting'04 datasets.
    • Performance approaches top methods on the challenging AFLW2000 database.
    • Successful in-the-wild operation demonstrated on the GOTCHA Video Dataset.

    Conclusions:

    • FASHE offers a powerful, non-deep learning alternative for head pose estimation.
    • The PIFS-based approach provides high accuracy and robustness.
    • FASHE shows promise for real-world, in-the-wild applications.